Abstract
By forming a specific formation during motion, the robotic swarm is a good candidate for unknown region exploration applications. The members of this kind of system are generally low complexity, which limits the communication and perception capacities of the agents. How to merge to the desired formation under those constraints is essential for performing relevant tasks. In this paper, a limited visual field constrained formation control strategy inspired by flying geese coordinated motion is introduced. Usually, they flock together in a V-shape formations, which is a well-studied phenomenon in biology and bionics. This paper illustrates the proposed methods by taking the research results from the above subjects and mapping them from the swarm engineering point of view. The formation control is achieved by applying a behavior-based formation forming method with the finite state machine while considering anti-collision and obstacle avoidance. Furthermore, a cascade leader–follower structure is adopted to achieve the large-scale formations. The simulation results from several scenarios indicate the presented method is robust with high scalability and flexibility.
Highlights
Swarm robotics is a research field of the multi-robot system inspired by the self-organizing behavior of social animals such as birds, bees, fish, and so forth [1]
Based on some previous works [7,9,17,26], here, we employ a modified leader–follower structure combined with a behavioral finite state machine to achieve the V-shaped formation control under the constraints we assumed above
It can be seen from the listed results above that the presented formation forming strategy for robotic swarms is proven to be effective with different populations
Summary
Swarm robotics is a research field of the multi-robot system inspired by the self-organizing behavior of social animals such as birds, bees, fish, and so forth [1]. We have shown a formation-based distributed processing paradigm for collaborative searching of swarms in a scanner-like manner with a moving line formation [5]. We extended this paradigm to more general cases for line formation and for V-shaped formations [6]. In those works, the moving formations are treated as a sensor network with dynamically changing positions, so that multi-dimensional based algorithms could be applied in a distributed way. We deal with how to get those formations under the constraints of limited visual sensing and communication abilities of each swarm member
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