Genetic Algorithm-Based Optimization of Velocity Profiles for Multi-Robot Collision Avoidance

Efficient coordination of multiple mobile robots is essential in automated systems, especially when robots must follow predefined paths while avoiding collisions. This paper proposes a centralized optimization framework using Genetic Algorithms to optimize the velocity profiles of a system of robots without altering their paths. The goal is to minimize task completion time and energy consumption while ensuring collision avoidance. Three Genetic Algorithm-based methods are introduced: Maximum Velocity Optimization, Slow-Down Segment Single-Objective Optimization and Slow-Down Segment Multi-Objective Optimization. The first method adjusts each robot’s maximum velocity along its entire path, whereas the second introduces a slow-down segment only at the start of its path. While these two approaches only optimize task completion time, the third method contains a multi-objective formulation, producing solutions that balance time and energy. Methods such as Brute-Force and Prioritized Planning were used as baseline methods for comparison. Simulation results indicate that the proposed strategies significantly outperform the baseline methods. Furthermore, the second method achieves better results than the first by introducing more targeted velocity adjustments, while the third further enhances flexibility by offering a range of trade-offs between task completion time and energy consumption. Scalability and computational cost remain critical challenges, especially as the number of robots increases.