Robotic Excavation: Experimental Results Using Fuzzy Behavior Control

Copyright © 1996 IFAC 13th Triennial World Congress. San Francisco, USA

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ROBOTIC EXCAVATION: EXPERIMENTAL RESULTS USING FUZZY BEHAVIOR CONTROL Xiaobo Shit :!:, Fei-Yue Wangt and Paul J.A. Lever:!: t Robotics and Automation LAboratory, Systems and Industrial Engineering Department *Advanced Mining Systems Laboratory, Mining and Geological Engineering Department The University ofArizona, Tucson, Arizona 85721, USA

Abstract. This paper presents experimental results for robotic excavation based on fuzzy behaviors. An excavation goal is achieved through excavation tasks. each of which is completed via sequences of behaviors that are carried out by primitive actions. Both tasks and behaviors are specified by fmite state machines. Behavior selection is achieved through situation assessment and behavior arbitration. A method of terminating a behavior execution is proposed. Excavation actioos are specified using fuzzy logic rules acquired frOOt human experience and heuristics. Experimental results indicate that the proposed formulation has led to a more efficient execution of excavation tasks than in a previous formulation. Keywords. fuzzy-logic; fuzzy behavior control; rock excavation; artifIcial intelligence.

1. INTRODUCTION Front-end-loader (FEL) type excavatm; are used for many rock/soil excavation and loading activities at construction sites. mines, hazardous waste cleanup locations. and road works. These expensive machines often place the operators in hazardous environments. or could be assigned to operate in places that are unsuitable for humans (waste sites, space).

The unpredictable and extremely variable conditions in the dynamic and unstructured operating environment of a FEL makes control of robotic excavation machines difficult. Much of the excavator control research has concentrated on control and modeling of backhoe type excavators (RomeroLois. et al.. 1989; Vaha and Skibniewski, 1993). The active control methods proposed for the cutting blade (Kuhn. 1954; Alekseeva, et al.. 1985; Desai, et al.• 1982) have been the basis for many studies. But these approaches require a mathematical model for machine environment interactions.

Bemold (1993) has analyzed bucket force/torque sensor data to determine bucket excavation status for control purposes. However. results have shown that modeliDg the dynamics of tool/soil interactions is very difficult and computationally intensive, thus is not practical for real-time and online execution. This makes conventiooal control system designs that are based on differential and/or difference equations infeasible or impractical for the problem of robotic excavation in dynamic and unstructured environments. On the other hand, skilled human operators can attain sophisticated control of excavation machines in these environments. Therefore. the approach to developing an excavation control system is based on using knowledge from skilled human operators. Previous studies (Lever and Wang, 1995; Lever. et al.. 1994, 1995) have described how the intrinsically imprecise. vague and uncertain linguistic terms used to describe human skills and expertize, can be implemented using fuzzy logic based control algorithms. In addi-

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ifles all the feasible sequences of behaviors for its completion. while a FSM for a behavior defines all the possible sequences of actions for its execution. Implementation of actims is discussed in the next section. 3.1 Task Formulation Let ST = [T,....• TNI represent the set of all the tasks that have been implemented for a robotic excavation system. Each task is accomplished by a set of excavation behavias. To specify all the feasible sequences of behaviors for the task completion. a FSM is used. T j = (s?,Sj.~.Hj.Oj),

(1)

where Sj is the set of states of Tj • s? E Sj is the initial state. S~ C Sj is the set of terminal states indicating the task c:mpletion. H j = (BI ..... B~j} is the set of behaviors applicable to Tj • and OJ: Sj x H j - - Sj is the state transition function. i.e .. OJ(sj.B) = Sk indicates that if behavior B E H j is executed under the current state Sj' then the next state will be Sk. where Sj' Sk E Sj' Examples of excavation tasks include. excavate a trench, fill the bucket from a rock pile ,fill the bucket at a constructed load point, remove large rock, load a truck and clean the floor. Figure 2 shows the fInite state machine for the task remove large rock. where the behavior set H = {b l : move-to-location. b2 : map-rock. b3 : return. b4 : selectentry-point. bs : Lift-rock. b6 : extract-bucket. b7 : dumprock. Bd. Here. BI defmes the appropriate set of excavatim behaviors in the context of the remove large rock task. Clearly. multiple behaviors are applicable at a given state. Let Hj(s) defme all possible behaviors under state s. i.e .•

Hj(s)

=

{B I B E Hj and b(s.B) is defined}

(2)

The decisim problem here is to choose the most appropriate behavior among HM) based on sensory feedback. A two-step solution using situation assessment and behavior

arbitration has been implemented using neural networks (Lever. et al .• 1995). 3.2 Behavior FormuLation Let SB = {B I ..... BM} represent the set of all the behaviOOi that are available fa robotic excavation tasks. Each behavior is carried out by a set of primitive motion actions which are directly machine executable. To specify all possible sequences of actions for behavior execution. a FSM is used. B j = (s?Sj.S{.Aj.b j)

(3)

similar to Tj • s? and S j are the initial state and the set of states of B j. Sf is the set of terminal states indicating the behavior C
Aj(s)

=

(a I a E Ai and O(S. a) is defined}

(4)

In this case. simple rules are used to select the best action for the given state based on force/torque readings. Note that for an action FSM. force/taque readings are evaluated only at the initial state.

4. FUZZY ACTION IMPLEMENTATION Fig. 2 Finite state machine fa task remove larKe rock

An excavation behavior is implemented by a sequence of primitive. machine executable motion actims. Based on the

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