Bio inspired approaches for target search and job allocation problems in a multi robot team

Abstract

Multi-robot team consists of multiple mobile robots working together newlineto achieve a goal. They provide efficient solutions to a wide range of applications newlinein various fields, including logistics, search and rescue, healthcare, newlineenvironmental monitoring, and warehouse management. Multi-robot target newlinesearch in an unknown environment is a complex problem with practical newlineapplications in various domains. The robots search for the target, utilizing the newlineavailable sensors with them. The exploration of the unknown environment, as newlinewell as the exploitation of the available information, needs to be balanced during newlinethe target search. Similarly, the job allocation among the robots is an important newlinechallenge in a multi-robot system. It involves the effective utilization of newlineresources available to complete the jobs in the environment. The bio-inspired newlineapproaches involve drawing inspiration from the natural world to solve complex newlineproblems. These approaches leverage the principles and strategies of biological newlineorganisms and ecosystems to create efficient solutions. Hence, this research newlinefocuses on employing bio-inspired approaches to address the target search and newlinejob allocation problems in a multi-robot team. newline The Dragonfly Algorithm (DA) is an optimization method that newlinedraws inspiration from the swarming behavior exhibited by dragonflies. The newlineswarming behaviors make it a good choice for addressing the target search newlineproblem using a multi-robot team. The proposed Robotic Dragonfly newlineAlgorithm (RDA) adapts the DA to make it suitable for the multi-robot target newlinesearch problem. Each robot in the team is considered as a dragonfly in the newlineswarm. The robots move in the environment, satisfying their navigational newlineconstraints. The adaptive inertia weight factor added in the RDA algorithm newlineimproves the diversity in the team. The obstacle avoidance for the robots is newlineachieved by utilizing the enemy escaping behavior newline