Swarm Robots Search for Multiple Targets

Qirong Tang,Zhipeng Xu,Peter Eberhard,Fangchao Yu

doi:10.1109/access.2020.2994151

Qirong Tang, Zhipeng Xu + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.2994151

Copy DOI

Abstract

This paper addresses the challenge of swarm robots search for multiple targets simultaneously. Techniques are investigated gradually and a systematic scheme which is based on mechanical particle swarm optimization and artificial potential field is eventually developed. The innovative extension makes the bio-inspired particle swarm optimization first endowed with the robots’ mechanical properties which reduces the control expense and is already beyond the conventional application scope of this algorithm. The scheme closely considers the practical applications of real robots thus uses the differences, for example, signal frequencies, between the targets for organizing corresponding sub-robot groups aiming at different targets. Those robot groups which move towards non-aimed targets are applied with penalties thus an unimodal objective function for each robot group is built. Meanwhile, the developed method contains the ability for obstacle avoidance based on the module-switching strategy according to their priorities. The methods for controlling the group size and make balance of the search convergence and diversity are investigated, too. Rich simulations and experiments with real robots have been performed to verify this work. Promising results show the effectiveness and robustness of the proposed search method.

Highlights

Researchers have proposed many methods which are inspired from biology and nature
The traditional methods are often pale in comparison with bio-inspired ones since the nature brings us highly adaptive and optimized systems. These methods are usually categorized as ‘bio-inspired’ and have been used in many areas including the robotics field, e.g., Sedlaczek and Eberhard used an augmented Lagrangian particle swarm optimization (ALPSO) for the structural optimization of a hexapod robot [1], Arena et al introduced the locomotion generation for a drosophila inspired legged robot [2], Boyer et al reported a robotic navigation sensor inspired by an electric fish [3], a crawling robot is designed inspired from inchworms [4], and a biomimetic wet adhesion pad for a wall-climbing robot was developed inspired from arthropods like stick insects [5], a self-organized swarm robot for target search and trapping was proposed inspired by bacterial
Doctor et al discussed using a bio-inspired algorithm of particle swarm optimization (PSO) for multi-robot search tasks with focus on optimizing PSO parameters [8], Tan et al proposed an ant colony algorithm for mobile robots robots realtime optimal path planning [9], Kisdi and Tatnall simulated the case of robots search for caves on mars using honey bees search strategy [10], Varga et al presented a method based on ant colony and honey bees behaviors for target localization and decision making [11], respectively

Summary

INTRODUCTION

Researchers have proposed many methods which are inspired from biology and nature. The traditional methods are often pale in comparison with bio-inspired ones since the nature brings us highly adaptive and optimized systems. Chemotaxis [6], whereas collective behavior is designed for a termite inspired autonomous robots team for construction task [7] Among those many bio-inspired methods, mimicking the behaviors of swarms of eusocial animals for robots to perform target search has received extreme attentions. Handling multiple targets search based on our previous investigations and performed by swarm robots becomes a natural research continuation. It mainly focuses on investigating strategies and methods for swarm robots multiple targets search in an unknown environment.

RELATED WORK

METHOD PROPOSED FOR SINGLE TARGET SEARCH

SWARM ROBOTS SEARCH FOR MULTIPLE TARGETS

VERIFICATION OF THE METHODS FOR A SINGLE TARGET SEARCH

VERIFICATION OF THE METHODS FOR THE MULTIPLE

CONCLUSION