Avoiding decoys in multiple targets searching problems using swarm robotics

In this paper, we consider the target searching problems with a new type of the object: decoys which can be sensed exactly as targets but cannot be collected by the robots. In real-life applications, decoys are very common especially for swarm robots whose hardware should be designed as simple and cheap as possible. This inevitably brings errors and mistakes in the sensing results and the swarm may mistakenly sense certain kinds of environment objects as the target they are looking for. We proposed a simple cooperative strategy to solve this problem, comparing with a non-cooperative strategy as the baseline. The strategies work with other searching algorithms and provide schemes for avoiding decoys. Simulation results demonstrate that the cooperative strategy shares almost the same computation overload yet has better performance in iterations and especially visited times of decoys. The strategy shows great adaptiveness to large scale problems and performs better when more decoys or robots exist in the simulation.

I. Introduction

Swarm robotics has achieved significant progress benefiting from the development of artificial intelligent [1]. Swarm robotics can be used in many applications, especially those require large amount of robots and time as well as difficult or dangerous for human beings, e.g. foraging [2], surveillance [3], monitoring [4] and search-and-rescue [5]. These applications can be abstracted as a multiple target searching problem with restrictions in the environment, such as obstacles. Searching strategies for solving this abstracted problem can be adapted to many applications and remains an important task for swarm robotics researchers.