Reinforcement learning

This paper presents a novel Extended Hybrid Reciprocal Velocity Obstacle (EHRVO) algorithm for multi-agent collision avoidance that incorporates linked agent constraints. The proposed approach introduces proximity constraints between paired gents while adapting the collision avoidance geometry to maintain these relationships. This research work extends the HRVO framework to create a hierarchical set of constraints that prioritize maintenance of spatial consistent agent pairs or triads while ensuring collision-free trajectories. .

This study introduces a novel trajectory planning approach for the transportation of cable-suspended loads employing three quadrotors, relying on a reinforcement learning (RL) algorithm. The primary objective of this path planning method is to transport the cargo smoothly while avoiding its swing.