Optimizing Collision Avoidance in Dynamic Multi-Robot Systems: A Velocity Obstacle and BB-PSO Approach with Priority Consideration Academic Article in Scopus uri icon

abstract

  • This study proposes integrating Reciprocal Velocity Obstacles (RVO) with Bare Bones Particle Swarm Opti-mization (BB-PSO) for prioritized motion planning in multi-robot systems. BB-PSO was chosen because it has fewer parameters to tune, reduced computational complexity, and provides potentially faster convergence compared to standard PSO. The methodology enables collision avoidance and path planning while allowing differentiated robot behaviors based on priority levels. Simulations used a two-phase experimental strategy: first, tuning cost function parameters through grid search, and second, evaluating various priority configurations and random scenarios. Results show that the selected weight configuration (¿ = 4, ß = 2) balances goal-seeking and obstacle avoidance, enabling high-priority agents to move directly while ensuring overall group safety. Scenarios with higher average priorities exhibited shorter travel distances and faster completion times, whereas those with lower or imbalanced priorities led to more conservative behavior and delays. Com-pared to a greedy baseline, the proposed method significantly reduced collisions, achieving an average of 1.0 collision per scenario versus 6.6 with the greedy approach. Some priority configurations achieved complete task fulfillment without any collisions, highlighting the potential for optimized multi-robot coordination. The proposed method offers a promising strategy for prioritized motion planning, balancing efficiency and safety based on task importance. Future research includes comparing BB-PSO with other optimization methods, reducing sample requirements, dynamically adjusting priorities, and extending the model to incorporate task parameterizations and autonomous priority adaptation. © 2025 by SCITEPRESS¿Science and Technology Publications, Lda.

publication date

  • January 1, 2025