A Two-stage Coordinated Strategy for Integrated Exploration of Multi-robot Systems

To improve performance for integrated exploration algorithms of multi-robot systems, a two-stage coordinated strategy is proposed in this paper. In the first stage, all robots explore unknown environments by a kind of forth and back trajectories which are shaped by social entropy and a voting mechanism. In the second stage, the solving process of coordination problem for multi-robot systems is modeled as a blackboard system, and each robot contributes a solution to the question determined by a capacity constrained auction algorithm. The proposed approach is verified by computer simulations for obstacle free environments. Introduction Integrated exploration approaches which considered coordination, mapping, localization and motion control tasks simultaneously is preferred. However, only a few of results about the topic have been reported [1-2]. On the other hand, a great deal of results has been proposed for the coordination of multi-robot systems. There is market economy based approaches [3-6], evolutionary computing based approaches [7-9], decision-theoretic approach [10], etc. These methods realized task assignments. Meanwhile, they considered route planning under intra-path constraints [7], cooperation among robots [3], and dispersion of robots over the environment [10] in the explorations of unknown environments. Shannon’s information entropy has been utilized as a measure for uncertainty about robots [8]. Hierarchic social entropy that is an application of Shannon’s information entropy was introduced as a continuous and quantitative measure for robot group diversity [11]. In this paper, the property that social entropy can represent spatial distribution of homogeneous robots is utilized to shape trajectories of robots over an unknown environment. Moreover, the exploring trajectories of all robots are used to partition operational environment into a set of sections. Notwithstanding the partitioning is not balanced, the subsequent auction actions will balance tasks among robots. Recently, a three-level optimization algorithm was proposed to coordinate emergency medical service robot team [12]. In the approach, a globally efficient solution at each level is obtained by a medication of auction algorithm. The time complexities of sing-item, multi-item (greedy), multi-item (optimal), and combinational auctions were compared in [6]. Moreover, computational complexity exists in determining the cost to perform a task that requires solving path planning of multi-robot systems; an instance is traveling salesman problem (TSP) which is NP-hard. Differently with the available approaches, the auction algorithm is utilized to solve a blackboard system which represents the coordination problem in the proposed approach. At the same time, the dispersion of robots over the environment is considered. Zigzag motion is an effectively way to cover the environment the robots located [13]. Inspired by the result meanwhile considering error corrections, a generalized forth and back -rectangle trajectories are adopted by all robots of a multi-robot system in the present work. The rest of this paper is organized as follows. Section 2 discusses coordination based on social entropy. Section 3 discusses This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Copyright © 2017, the Authors. Published by Atlantis Press. 79 Advances in Engineering Research (AER), volume 131 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017)