Abstract

Field of robotics has been under the limelight because of recent advances in Artificial Intelligence (AI). Due to increased diversity in multi-agent systems, new models are being developed to handle complexity of such systems. However, most of these models do not address problems such as; uncertainty handling, efficient learning, agent coordination and fault detection. This paper presents a novel approach of implementing Reinforcement Learning (RL) on hierarchical robotic search teams. The proposed algorithm handles uncertainties in the system by implementing Q-learning and depicts enhanced efficiency as well as better time consumption compared to prior models. The reason for that is each agent can take action on its own thus there is less dependency on leader agent for RL policy. The performance of this algorithm is measured by introducing agents in an unknown environment with both Markov Decision Process (MDP) and RL policies at their disposal. Simulation-based comparison of the agent motion is presented using the results from of MDP and RL policies. Furthermore, qualitative comparison of the proposed model with prior models is also presented.

Highlights

  • Artificial intelligence (AI) based robotics is gaining significant importance because of popularity in the field of Unmanned Search and Rescue (USR) operations

  • The proposed model is this paper can handle all of these problems but other approaches lack in providing a solution for some of these problems

  • A comparison between Reinforcement learning technique and previous Markov Decision Process (MDP) technique mentioned in [35] demonstrates an addition of learning topology and fault detection based on states in which agent is present

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Summary

Introduction

Artificial intelligence (AI) based robotics is gaining significant importance because of popularity in the field of Unmanned Search and Rescue (USR) operations. USR is of utmost importance when it comes to deal with both natural and human created disasters. During these disasters using a robot instead of humans and rescue dogs seems both reasonable and logical. After an unfortunate disaster of World Trade Center (WTC) in 2001, the feasibility and performance of robotic search teams have been studied [1]. In 2011, robots performed surveillance and rescue during earthquake in Japan.

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