Do whatever works: A robust approach to fault-tolerant autonomous control

Payton, David W.; Keirsey, D.; Kimble, Dan M.; Krozel, Jimmy; Rosenblatt, Julio K.

doi:10.1007/bf00119550

Cited by 56 publications

(24 citation statements)

References 16 publications

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“…5. In Spikes and Zones [15], the Behavior Output includes both spikes, desired control outputs, and zones, acceptable intervals for the control output. The Arbitration then tries to find a spike inside the intersection of all zones.…”

Section: Background: Modular Control Architecturesmentioning

confidence: 99%

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A Model Based Approach to Modular Multi-Objective Robot Control

Ögren

Robinson

2011

J Intell Robot Syst

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Two broad classes of robot controllers are the modular, and the model based approaches. The modular approaches include the Reactive or Behavior Based designs. They do not rely on mathematical system models, but are easy to design, modify and extend. In the model based approaches, a model is used to design a single controller with verifiable system properties. The resulting designs are however often hard to extend, without jeopardizing the previously proven properties. This paper describes an attempt to narrow the gap between the flexibility of the modular approaches, and the predictability of the model based approaches, by proposing a modular design that does the combination, or arbitration, of the different modules in a model based way. By taking the (model based) time derivatives of scalar, Lyapunov-like, objective functions into account, the arbitration module can keep track of the time evolution of the objectives. This enables it to handle objective tradeoffs in a predictable way by finding controls that preserve an important objective that is currently met, while striving to satisfy another, less important one that is not yet achieved. To illustrate the approach a UAV control problem from the literature is solved, resulting in comparable, or better, performance.

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Section: Background: Modular Control Architecturesmentioning

confidence: 99%

“…Examples of selection schemes are [4,5,10], while fusion methods include [7,8,15,17,19]. The modular methods have received a lot of attention due to the fact that they are flexible, robust and easy to use.…”

Section: Introductionmentioning

confidence: 99%

A Model Based Approach to Modular Multi-Objective Robot Control

Ögren

Robinson

2011

J Intell Robot Syst

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“…Numerous principled as well as ad hoc methods for addressing the action selection problem have been developed and demonstrated on robotic systems. These include varieties of command fusion (13) and spreading of activation (14), among many others. For a comprehensive survey on action selection mechanisms, see (15).…”

Section: Behavior-based Controlmentioning

confidence: 99%

Behavior-Based Coordination in Multi-Robot Systems

Jones¹,

Matarić²

2006

Automation and Control Engineering

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The successful deployment of a multi-robot system (MRS) requires an effective method of coordination to mediate the interactions among the robots and between the robots and the task environment in order for a given system-level task to be performed. The design of coordination mechanisms has received increasing attention in recent years and has included investigations into a wide variety of coordination mechanisms. A popular and successful framework for the control of robots in coordinated MRS is behavior-based control (1,2). Behavior-based control is a methodology in which robots are controlled through the principled integration of a set of interacting behaviors (e.g., wall following, collision avoidance, landmark recognition, etc.) in order to achieve desired system-level behavior. This chapter will describe, through explanation, discussion of demonstrated simulated and physical mobile robots, and formal design and analysis, the range and capabilities of behavior-based control applied to multi-robot coordination.

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“…F or example, research is underway t o d e v elop autonomous systems capable of exploring other planets (Angle & Brooks 1990), (Bares & Whittaker 1990) and deep within oceans (Payton, Keirsey, Kimple, Krozel & Rosenblatt 1992), (Stuart 1988). Equipping robots with more sensors increases the quantity and reliability of information the robot can extract from its environment.…”

Section: Introduction 3 Introductionmentioning

confidence: 99%

Global behavior via cooperative local control

Ferrell

1995

Auton Robot

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The purpose of this paper is twofold. First, we outline important issues in designing real-time controllers for robots with numerous sensors, actuators, and behaviors. We address these issues by implementing a behavior based controller on a sophisticated autonomous robot. Hence, this work provides a point of reference for the scalability, ease of design, and e ectiveness of the behavior based control for complex robots.Second, we explore the viability of using cooperation among local controllers to achieve coherent global behavior. Our approach is to decompose a di cult control task for a complex robot into a multitude of simpler control tasks for robotic subsystems. We illustrate and examine the e ectiveness of this approach via rough terrain locomotion using an autonomous hexapod robot. Traversing rough terrain is a good task to test the viability of this approach because it requires a considerable amount of leg coordination.We found that implementing a complicated global control task with cooperating local controllers can e ectively control complex robots.

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Do whatever works: A robust approach to fault-tolerant autonomous control

Cited by 56 publications

References 16 publications

A Model Based Approach to Modular Multi-Objective Robot Control

A Model Based Approach to Modular Multi-Objective Robot Control

Behavior-Based Coordination in Multi-Robot Systems

Global behavior via cooperative local control

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