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On the dependence of cooperative algorithms on underwater communication performance
On the dependence of cooperative algorithms on underwater communication performance A. Caiti ∗ V. Calabro’ ∗ A. Munafo’ ∗ ∗
Centro E. Piaggio, Inter-university Center on Integrated Systems for the Marine Environment, University of Pisa, Pisa, Italy (e-mail: [andrea.caiti, v.calabro, munafo]@ dsea.unipi.it).
Abstract: Any cooperative mission necessarily involves communication among multiple agents. When the cooperating agents/sensors are used in the underwater domain, communication issues become of paramount importance (e.g. the agent spatial locations and mutual separation has a direct influence on the capability to communicate). This contribution analyses the variation in performance of an adaptive cooperative algorithm for Autonomous Underwater Vehicles (AUVs) with respect to two different communication modalities. The first case is based on the use of an underwater acoustic network which is able to route the information towards the desired recipients; the second case uses a simpler scheme where each agent uses its onboard acoustic modem for broadcast transmission (i.e. all the agents that are able to directly listen the messages receive the information). The simulation testbed is based on the underwater acoustic network library AUVNetSim, adapted to simulate cooperative autonomous agents and broadcast transmission at physical layer. Keywords: Autonomous vehicles, communication networks, co-operative control 1. INTRODUCTION Several recent researches have shown how a set of autonomous mobile agents and sensors, able to self-adapt and self-configure, can be used in complex scenarios (Martinez et al. [2007]). The collaborative use of multiple sensors is in fact of great advantage thanks to the resulting flexibility and robustness in the accomplishment of tasks. For instance, exploration of partially known or unknown environments can effectively be performed by a team of cooperating autonomous vehicles with an optimized use of the available resources and consequent saving of time and money (Caiti et al. [2007]). As another example, the use of sensor networks for continuous monitoring of vital areas allows for disaster prevention and for a prompt reaction against unexpected situations (Soreide et al. [2001]). One scenario in which the use of multiple vehicles or sensor nodes presents critical aspects and peculiar characteristics is the underwater one. Many relevant infrastructures are placed very close to the sea or directly in the water, opening new scenarios for the use of Autonomous Underwater Vehicles (AUVs) and the development of Autonomous Ocean Sampling Networks (AOSN), where multiple nodes can cooperate as a group to achieve some common goals (Curtin et al. [1993]). In the underwater environment, communication is a challenge. The acoustic propagation, the main mean of underwater communication, is strongly dependent on the specific environmental conditions, and during the evolution of the mission, each vehicle can experience abrupt changes in the channel, with a consequent variation in commu-
nication performance. Moreover, acoustic communication is severely band-limited and range-limited. Sudden reduction of the channel capacity and bandwidth, or even a temporary loss of connectivity with the rest of the team, is a frequent condition for underwater communications, influencing the agents ability to continue the mission in cooperation (Akyildiz et al. [2005], Stojanovic [2007]). It is clear that in such a scenario and in order to set up a successful cooperative mission, the ability to predict the cooperation performance as a result of the communication among the agents becomes of paramount importance. The aim of this contribution is to analyse, through simulations, the variation in performance of an adaptive cooperative algorithm for Autonomous Underwater Vehicles (AUVs) with respect to two different communication networking modalities. The cooperation algorithm is designed for protection of an at-sea asset, and it is based on emerging behaviour of the AUV group. The network schemes analyzed are based on two communication modalities recently tested experimentally. The first option consider a layered ISO/OSI configuration, which includes Madium Access Control in the form of CSMA/CD, an addressing system to support data packet switching and forwarding, retransmission operations, message routing. In the second case a time division scheme, with no multi-hop, is considered. The first of the two configurations is the one employed and tested experimentally in the FP7 project U AN , and the simulative parameters have been set in both cases similar to those experimentally encountered in the UAN sea trial held in May 2011 in the Trondheim
area; the second modality has been employed in part of the experimentations of the FP7 project Co3 AU V . The simulation testbed is based on the underwater acoustic network library AUVNetSim (Montana [2011]), adapted to simulate cooperative autonomous agents and broadcast transmission at physical layer. The paper is organized as follow: the next section briefly reports the cooperative algorithm used for communication comparison in this work. Section 3 describes first the simulation scenario used, and then it goes into the details of the communication issues, pointing out the difference in performance achieved by the algorithm depending on the communication scheme used. Comments and remarks are given to explain the results and to clarify the advantages and disadvantages of each method. Finally, conclusions are given in Section 4.
2. COOPERATIVE ALGORITHM FOR AUVS The AUV cooperation algorithm is based on the one proposed in Caiti et al. [2012], and it is here briefly reported. Let us suppose we have the availability of n AUVs, each one equipped with an acoustic modem for communication up to a maximum range RC and with a detection sonar characterized by a maximum range RD . The main mission objective is that of covering with the mobile node detection sonars the maximum area around the asset, while each vehicle has to move to keep at least one other vehicle of the team within its communication range. This general goal is divided, according to the behavioral approach paradigm into simpler subtasks (behaviors or rules) solvable in parallel. A composition rule is also defined to transpose the commands generated by each subtask into one single motion command for each vehicle. The above mention objective is hence splitted into two subtasks or rules: (1) Move toward the High Value Asset (HVA) to be defended. (2) Move away from your closest neighbor but without exiting from its communication range. The first task allows the vehicle to move closer to the asset to ensure the asset protection. The second task represents the coordination level. It allows each agent to adapt its movement to keep into account actions of the other members of the team. Specifically, it lets each vehicle cover the maximum area around the asset, with minimum overlaps of the on board sonar detection ranges, while guaranteeing the communication links with at least one other teammate. The composition of the two subtasks is achieved through a priority-based mechanism which assigns to each of the subtask a dynamic priority on the basis of the vehicle status with respect to the fulfillment of each one of the subtasks. In particular, each vehicle assigns to each task a priority computed on the basis of an artificial potential function. This function defines, at any given stage of the mission, the interest of the vehicle in fulfilling the specific task while a comparison among the functions of interest determines the priority of the tasks to be executed at any time frame by each vehicle:
• the Asset attraction function, hA (xasset , xi (k)) is a function of the agent’s distance from the asset xasset . It defines the interest of the agent (priority of the subtask) in moving towards the asset. Figure 1 shows a qualitative example of such a function. • the Coordination function, hC (xj (k), xi (k)) defines the priority of the coordination task (see Figure 2). It is computed online and modified by the vehicle during the evolution of the mission on the basis of the detection and communication performance of its onboard devices: the detection sonar range RD defines the minimum distance between two vehicles; the maximum communication range RC achieved at a given spatial and temporal location defines the maximum separation between two vehicles; in addition, we also define the parameter RM as the maximum distance at which each agent wants to keep its closest neighbor. The parameter RD can be thought as the maximum detection range at which the detection performance is above a desired threshold T H, RM as the range above which the detection performance is below a minimum level T H. Finally, the agent control input u(t), at each time frame, is computed as the vector sum of the gradient of each potential function. We do not go into the theoretical details of the algorithm as it would go beyond the scope of this work. It is important, however, to note some characteristics of the approach. First, as described with details in Caiti et al. [2012], the algorithm is able to reach a final configuration in which the agents are placed around the asset, with minimum overlap of their detection sonars. A stability analysis of the algorithm can also be pursued, for example, with an adaptation of the approach proposed in Bachmayer and Leonard [2002]. Furthermore, note that the algorithm makes each vehicle able to move back to the asset it needs to protect even when it loses the communication with the other team members, since each agent can always execute task 1 (move towards the asset). The performance of the cooperation is degraded but the subset of the agents that can still communicate, or in the worst case, each vehicle independently, can continue the mission. In addition, at each step of the mission each agent does not necessarily need to receive information from all the other nodes: only information from its closest neighbour is needed for the coordination task. If information is available from more than one vehicle, an additional step is needed to determine the closest neighbour. In any case, the amount of information the vehicles need to exchange is limited, as they only require communicating their own position and maximum detection sonar range. Finally, it is important to point out that when a vehicle loses the communication with the remainder of the team or it is not able to receive a new data, it is still able to execute task 2 but using only not up-to-date information. In this case, the agent calculates the control input on the basis of the last known position of its neighbours. 3. SIMULATIONS AND PERFORMANCE ANALYSIS The variation in performance of the cooperative approach is now described using two different communication meth-
communication burden or it may be achieved in parallel with the vehicles normal communication phases, as for example in the case of vehicles equipped with USBL heads. This work considers this possibility with no additional communication overhead requested for localisation.
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Fig. 1. Function of interest for task 1: move towards the asset to be protected. The higher the distance from the asset the higher the interest in fulfilling the task. Cooperation function 6
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The High Value Asset (HVA) to be protected is placed at (500, 500)m. One vehicle is placed further away from the others to simulate an initial tougher communication condition. The simulation maximum duration is set to 3000s.
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Note also that the vehicles motion will be approximated with a reduced 3-Degree of Freedom (DoF) kinematic model constrained to move on a plane at a constant depth of 20m. However, this is not such a strong simplification in the specific communication scenario. All the vehicles have omni-directional modems and no bottom or surface interactions are considered; the oceanic environment is uniform and characterized by a constant sound speed profile equal to 1482m/s, at all depth. Under this conditions the communication depends mainly on the relative distance between the agents.
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Fig. 2. Interest function for task 2: move away from your closest neighbour while maintaining the communication connectivity. The function represents the cohesiveness among the vehicles, as a function of range from the nearest neighbour (move away when the vehicles are close, move closer for values approaching the maximum communication range) ods. The first case is based on the use of an Underwater Acoustic Network (UAN) as communication infrastructure. In this case, each agent decides the recipient of the message and the network guarantees the delivery of the data to the right peer. The second scenario uses a broadcast communication scheme, which does not include any form of networking: when an agent wants to communicate, it starts a broadcast transmission using its onboard acoustic modem. The message can hence be listened by all the receivers within its communication range. 3.1 Scenario description The simulative scenario is represented by a marine area of 800x800m width; four mobile agents are simulated, with a maximum speed of 1m/s. Each vehicle is equipped with an acoustic modem for communication, whose technical specifications are based on those of the one employed in FP7 UAN project: Bit rate = 500bps, operative central frequency f = 25.6kHz, maximum source level SL = 190dB re 1 µP a @ 1m. The maximum packet size is 1200bits and it permits the inclusion, according to the described cooperative algorithm, of the vehicle location and of its maximum detection sonar range (RD = 50m). Note that, all the robots are assumed to know perfectly their position at all time. Whereas most AUV systems rely on IMU dead-reckoning, potentially with some GPS fix from time to time, a full localisation system would be possible to setup, in particular in the context of a network infrastructure. This might require an additional
3.2 Case 1: using an underwater acoustic network The first communication scheme analysed is based on the UAN project network architecture. Accordingly to the UAN concept, the simulated network architecture is composed by a layered structure including: the CSMA/CD access, the use of a static FLOOD-based routing algorithm (Rudstad [2009]) and multi-hop. The highest layer of the network (application level) is represented by the cooperative algorithm. Using a networked communication scheme, any form of explicity transmission coordination among the agents would not be necessary, the network itself being responsible for implementing all the required mechanisms such as medium availability, collision detections, packet forwarding, etc. to guarantee the correct transmission and to optimize the communication. However, in the case of an acoustic network that necessarily involves not negligible delays, it becomes important to introduce some level of communication awareness also at application layer in order not to overload the acoustic channel and adapting the transmissions to the limitations of the medium. In this work, this is achieved implementing periodic communications: each agent can communicate only once every Ts = 120s whereas the initial instant of the first communication period is selected randomly within the first 30s of the mission. Removing this coordination among the agents, would not impede the success of the networked communication, but it would result in much bigger delays with more collisions, retransmissions and packet drops caused by simultaneous access to the channel and modem buffer overflows. The results of the simulation are shown in Table 1 in terms of communication performance and in Figure 3 in terms of the vehicles paths followed during one of the run. Note in fact that in different simulations, slightly different results can be obtained depending on how the agents transmissions collide and on how the network reacts (e.g. CSMA/CD random back off mechanisms). From Figure 3, it is clearly visible the impact of the periodic transmission,
Fig. 3. Underwater acoustic network with CSMA/CD access. Vehicles trajectories during the area coverage mission. Each vehicle is able to transmit with a communication period of 120s. The zig-zag movement is due to the agents considering their teammates as static when no updated information is available. The final formation places the given asset at center of the sonar detection area. Darker circles represent the sonar detection range RD ; brighter circles represent the maximum distance allowed RM .
Fig. 4. Vehicles trajectories during the area coverage mission using a time division broadcast transmission. Trajectories are smoother than the networked case thanks to shorter transmission delays. Darker circles represent the sonar detection range RD ; brighter circles represent the maximum distance allowed RM .
which causes the round-shaped zig-zag movement. Each agent, in fact, considers its closest neighbor as fixed at its last known location when it does not have an updated information. In this case, task 2 forces the vehicle to move around the neighbor to minimize the sonar overlap. Note also that since each agent can always apply rule 1 they keep moving towards the asset also when they lose the communication with the other vehicles and are able to reach a final configuration placing the asset to protect at the center of the sonar detection area with a minimum of sonar overlap.
The time division broadcast communication performance is depicted in Table 1. The average transmission delay is 2.5s, which corresponds to the time needed by the modem to transmit 1200bits and to reach the destination given the sound speed profile. The total energy consumption is 73.37J, and a total number of 300 packets were transmitted in 3000s of simulation. From the cooperative algorithm point of view, the resulting trajectories are shown in Fig. 4. The agents reached a final configuration with the asset placed at the center of the sonar detection area, however, the vehicles paths are different (note that all configurations with the asset placed at the center of mass of the sonar area are equally optimal). The broadcast communication permits to have quicker position updates thanks to the fact that whenever a receiving vehicle is inside the communication range of the transmitter, it has always a direct link (no hops). This results in smoother trajectories towards the final configuration.
From a more communication related viewpoint, a total number of 298 packets were transmitted through the network, requiring 243J of energy, and with an average end to end delay of 47.5s. Finally, in the whole time 12 collisions occurred. 3.3 Case 2: using a broadcast communication scheme In the second scenario, a different communication scheme, using a time division multiplex is considered. The basic implementation is similar to that implemented during the Co3 AU V project sea trial. According to this scheme, when a vehicle wants to transmit a message it directly accesses its acoustic modem for a broadcast transmission, which can be listened by all the receivers within its communication range. No collision detection/avoidance or retransmission protocols are implemented in this case. If a packet collides or if it is lost, the transmitting node does not resend the message and it is completely unaware of the communication result. For this reason and to limit simultaneous transmissions, which would result in too frequent collisions and communication failures, in this work a round robin channel sharing technique has been implemented: each agent is assigned to a periodic time slot where it has to concentrate all its communication burden.
In the four vehicles scenario simulated here, each agent has 30s for communication, and a communication period of Ts = 120s.
3.4 Performance comparison: remarks and observations The simulative results lead to some major considerations: first, the less elaborated time division scheme may endup to be more efficient, despite the absence of multi-hop capabilities, as long as the cooperation scheme is explicitly designed taking into account the loss of communication among some of the agents. Note in fact that the each vehicle can always apply rule A (move towards asset) even when it is not able to communicate with the other team members (e.g. loss of connection due to unexpected changes in the environment; movement within an area with poor acoustic characteristics). Since all the vehicles are expected to do the same, this allows the agents to move towards an area where it is more likely to recover the acoustic connection. Furthermore, thanks to the continuous application of rule A, the execution time
necessary to reach the final configuration is comparable in both cases. The networked scheme requires, with respect to the time division multiplex case and with such a limited number of nodes, bigger transmission delays and more energy. The reason is due to the fact that the use of a network implies the use of additional complexity to guarantee that each message is correctly received by the right recipient. Retransmissions and multi-hops are usual operative conditions in the networked case and this has a great impact on the overall communication performance. In this particular configuration, the routing scheme actually penalizes the network. It is based on a static protocol, which hence does not update the routing table when the network geometry changes. As a result, the time division broadcast scheme appears to be more efficient in a scenario composed only by few moving nodes, which also remains quite close to each other (note that the objective of the cooperation is to bring the vehicles as close as possible to each other without overlapping the detection sonar ranges). Of course, this better result for the time division case, has the drawback of requiring explicit coordination at application level, which results in a lack of modularity: the application level (i. e. the cooperative algorithm) must be tuned explicitly on the communication mechanisms. Moreover, it lacks of scalability with the number of nodes, and the inclusion or removal of an agent implies an explicit reprogramming of the whole team (e.g. to increase or decrease the communication period). Parameter Average delay (s) Energy (J) n. Packets n. Collisions
UAN 47.5 243 298 12
Broadcast 2.5 73.37 300 -
Table 1. Performance comparison
4. CONCLUSION This paper describes through simulations the impact of different communication schemes on the performance of an adaptive cooperative algorithm for AUV teams. The simulative environment is represented by a modified version of the AUVNetSim acoustic network simulator, adapted for broadcast transmission at physical layer and to handle cooperation of multiple mobile nodes. Two simulated scenarios are analysed: one in which an acoustic network is used as communication infrastructure, and a much simpler one based on broadcast transmission. Results show how the communication method impacts on the final performance achieved. On going work is focusing on the analysis of more complex scenarios with more nodes (fixed and mobile) and different types of network layers. ACKNOWLEDGEMENTS This work was supported in part by European Union, 7th Framework Programme, Project Co3 AU V - Cognitive Cooperative Control for Autonomous Underwater Vehicles, Grant n. IST-231378, and Project U AN - Underwater Acoustic Network, Grant n. 225669.
REFERENCES I. F. Akyildiz, D. Pompili, and T. Melodia. Underwater acoustic sensor networks: Research challenges. Ad Hoc Networks (Elsevier), 3:257–279, 2005. R. Bachmayer and N.E. Leonard. Vehicle networks for gradient descent in a sampled environment. In Decision and Control, 2002, Proceedings of the 41st IEEE Conference on, volume 1, pages 112 – 117, dec. 2002. doi: 10.1109/CDC.2002.1184477. A. Caiti, A. Munafo’, and R. Viviani. Adaptive online planning of environmental sampling missions with a team of cooperating autonomous under water vehicles. International Journal of Control, 7(80):1151–1168, 2007. A. Caiti, V. Calabro’, G. Dini, A. Lo Duca, and A. Munafo’. Secure cooperation of autonomous mobile sensors using an underwater acoustic network. Sensors, 12(2): 1967–1989, 2012. T. Curtin, J.G. Bellingham, J. Catipovic, and D. Webb. Autonomous ocean sampling network. Oceanography, 6 (3):86–94, 1993. S. Martinez, J. Cortez, and F. Bullo. Motion coordination with distributed information. IEEE Control System Magazine, pages 75–88, August 2007. J. M. J. Montana. Auvnetsim: A simulator for underwater acoustic networks. Technical report, Massachusetts Institute of Technology, 2011. H. Rudstad. A lightweight protocol suite for underwater communication. In International IEEE Conference on Advanced Information Networking and Applications, 2009. N.N. Soreide, C.E. Woody, and S.M. Holt. Overview of ocean based buoys and drifters: Present application and future needs. In Proc. 16th OCEANS, 2001. MTS/IEEE Conference and Exhibition, volume 4, pages 2470–2472, 2001. M. Stojanovic. On the relationship between capacity and distance in an underwater acoustic communication channel. ACM SIGMOBILE Mobile Computing and Communications Review (MC2R), 11(4):34–43, October 2007.
Centro E. Piaggio, Inter-university Center on Integrated Systems for the Marine Environment, University of Pisa, Pisa, Italy (e-mail: [andrea.caiti, v.calabro, munafo]@ dsea.unipi.it).
Abstract: Any cooperative mission necessarily involves communication among multiple agents. When the cooperating agents/sensors are used in the underwater domain, communication issues become of paramount importance (e.g. the agent spatial locations and mutual separation has a direct influence on the capability to communicate). This contribution analyses the variation in performance of an adaptive cooperative algorithm for Autonomous Underwater Vehicles (AUVs) with respect to two different communication modalities. The first case is based on the use of an underwater acoustic network which is able to route the information towards the desired recipients; the second case uses a simpler scheme where each agent uses its onboard acoustic modem for broadcast transmission (i.e. all the agents that are able to directly listen the messages receive the information). The simulation testbed is based on the underwater acoustic network library AUVNetSim, adapted to simulate cooperative autonomous agents and broadcast transmission at physical layer. Keywords: Autonomous vehicles, communication networks, co-operative control 1. INTRODUCTION Several recent researches have shown how a set of autonomous mobile agents and sensors, able to self-adapt and self-configure, can be used in complex scenarios (Martinez et al. [2007]). The collaborative use of multiple sensors is in fact of great advantage thanks to the resulting flexibility and robustness in the accomplishment of tasks. For instance, exploration of partially known or unknown environments can effectively be performed by a team of cooperating autonomous vehicles with an optimized use of the available resources and consequent saving of time and money (Caiti et al. [2007]). As another example, the use of sensor networks for continuous monitoring of vital areas allows for disaster prevention and for a prompt reaction against unexpected situations (Soreide et al. [2001]). One scenario in which the use of multiple vehicles or sensor nodes presents critical aspects and peculiar characteristics is the underwater one. Many relevant infrastructures are placed very close to the sea or directly in the water, opening new scenarios for the use of Autonomous Underwater Vehicles (AUVs) and the development of Autonomous Ocean Sampling Networks (AOSN), where multiple nodes can cooperate as a group to achieve some common goals (Curtin et al. [1993]). In the underwater environment, communication is a challenge. The acoustic propagation, the main mean of underwater communication, is strongly dependent on the specific environmental conditions, and during the evolution of the mission, each vehicle can experience abrupt changes in the channel, with a consequent variation in commu-
nication performance. Moreover, acoustic communication is severely band-limited and range-limited. Sudden reduction of the channel capacity and bandwidth, or even a temporary loss of connectivity with the rest of the team, is a frequent condition for underwater communications, influencing the agents ability to continue the mission in cooperation (Akyildiz et al. [2005], Stojanovic [2007]). It is clear that in such a scenario and in order to set up a successful cooperative mission, the ability to predict the cooperation performance as a result of the communication among the agents becomes of paramount importance. The aim of this contribution is to analyse, through simulations, the variation in performance of an adaptive cooperative algorithm for Autonomous Underwater Vehicles (AUVs) with respect to two different communication networking modalities. The cooperation algorithm is designed for protection of an at-sea asset, and it is based on emerging behaviour of the AUV group. The network schemes analyzed are based on two communication modalities recently tested experimentally. The first option consider a layered ISO/OSI configuration, which includes Madium Access Control in the form of CSMA/CD, an addressing system to support data packet switching and forwarding, retransmission operations, message routing. In the second case a time division scheme, with no multi-hop, is considered. The first of the two configurations is the one employed and tested experimentally in the FP7 project U AN , and the simulative parameters have been set in both cases similar to those experimentally encountered in the UAN sea trial held in May 2011 in the Trondheim
area; the second modality has been employed in part of the experimentations of the FP7 project Co3 AU V . The simulation testbed is based on the underwater acoustic network library AUVNetSim (Montana [2011]), adapted to simulate cooperative autonomous agents and broadcast transmission at physical layer. The paper is organized as follow: the next section briefly reports the cooperative algorithm used for communication comparison in this work. Section 3 describes first the simulation scenario used, and then it goes into the details of the communication issues, pointing out the difference in performance achieved by the algorithm depending on the communication scheme used. Comments and remarks are given to explain the results and to clarify the advantages and disadvantages of each method. Finally, conclusions are given in Section 4.
2. COOPERATIVE ALGORITHM FOR AUVS The AUV cooperation algorithm is based on the one proposed in Caiti et al. [2012], and it is here briefly reported. Let us suppose we have the availability of n AUVs, each one equipped with an acoustic modem for communication up to a maximum range RC and with a detection sonar characterized by a maximum range RD . The main mission objective is that of covering with the mobile node detection sonars the maximum area around the asset, while each vehicle has to move to keep at least one other vehicle of the team within its communication range. This general goal is divided, according to the behavioral approach paradigm into simpler subtasks (behaviors or rules) solvable in parallel. A composition rule is also defined to transpose the commands generated by each subtask into one single motion command for each vehicle. The above mention objective is hence splitted into two subtasks or rules: (1) Move toward the High Value Asset (HVA) to be defended. (2) Move away from your closest neighbor but without exiting from its communication range. The first task allows the vehicle to move closer to the asset to ensure the asset protection. The second task represents the coordination level. It allows each agent to adapt its movement to keep into account actions of the other members of the team. Specifically, it lets each vehicle cover the maximum area around the asset, with minimum overlaps of the on board sonar detection ranges, while guaranteeing the communication links with at least one other teammate. The composition of the two subtasks is achieved through a priority-based mechanism which assigns to each of the subtask a dynamic priority on the basis of the vehicle status with respect to the fulfillment of each one of the subtasks. In particular, each vehicle assigns to each task a priority computed on the basis of an artificial potential function. This function defines, at any given stage of the mission, the interest of the vehicle in fulfilling the specific task while a comparison among the functions of interest determines the priority of the tasks to be executed at any time frame by each vehicle:
• the Asset attraction function, hA (xasset , xi (k)) is a function of the agent’s distance from the asset xasset . It defines the interest of the agent (priority of the subtask) in moving towards the asset. Figure 1 shows a qualitative example of such a function. • the Coordination function, hC (xj (k), xi (k)) defines the priority of the coordination task (see Figure 2). It is computed online and modified by the vehicle during the evolution of the mission on the basis of the detection and communication performance of its onboard devices: the detection sonar range RD defines the minimum distance between two vehicles; the maximum communication range RC achieved at a given spatial and temporal location defines the maximum separation between two vehicles; in addition, we also define the parameter RM as the maximum distance at which each agent wants to keep its closest neighbor. The parameter RD can be thought as the maximum detection range at which the detection performance is above a desired threshold T H, RM as the range above which the detection performance is below a minimum level T H. Finally, the agent control input u(t), at each time frame, is computed as the vector sum of the gradient of each potential function. We do not go into the theoretical details of the algorithm as it would go beyond the scope of this work. It is important, however, to note some characteristics of the approach. First, as described with details in Caiti et al. [2012], the algorithm is able to reach a final configuration in which the agents are placed around the asset, with minimum overlap of their detection sonars. A stability analysis of the algorithm can also be pursued, for example, with an adaptation of the approach proposed in Bachmayer and Leonard [2002]. Furthermore, note that the algorithm makes each vehicle able to move back to the asset it needs to protect even when it loses the communication with the other team members, since each agent can always execute task 1 (move towards the asset). The performance of the cooperation is degraded but the subset of the agents that can still communicate, or in the worst case, each vehicle independently, can continue the mission. In addition, at each step of the mission each agent does not necessarily need to receive information from all the other nodes: only information from its closest neighbour is needed for the coordination task. If information is available from more than one vehicle, an additional step is needed to determine the closest neighbour. In any case, the amount of information the vehicles need to exchange is limited, as they only require communicating their own position and maximum detection sonar range. Finally, it is important to point out that when a vehicle loses the communication with the remainder of the team or it is not able to receive a new data, it is still able to execute task 2 but using only not up-to-date information. In this case, the agent calculates the control input on the basis of the last known position of its neighbours. 3. SIMULATIONS AND PERFORMANCE ANALYSIS The variation in performance of the cooperative approach is now described using two different communication meth-
communication burden or it may be achieved in parallel with the vehicles normal communication phases, as for example in the case of vehicles equipped with USBL heads. This work considers this possibility with no additional communication overhead requested for localisation.
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Fig. 1. Function of interest for task 1: move towards the asset to be protected. The higher the distance from the asset the higher the interest in fulfilling the task. Cooperation function 6
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The High Value Asset (HVA) to be protected is placed at (500, 500)m. One vehicle is placed further away from the others to simulate an initial tougher communication condition. The simulation maximum duration is set to 3000s.
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Note also that the vehicles motion will be approximated with a reduced 3-Degree of Freedom (DoF) kinematic model constrained to move on a plane at a constant depth of 20m. However, this is not such a strong simplification in the specific communication scenario. All the vehicles have omni-directional modems and no bottom or surface interactions are considered; the oceanic environment is uniform and characterized by a constant sound speed profile equal to 1482m/s, at all depth. Under this conditions the communication depends mainly on the relative distance between the agents.
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Fig. 2. Interest function for task 2: move away from your closest neighbour while maintaining the communication connectivity. The function represents the cohesiveness among the vehicles, as a function of range from the nearest neighbour (move away when the vehicles are close, move closer for values approaching the maximum communication range) ods. The first case is based on the use of an Underwater Acoustic Network (UAN) as communication infrastructure. In this case, each agent decides the recipient of the message and the network guarantees the delivery of the data to the right peer. The second scenario uses a broadcast communication scheme, which does not include any form of networking: when an agent wants to communicate, it starts a broadcast transmission using its onboard acoustic modem. The message can hence be listened by all the receivers within its communication range. 3.1 Scenario description The simulative scenario is represented by a marine area of 800x800m width; four mobile agents are simulated, with a maximum speed of 1m/s. Each vehicle is equipped with an acoustic modem for communication, whose technical specifications are based on those of the one employed in FP7 UAN project: Bit rate = 500bps, operative central frequency f = 25.6kHz, maximum source level SL = 190dB re 1 µP a @ 1m. The maximum packet size is 1200bits and it permits the inclusion, according to the described cooperative algorithm, of the vehicle location and of its maximum detection sonar range (RD = 50m). Note that, all the robots are assumed to know perfectly their position at all time. Whereas most AUV systems rely on IMU dead-reckoning, potentially with some GPS fix from time to time, a full localisation system would be possible to setup, in particular in the context of a network infrastructure. This might require an additional
3.2 Case 1: using an underwater acoustic network The first communication scheme analysed is based on the UAN project network architecture. Accordingly to the UAN concept, the simulated network architecture is composed by a layered structure including: the CSMA/CD access, the use of a static FLOOD-based routing algorithm (Rudstad [2009]) and multi-hop. The highest layer of the network (application level) is represented by the cooperative algorithm. Using a networked communication scheme, any form of explicity transmission coordination among the agents would not be necessary, the network itself being responsible for implementing all the required mechanisms such as medium availability, collision detections, packet forwarding, etc. to guarantee the correct transmission and to optimize the communication. However, in the case of an acoustic network that necessarily involves not negligible delays, it becomes important to introduce some level of communication awareness also at application layer in order not to overload the acoustic channel and adapting the transmissions to the limitations of the medium. In this work, this is achieved implementing periodic communications: each agent can communicate only once every Ts = 120s whereas the initial instant of the first communication period is selected randomly within the first 30s of the mission. Removing this coordination among the agents, would not impede the success of the networked communication, but it would result in much bigger delays with more collisions, retransmissions and packet drops caused by simultaneous access to the channel and modem buffer overflows. The results of the simulation are shown in Table 1 in terms of communication performance and in Figure 3 in terms of the vehicles paths followed during one of the run. Note in fact that in different simulations, slightly different results can be obtained depending on how the agents transmissions collide and on how the network reacts (e.g. CSMA/CD random back off mechanisms). From Figure 3, it is clearly visible the impact of the periodic transmission,
Fig. 3. Underwater acoustic network with CSMA/CD access. Vehicles trajectories during the area coverage mission. Each vehicle is able to transmit with a communication period of 120s. The zig-zag movement is due to the agents considering their teammates as static when no updated information is available. The final formation places the given asset at center of the sonar detection area. Darker circles represent the sonar detection range RD ; brighter circles represent the maximum distance allowed RM .
Fig. 4. Vehicles trajectories during the area coverage mission using a time division broadcast transmission. Trajectories are smoother than the networked case thanks to shorter transmission delays. Darker circles represent the sonar detection range RD ; brighter circles represent the maximum distance allowed RM .
which causes the round-shaped zig-zag movement. Each agent, in fact, considers its closest neighbor as fixed at its last known location when it does not have an updated information. In this case, task 2 forces the vehicle to move around the neighbor to minimize the sonar overlap. Note also that since each agent can always apply rule 1 they keep moving towards the asset also when they lose the communication with the other vehicles and are able to reach a final configuration placing the asset to protect at the center of the sonar detection area with a minimum of sonar overlap.
The time division broadcast communication performance is depicted in Table 1. The average transmission delay is 2.5s, which corresponds to the time needed by the modem to transmit 1200bits and to reach the destination given the sound speed profile. The total energy consumption is 73.37J, and a total number of 300 packets were transmitted in 3000s of simulation. From the cooperative algorithm point of view, the resulting trajectories are shown in Fig. 4. The agents reached a final configuration with the asset placed at the center of the sonar detection area, however, the vehicles paths are different (note that all configurations with the asset placed at the center of mass of the sonar area are equally optimal). The broadcast communication permits to have quicker position updates thanks to the fact that whenever a receiving vehicle is inside the communication range of the transmitter, it has always a direct link (no hops). This results in smoother trajectories towards the final configuration.
From a more communication related viewpoint, a total number of 298 packets were transmitted through the network, requiring 243J of energy, and with an average end to end delay of 47.5s. Finally, in the whole time 12 collisions occurred. 3.3 Case 2: using a broadcast communication scheme In the second scenario, a different communication scheme, using a time division multiplex is considered. The basic implementation is similar to that implemented during the Co3 AU V project sea trial. According to this scheme, when a vehicle wants to transmit a message it directly accesses its acoustic modem for a broadcast transmission, which can be listened by all the receivers within its communication range. No collision detection/avoidance or retransmission protocols are implemented in this case. If a packet collides or if it is lost, the transmitting node does not resend the message and it is completely unaware of the communication result. For this reason and to limit simultaneous transmissions, which would result in too frequent collisions and communication failures, in this work a round robin channel sharing technique has been implemented: each agent is assigned to a periodic time slot where it has to concentrate all its communication burden.
In the four vehicles scenario simulated here, each agent has 30s for communication, and a communication period of Ts = 120s.
3.4 Performance comparison: remarks and observations The simulative results lead to some major considerations: first, the less elaborated time division scheme may endup to be more efficient, despite the absence of multi-hop capabilities, as long as the cooperation scheme is explicitly designed taking into account the loss of communication among some of the agents. Note in fact that the each vehicle can always apply rule A (move towards asset) even when it is not able to communicate with the other team members (e.g. loss of connection due to unexpected changes in the environment; movement within an area with poor acoustic characteristics). Since all the vehicles are expected to do the same, this allows the agents to move towards an area where it is more likely to recover the acoustic connection. Furthermore, thanks to the continuous application of rule A, the execution time
necessary to reach the final configuration is comparable in both cases. The networked scheme requires, with respect to the time division multiplex case and with such a limited number of nodes, bigger transmission delays and more energy. The reason is due to the fact that the use of a network implies the use of additional complexity to guarantee that each message is correctly received by the right recipient. Retransmissions and multi-hops are usual operative conditions in the networked case and this has a great impact on the overall communication performance. In this particular configuration, the routing scheme actually penalizes the network. It is based on a static protocol, which hence does not update the routing table when the network geometry changes. As a result, the time division broadcast scheme appears to be more efficient in a scenario composed only by few moving nodes, which also remains quite close to each other (note that the objective of the cooperation is to bring the vehicles as close as possible to each other without overlapping the detection sonar ranges). Of course, this better result for the time division case, has the drawback of requiring explicit coordination at application level, which results in a lack of modularity: the application level (i. e. the cooperative algorithm) must be tuned explicitly on the communication mechanisms. Moreover, it lacks of scalability with the number of nodes, and the inclusion or removal of an agent implies an explicit reprogramming of the whole team (e.g. to increase or decrease the communication period). Parameter Average delay (s) Energy (J) n. Packets n. Collisions
UAN 47.5 243 298 12
Broadcast 2.5 73.37 300 -
Table 1. Performance comparison
4. CONCLUSION This paper describes through simulations the impact of different communication schemes on the performance of an adaptive cooperative algorithm for AUV teams. The simulative environment is represented by a modified version of the AUVNetSim acoustic network simulator, adapted for broadcast transmission at physical layer and to handle cooperation of multiple mobile nodes. Two simulated scenarios are analysed: one in which an acoustic network is used as communication infrastructure, and a much simpler one based on broadcast transmission. Results show how the communication method impacts on the final performance achieved. On going work is focusing on the analysis of more complex scenarios with more nodes (fixed and mobile) and different types of network layers. ACKNOWLEDGEMENTS This work was supported in part by European Union, 7th Framework Programme, Project Co3 AU V - Cognitive Cooperative Control for Autonomous Underwater Vehicles, Grant n. IST-231378, and Project U AN - Underwater Acoustic Network, Grant n. 225669.
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