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044 Towards using neural network for automatic guidance of mining robots
160
Abstracts
integrate the conflicting demands of navigation and obstacle avoidance. This paper introduces a fuzzy logic based controller which navigates a conventionally steered vehicle and which is combined with an obstacle avoidance scheme before the defuzzification stage of the controller. Simulation results are presented, demonstrating that this combination produces a sensible fusion of these two functions.
044 Towards using Neural Network for Automatic Guidance of Mining Robots J.B. Edwards and S. Yaacob, pp 263-268 In this paper a neural-network controller is investigated as a possibility for implementing this type of controller in certain operations pertaining to the automatic guidance of mining robots. Preliminary results obtained from the neural-network controller show an improvement over the tractable analytic optimal controller (derived for a necessarily simplified system).
045 Automatic Road Following using Fuzzy Control* B. Bltchl, S. Tsinas, pp 269-274 Lateral control of a vision-guided autonomous mobile vehicle is shown as an application of a fuzzy controller. A description of the experimental vehicle and the image processing used in the recognition of the pathway are given. The design of the fuzzy controller and the results of the experiments are described. As a proof on an actual implementation, a real-time application, namely the behaviour module "follow corridor", is demonstrated on an indoor vehicle.
046 Fuzzy Supervisory Path Tracking of Mobile Robots* A. Ollero, A. Garcia-Cerezo, J.L. Martinez, pp 275-280 In this paper a new method for automatic path tracking of mobile robots and autonomous vehicles is proposed. The paper also includes the application to RAM-l, a new mobile robot testbed designed and built for research and applications in indoor and outdoor industrial environments. The method generates the appropriate vehicle steering angle command by combining fuzzy logic with the geometric pure-pursuit technique and the generalized predictive control method. In the proposed fuzzy pathtracking strategy the control parameters of these methods are inferred automatically in real time from the characteristics of the current segment of the path to follow, the vehicle's velocity and its current relative position and orientation.
047 Intelligent Navigation Control of an Autonomous Mobile Robot Young Hoon Joo, Hee Soo Hwang, Kwang Bang Woo, Kun Woong Bae, Sung KwunKlm, pp 281-286 This paper proposes a method for navigation and obstacle avoidance of an autonomous mobile robot, based on a fuzzy inference system which enables it to deal with imprecise and uncertain information, and also a neural network which enables it to learn input and output pattern data obtained from ultrasonic sensors. A fuzzy model for wall-following navigation utilizing input-output data is consWucted for autonomous navigation. An approach using the neural network is developed for obstacle avoidance because of the redundant input data. For autonomous navigation, the controls of the fuzzy and neural network are integrated. The system's feasibility is demonstrated by means of an experiment.
048 Modelling and Control of a Wheeled Mobile Robot G.M. van der Molen, pp 28%292 Kinematic and dynamic models of a car-like mobile robot are presented. Part of the kinematic model is applicable to most wheeled robots, and part is specific for vehicles with coupled front wheel steering. The dynamic modelling process follows a structured method using bond grapths to develop the mathematical (state-space) model from the physical system descripton. The controller consists of two levels, the lower of which is specific to the MARIE robot and the higher of which is applicable to most robots, wheeled or legged. The resulting control structure is placed in a theoretical framework.
049 Robust Tracking Control of Two Degrees of Freedom Mobile Robots* W. Oelen, J, van Amerongen, pp 293-298 A tracking controller has been developed for a mobile robot (MR) with two degrees of freedom. Where other controllers show decreasing performance for low reference velocities, the performance of this controllerdepends only on the geometry of the reference trajectory. This allows accurate positioning at low speeds, close to obstacles. The dynamics of the velocity-controlled MR are considered as a perturbed unity transfer from input velocity to actual velocity. A position controller is developed, which is robust with respect to these perturbations.
050 Lateral Motion Control of Mobile Robots E.A. Puente, M.A. Salichs, L. Morencb J. Pimentei, pp 299-303 In many applications it is necessary to control the lateral position of a mobile robot. When the robot is near an obstacle, the control system must keep a safe distance between the obstacle and side of the robot. Similar circumstances appear when the robot is moving in a corridor, or when it is following a road. This paper analyzes the lateral behaviour of the robot, from a control point of view. As a result of the analysis some general control criteria are found. The criteria allow a suitable selection of various parameters, for example the point from which the distance feedback is taken.
051 An Intelligent Approach to Robust Real-Time Trajectory Control for Advanced Flight Vehicles G.E. Chamitoff, pp 305-310 This paper presents a new flight control method that is generally applicable to tracking control problems with nonlinear dynamics, model uncertainty, multiple constraints, and bounded disturbances. This approach uniquely combines intelligent optimization methods with Lyapunov stability theory in order to perform robust realtime trajectory control. The efficiency of an intelligant approach, and the theoretical guarantees of an analytical method, are both achieved. This method is applied to the flight control of air-breathing hypersonic vehicles.
052 Design of Fuzzy Learning Compensators and Controllers for Autonomous, Redundant Robot Manipulators R. Vepa, A. Now&, pp 311-316 This paper presents two techniques for learning rules for implementing fuzzy controllers, the first of which is based on the application of a suitable control strategy. The second seeks to generate the appropriate control rules to drive the plant towards the desired response. Both techniques are discussed in the context of their application to robot manipulators.
Abstracts
integrate the conflicting demands of navigation and obstacle avoidance. This paper introduces a fuzzy logic based controller which navigates a conventionally steered vehicle and which is combined with an obstacle avoidance scheme before the defuzzification stage of the controller. Simulation results are presented, demonstrating that this combination produces a sensible fusion of these two functions.
044 Towards using Neural Network for Automatic Guidance of Mining Robots J.B. Edwards and S. Yaacob, pp 263-268 In this paper a neural-network controller is investigated as a possibility for implementing this type of controller in certain operations pertaining to the automatic guidance of mining robots. Preliminary results obtained from the neural-network controller show an improvement over the tractable analytic optimal controller (derived for a necessarily simplified system).
045 Automatic Road Following using Fuzzy Control* B. Bltchl, S. Tsinas, pp 269-274 Lateral control of a vision-guided autonomous mobile vehicle is shown as an application of a fuzzy controller. A description of the experimental vehicle and the image processing used in the recognition of the pathway are given. The design of the fuzzy controller and the results of the experiments are described. As a proof on an actual implementation, a real-time application, namely the behaviour module "follow corridor", is demonstrated on an indoor vehicle.
046 Fuzzy Supervisory Path Tracking of Mobile Robots* A. Ollero, A. Garcia-Cerezo, J.L. Martinez, pp 275-280 In this paper a new method for automatic path tracking of mobile robots and autonomous vehicles is proposed. The paper also includes the application to RAM-l, a new mobile robot testbed designed and built for research and applications in indoor and outdoor industrial environments. The method generates the appropriate vehicle steering angle command by combining fuzzy logic with the geometric pure-pursuit technique and the generalized predictive control method. In the proposed fuzzy pathtracking strategy the control parameters of these methods are inferred automatically in real time from the characteristics of the current segment of the path to follow, the vehicle's velocity and its current relative position and orientation.
047 Intelligent Navigation Control of an Autonomous Mobile Robot Young Hoon Joo, Hee Soo Hwang, Kwang Bang Woo, Kun Woong Bae, Sung KwunKlm, pp 281-286 This paper proposes a method for navigation and obstacle avoidance of an autonomous mobile robot, based on a fuzzy inference system which enables it to deal with imprecise and uncertain information, and also a neural network which enables it to learn input and output pattern data obtained from ultrasonic sensors. A fuzzy model for wall-following navigation utilizing input-output data is consWucted for autonomous navigation. An approach using the neural network is developed for obstacle avoidance because of the redundant input data. For autonomous navigation, the controls of the fuzzy and neural network are integrated. The system's feasibility is demonstrated by means of an experiment.
048 Modelling and Control of a Wheeled Mobile Robot G.M. van der Molen, pp 28%292 Kinematic and dynamic models of a car-like mobile robot are presented. Part of the kinematic model is applicable to most wheeled robots, and part is specific for vehicles with coupled front wheel steering. The dynamic modelling process follows a structured method using bond grapths to develop the mathematical (state-space) model from the physical system descripton. The controller consists of two levels, the lower of which is specific to the MARIE robot and the higher of which is applicable to most robots, wheeled or legged. The resulting control structure is placed in a theoretical framework.
049 Robust Tracking Control of Two Degrees of Freedom Mobile Robots* W. Oelen, J, van Amerongen, pp 293-298 A tracking controller has been developed for a mobile robot (MR) with two degrees of freedom. Where other controllers show decreasing performance for low reference velocities, the performance of this controllerdepends only on the geometry of the reference trajectory. This allows accurate positioning at low speeds, close to obstacles. The dynamics of the velocity-controlled MR are considered as a perturbed unity transfer from input velocity to actual velocity. A position controller is developed, which is robust with respect to these perturbations.
050 Lateral Motion Control of Mobile Robots E.A. Puente, M.A. Salichs, L. Morencb J. Pimentei, pp 299-303 In many applications it is necessary to control the lateral position of a mobile robot. When the robot is near an obstacle, the control system must keep a safe distance between the obstacle and side of the robot. Similar circumstances appear when the robot is moving in a corridor, or when it is following a road. This paper analyzes the lateral behaviour of the robot, from a control point of view. As a result of the analysis some general control criteria are found. The criteria allow a suitable selection of various parameters, for example the point from which the distance feedback is taken.
051 An Intelligent Approach to Robust Real-Time Trajectory Control for Advanced Flight Vehicles G.E. Chamitoff, pp 305-310 This paper presents a new flight control method that is generally applicable to tracking control problems with nonlinear dynamics, model uncertainty, multiple constraints, and bounded disturbances. This approach uniquely combines intelligent optimization methods with Lyapunov stability theory in order to perform robust realtime trajectory control. The efficiency of an intelligant approach, and the theoretical guarantees of an analytical method, are both achieved. This method is applied to the flight control of air-breathing hypersonic vehicles.
052 Design of Fuzzy Learning Compensators and Controllers for Autonomous, Redundant Robot Manipulators R. Vepa, A. Now&, pp 311-316 This paper presents two techniques for learning rules for implementing fuzzy controllers, the first of which is based on the application of a suitable control strategy. The second seeks to generate the appropriate control rules to drive the plant towards the desired response. Both techniques are discussed in the context of their application to robot manipulators.