Prof. Michael Santora Profile

Prof. Michael Santora

at Univ. of Detroit Mercy

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Proceedings Article | 7 June 2024 Presentation + Paper

Multi-robot path planning using potential field-based simulated annealing approach

Saleh Alarabi, Tingjun Lei, Michael Santora, Chaomin Luo, Timothy Sellers

Proceedings Volume 13055, 130550D (2024) https://doi.org/10.1117/12.3013871

KEYWORDS: Neurons, Algorithms, Neural networks, Navigation systems, Systems modeling, Algorithm development, Mathematical optimization, Particles, Computation time, Particle swarm optimization

Read Abstract +

The rapid growth of robot applications across various sectors, such as agriculture, disaster management, and package delivery, has led to an increased demand for efficient and safe multi-robot path planning methods. Existing solutions face significant challenges in simultaneously planning paths for multiple robots, maintaining safe distances from obstacles and other robots, and dealing with local minima issues. In this research, an efficient potential field-based method with nature-inspired algorithm is proposed to overcome these limitations, resulting in competent, short, and time-saving paths even in complex environments. Our proposed algorithms consider the simultaneous path planning of robots located at different locations while ensuring a safe distance from other robots. Multi-Robot multi-task allocation method is developed to optimize the path distance and time. A potential field-based scheme is proposed to avoid static and dynamic obstacles effectively. Additionally, our approach solves the local minima problem with simulated annealing algorithm, which is a supplement to this navigator. Finally, the proposed approach is efficient in generating short and time-efficient paths, even in complex dynamic environments. By augmenting the efficiency and safety of multi-robot operations, our work will contribute to the reduction of operational expenses, the enhancement of productivity, and the minimization of accident risks.

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