From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue (2311.16958v1)

Abstract: In this study, we present a novel hybrid algorithm, combining Levy Flight (LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information. The research addresses the growing interest in employing multiple autonomous robots for exploration tasks, particularly in scenarios such as Urban Search and Rescue (USAR) operations. Multiple robots offer advantages like increased task coverage, robustness, flexibility, and scalability. However, existing approaches often make assumptions such as search area, robot positioning, communication restrictions, and target information that may not hold in real-world situations. The hybrid algorithm leverages LF, known for its effectiveness in large space exploration with sparse targets, and incorporates inter-robot repulsion as a social component through PSO. This combination enhances area exploration efficiency. We redefine the local best and global best positions to suit scenarios without continuous target information. Experimental simulations in a controlled environment demonstrate the algorithm's effectiveness, showcasing improved area coverage compared to traditional methods. In the process of refining our approach and testing it in complex, obstacle-rich environments, the presented work holds promise for enhancing multi-robot exploration in scenarios with limited information and communication capabilities.

• The paper introduces a hybrid LF-PSO algorithm that fuses Levy Flight with Particle Swarm Optimization to boost multi-robot exploration effectiveness.
• It refines traditional PSO by adjusting local and global best definitions and incorporating a repulsion mechanism to reduce overlapping search paths.
• Simulation results demonstrate superior area coverage compared to standard methods, indicating promising efficiency in urban search and rescue scenarios.

Introduction

Autonomous robots have become increasingly valuable for tasks in challenging environments where human intervention might be risky or impractical. Among the many applications, one rapidly growing field is Urban Search and Rescue (USAR) operations, where multiple robots are deployed to navigate complex and potentially hazardous areas, such as post-disaster environments, to locate survivors. Multi-robot systems offer benefits like task distribution, robustness, and large-area coverage, but the effectiveness of their exploration capabilities largely depends on their search algorithms.

Hybrid Algorithm for Exploration

A paper introduces a hybrid algorithm that combines Levy Flight (LF) and Particle Swarm Optimization (PSO), specifically tailored for multi-robot exploration in environments where initial data regarding the location and terrain are limited. The new LF-PSO algorithm integrates LF known for its random search behavior ideal for exploring large spaces with sparse targets, with a PSO component that includes a repulsion mechanism among robots to minimize overlapping paths, thus increasing the thoroughness of the search.

Refinements for Practical Applications

One key aspect of the paper is its focus on addressing common assumptions prevalent in prior research, such as known search space and robot positioning, as well as unrestricted communication and prior target location information. The LF-PSO algorithm operates without a global positioning system and standard communication capabilities, making it a practical solution for real-world scenarios. The research tweaks the definitions of the local and global best positions in the PSO framework to cater to scenarios without continuous target information, allowing for efficient and cooperative searching with minimal prior knowledge.

Simulation Results and Future Work

The researchers tested the algorithm through simulations involving various scenarios, such as robots starting at random positions and from specific points within a designated area. The LF-PSO demonstrated excellent area coverage, surpassing traditional PSO and LF algorithms in efficiency. Initial results are promising, with high percentages of the search area being explored within limited time frames. Follow-up studies aim to explore more complex scenarios with obstacles and the incorporation of target detection tasks. Future directions also include further finetuning of parameters and extending the simulation environment to continue improving the algorithm's usability and efficiency in diverse applications beyond USAR, such as space exploration.

The advancements demonstrated by the LF-PSO algorithm indicate a step forward in the practical deployment of autonomous multi-robot systems for critical search operations. With further development and refinement, this research has the potential to revolutionize how robots collaboratively explore unknown and dangerous terrains, ultimately saving lives and resources.