Abstract
Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.
Highlights
The process of searching for a specified target in an unknown environment by a group of robots is important for solving various problems in unknown environments, such as navigation [1,2,3], searching the whole area to solve the area coverage problem [4] and realism the whole area’s the simultaneous localization and mapping (SLAM) [5]
Even for exploration tasks, many algorithms still rely on centralized control and the accurate positioning system, and even the principal idea of swarm robotics of distributed decision making is neglected [32]. e path formation strategies for swarm robotics can be divided into the following categories: (1) Strategy based on a priori experience: Sperati et al [22] and Motoaki et al [33] trained a group of robots by a neural network model so that the robot explored the entire area and formed a path between the two specified locations
We succeed in designing a new method inspired by Physarum polycephalum for multirobot systems to perform path formation tasks
Summary
The process of searching for a specified target in an unknown environment by a group of robots is important for solving various problems in unknown environments, such as navigation [1,2,3], searching the whole area to solve the area coverage problem [4] and realism the whole area’s the simultaneous localization and mapping (SLAM) [5]. Many studies are based on observations of biological organisms, with the goal of simulating their intelligent behaviour from physical, chemical, biological, and other perspectives for application to various practical problems, such as wireless sensor networks (WSNs) [17] and the travelling salesman problem (TSP) [18] Such a foraging behaviour without central control meets the requirements of a multirobot system. We draw inspiration from the foraging behaviour of Physarum polycephalum and present the first proposal that combines this behaviour with the requirements of path formation tasks for swarm robots. We use the same environment that was used to test the Physarum-inspired multiagent system (P-MAS) [19] to verify that our algorithm is effective in solving maze problems and exhibits search characteristics similar to the foraging behaviour of Physarum polycephalum. For the multirobot system, there are more challenges: (1) e robot operating environment is not composed of chemical nutrients, and the robot cannot release chemical nutrients to induce other individuals to move. (2) In the swarm robot system, collisions will affect the overall operation effect, while the virtual multiagent system can overlap in the same position without the impact of collisions. is work is the first one combining multirobot systems and Physarum polycephalum
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