Author_Institution :
Collective Robotics Group, California Inst. of Technol., Pasadena, CA, USA
Abstract :
In this paper, we present an optimal design for local explicit communication and collaboration in a swarm-based, mobile, robotic system. We propose a methodology for finding the design parameter, i.e., the communication range, for a fast, distributed information exchange and dissemination among the teammates, and a reliable and efficient information retrieval by the base station(s). Our approach consists of maximizing the interaction/collaboration rate, and hence the rate of information exchange between the mobile agents in the presence of destructive interference. The proposed approach takes into account the spatial distribution of the agents, which is a function of the agents´ movement pattern in the environment. We illustrate the usefulness and efficiency of local interaction between the teammates and its effect on information retrieval, first by presenting a collaborative, sensing task consisting of using a team of autonomous agents with limited sensing capabilities to count some objects scattered throughout an enclosed region. Then we introduce a general mathematical methodology for maximizing the collaboration rate, and hence the rate of information spread in similar networks of multiple, cooperating mobile agents. We validate our results with a sensor-based simulator of real, mobile robots.
Keywords :
evolutionary computation; information dissemination; information retrieval; mobile agents; mobile robots; optimisation; base station; collaboration; collaborative sensing task; communication range; design parameter; distributed information retrieval; information dissemination; interaction/collaboration rate; local explicit communication; mobile autonomous agents; mobile robotic system; multiple cooperating mobile agents; optimal design; sensor-based simulator; spatial distribution; swarm-based networks; Autonomous agents; Biology computing; Collaboration; Information retrieval; Intelligent networks; Interference; Mobile agents; Mobile communication; Mobile robots; Particle swarm optimization;