Flexible and Hybrid Action Selection Process for the Control of Highly Dynamic Multi-Robot Systems

Flexible and Hybrid Action Selection Process for the Control of Highly Dynamic Multi-Robot Systems

Lounis Adouane
DOI: 10.4018/978-1-4666-9572-6.ch020
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This chapter presents a behavioral mechanism of control in order to break the complexity of multi-robot control systems. Specifically, this chapter proposes a Hierarchical Action Selection Process (HASP) which aims to coordinate a set of elementary controllers endowed in behavioral control architectures. This process allows at the scale of the robot to coordinate in a hierarchical and flexible way the activity of a set of elementary controllers, and at the scale of the group, the coordination of robots' interactions for reaching global objective and desired mass effects. The performances of the HASP were improved via the addition of an appropriate mechanism of fusion of actions leading thus to a Hybrid-HASP. Two architectures of control using respectively the HASP and the Hybrid-HASP are proposed to achieve the cooperative box-pushing task. The validation of the proposed mechanisms of control was made through experimentations using minimalist mobile robots and more intensively according to statistical studies achieved on a large number of data gotten thanks to MiRoCo simulator.
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1. Introduction

The domain of cooperative robotics constitutes an active research field and is currently linked to many key application areas with large importance. A variety of tasks is performed in a more reliable and flexible way by teams of robots cooperating among them and/or with humans. These tasks concern collaborative manipulation, cooperative search and transportation, ground, space and underwater exploration, surveillance and autonomous rescue operations. The scientific problems associated to multi-robot systems concern formation analysis and control, cooperative perception, multi-robot self-localization, multi-robot task coordination, architectures for cooperation and communication Cao et al. (1997), Ota (2006). Multi-robot cooperation offers advantages like acceleration of performance due to the parallel processing and coordination among robots and also robustness due to the presence and the modularity of multiple robots. Nevertheless, the coordination of multi-robot team in dynamic environments constitutes one of the fundamental problems. Coordination implies normally synchronization of robot actions and exchanges of information among the robots. The amount of synchronization and communication depends heavily on the tasks requirement, characteristics of the robot and its environment. More particularly, the control of simple and reactive group of robots in a distributed manner is a complex problem because global behaviors must emerge as a result of many local interactions Forrest (1991), (Arkin, 1998, p.105), Shen et al. (2004). The objective of our works is to control as minimalist as possible robotics entities Melhuish (2001), Adouane and Le Fort-Piat (2004a) and to use the local interactions between them to produce a form of advanced collective intelligence. More specially, the presented work concern the design and development of a control architecture exhibiting as well individual features adapted to highly dynamic multi-robot systems (a swarm of robots) than collective features favoring global goals. The architectures presented in this chapter are fully distributed and are based on reactive behaviors in order to perform complex cooperative tasks while using robots with limited sensorial and computational capabilities. The proposed process of coordination between behaviors is based on one hand, on a hierarchical coordination and on the other hand, on fusion mechanism. The goal is to select and combine appropriately elementary behaviors responses so that the robots will be able to perform complex tasks.

Key Terms in this Chapter

Reactive Control: The way to control any robotic entities without very high cognition aspect. The robot is generally controlled based on simple stimuliresponse behaviors.

Altruistic Behavior: Bio-inspired concept which correspond to the fact to generate a signal/action with the objective to communicate/cooperate with its neighbors, and this without immediate obvious gain for the entity which generate this effect.

Behavioral Emergence: Concept which aim to obtain a high level (or complex) behavior from local and simple entities’ interactions.

Cooperative Box Pushing Task: Is among the privileged complex task used in the literature in order to study the relevance of reactive and distributed control architectures.

Behavioral Arbitration Mechanism: Responsible to manage the interaction between several elementary behaviors. The two most important mechanisms used in the literature to manage behavioral architectures are Action selection and Fusion of actions mechanisms.

Collaborative Robotics Control: A part of robotics which use the interaction of the robots either to enhance certain criteria or to permit to achieve certain complex tasks.

Behavior-Based Control: A way to control a complex system while decomposing it on several sub-tasks (elementary behaviors) to achieve.

Swarm Robotics: Corresponds to a relative new approach to perform the coordination of a large group of robots without neither centralized control nor high level cognition between the robots.

Low-Level Communication: Is a communication protocol, inspired by the society of insects, which does not need high level information exchanges between the senders and the receivers.

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