Discrete Event Systems for autonomous mobile agents

Košecká, Jana; Bajcsy, Růžena

doi:10.1016/0921-8890(94)90025-6

Cited by 65 publications

(29 citation statements)

References 6 publications

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“…The temporal order represented by a finite state machine (FSM) is used to model the deliberative reasoning. We use fuzzy logic inference to determine the inputs to the FSM in this paper, which is similar to the discrete event system in [36].…”

Section: Work In This Papermentioning

confidence: 99%

Integration of Coordination Architecture and Behavior Fuzzy Learning in Quadruped Walking Robots

2007

IEEE Trans. Syst., Man, Cybern. C

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Abstract-This paper presents the design and implementation of a coordination architecture for quadruped walking robots to learn and execute soccer-playing behaviors. A typical hybrid architecture combing reactive behaviors with deliberative reasoning is developed. The reactive behaviors directly map spatial information extracted from sensors into actions. The deliberative reasoning represents temporal constraints of a robot's strategy in terms of finite state machines. In order to achieve real-time and robust control performance in reactive behaviors, fuzzy logic controllers (FLCs) are used to encode the behaviors, and a two-stage learning scheme is adopted to make these FLCs adaptive to complex situations. The experimental results are provided to show the suitability of the architecture and effectiveness of the proposed learning scheme.Index Terms-Behavior-based control, fuzzy logic controller (FLC), reinforcement learning (RL).

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Section: Work In This Papermentioning

confidence: 99%

Integration of Coordination Architecture and Behavior Fuzzy Learning in Quadruped Walking Robots

2007

IEEE Trans. Syst., Man, Cybern. C

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“…on tasks that are considered to be relevant. CAMPOUT implements priority-based arbitration, where behaviors with higher priorities are allowed to suppress the output of behaviors with lower priorities; and state-based arbitration which is based on the discrete event systems (DES) formalism [34], and is suitable for behavior sequencing.…”

Section: Behavior Coordination Mechanismsmentioning

confidence: 99%

CAMPOUT: a control architecture for tightly coupled coordination of multirobot systems for planetary surface exploration

Huntsberger

Pirjanian²,

Trebi-Ollennu

et al. 2003

IEEE Trans. Syst., Man, Cybern. A

101

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Abstract-Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads.

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“…The states of the fundamental process correspond t o a basic motion/sensing strategy and the transitions between the states are created by external events (depending on the task) or by the successful completion or failure of the strategy. Each fundamental process is modeled as a DES system as described by its associated automaton = (Q, C, 6, go, Qm) in the fashion defined in [19]. Events are classified into two categories: controllable events &(the ones which may be prevented from occurring or forced to occur) and uncontrollable events C,(those which may not be prevented from occurring).…”

Section: Discrete Event Systems Modelmentioning

confidence: 99%

Cooperative material handling by human and robotic agents: Module development and system synthesis

Adams

Bajcsy

Košecká

et al. 1996

Expert Systems with Applications

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In this paper we present the results of a collaborative effort to design and implement a system for cooperative material handling by a small team of human and robotic agents in an unstructured indoor environment. Our approach makes fundamental use of human agents' expertise for aspects of task planning, task monitoring, and error recovery. Our system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of human abilities within the present state of the art of autonomous systems. It is designed to allow for and promote cooperative interaction between distributed agents with various capabilities and resources. Our robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. Our robotic agents are not required to be homogeneous with respect to either capabilities or function. Our research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to the basic functioning of such a cooperative multi-agent system. We have constructed a testbed facility for experimenting with distributed multi-agent architectures. The required modular components of this testbed are currently operational and have been tested individually. Our current research focuses on the integration of agents in a scenario for cooperative material handling. Comments

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Discrete Event Systems for autonomous mobile agents

Cited by 65 publications

References 6 publications

Integration of Coordination Architecture and Behavior Fuzzy Learning in Quadruped Walking Robots

Integration of Coordination Architecture and Behavior Fuzzy Learning in Quadruped Walking Robots

CAMPOUT: a control architecture for tightly coupled coordination of multirobot systems for planetary surface exploration

Cooperative material handling by human and robotic agents: Module development and system synthesis

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