Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots
This addresses collision avoidance for mobile robots in constrained settings without communication, but it is incremental as it builds on existing DRL methods with a novel reward modulation.
The paper tackled collision avoidance for mobile robots by using an LSTM to predict agent positions and dynamically modulating DQN rewards, resulting in a significant decrease in collisions and improved stability in constrained environments.
This article proposes a collision risk anticipation method based on short-term prediction of the agents position. A Long Short-Term Memory (LSTM) model, trained on past trajectories, is used to estimate the next position of each robot. This prediction allows us to define an anticipated collision risk by dynamically modulating the reward of a Deep Q-Learning Network (DQN) agent. The approach is tested in a constrained environment, where two robots move without communication or identifiers. Despite a limited sampling frequency (1 Hz), the results show a significant decrease of the collisions number and a stability improvement. The proposed method, which is computationally inexpensive, appears particularly attractive for implementation on embedded systems.