Authors:
Ellips Masehian
and
Mitra Royan
Affiliation:
Tarbiat Modares University, Iran, Islamic Republic of
Keyword(s):
Robot Flocking Systems, Shepherding, Object Collecting, Fuzzy Expert System, Obstacle Avoidance.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Robotics and Intelligent Agents
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
Shepherding behavior is a class of collective behaviors in flocking systems which requires that a swarm of mobile robots enter an area populated with known or unknown obstacles, collect a flock of static or dynamic particles (objects), and guide them safely to a predefined goal position. Applications of this behavior are in sheep or duck shepherding and fishing. In this paper, a new algorithmic model is developed for online for-mation control, decision making, behavior selection, and motion planning of a team of homogeneous and anonymous (no leader and follower) flocking robots which simultaneously perform object collecting and shepherding tasks. The model’s architecture is enriched with various complex flocking actions such as flock deformation, flock split and merge, flock expansion, and flock obstacle avoidance. Contributions of this paper include (i) defining a new class of problems for flocking robots called Simultaneous Object Collecting and Shepherding (SOCS) problem, (ii) inc
orporating online obstacle sensing and avoidance methods in the flocking behavior, and (iii) developing a fuzzy expert system for determining the strategy of environment exploration. The fuzzy inference engine provides an effective way to minimize the time spent on collecting objects while maximizing the gain obtained by object collection, in a way that the flock’s formation and in-tegrity is maintained. The proposed model was implemented on a number of simulations and produced rational and satisfactory results.
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