Mitigating Imminent Collision for Multi-robot Navigation: A TTC-force Reward Shaping Approach

We study the distributed multi-robot navigation problem, which refers to a group of mobile robots avoiding collision with each other while navigating from their start positions to the goal positions. Existing works still suffer from two limitations: 1) accurately quantify the risk of collisions for heterogeneous robots and 2) effectively capture the state representation under dynamic environments. These limitations make the heterogeneous robots prone to collisions in high-density and dynamic environments. This work proposes a new time-to-collision force (TTC-force) reward shaping approach, termed Tfresh, incorporating reinforcement learning to learn a policy that adaptively chooses the optimal actions to mitigate the imminent collision. Specifically, we use TTC-force to quantify the risk of each robot exerted by its neighbors and shape the reward signal with TTC-force in applying the reinforcement learning scheme. Meanwhile, we design the spatial attention mechanism involving the dynamic adjacent matrix to capture the state representation effectively. We evaluate the learned policy in numerous simulated scenarios in which groups of mobile robots perform navigation tasks. The experimental results demonstrate that our approach outperforms the state-of-the-art methods regarding success rate, travel distance, and travel time.