用LSTM预测机器人位置，提前预警碰撞并优化强化学习决策。
Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots
- 用LSTM预测机器人下一位置，实现碰撞风险前瞻感知。
- 在1Hz采样下碰撞数显著减少，系统运行更稳定。
- 计算开销小，适合嵌入式设备部署，无需通信或标识。
本文提出一种基于短期轨迹预测的碰撞风险前瞻方法。利用训练于历史轨迹的长短期记忆（LSTM）模型，估算每个机器人的下一个位置，据此动态调节深度Q网络（DQN）的奖励信号，实现对潜在碰撞的提前感知。该方法在无通信、无标识符的受限环境中测试，尽管采样频率仅为1 Hz，仍显著降低碰撞次数并提升系统稳定性。所提方法计算成本低，特别适合嵌入式系统部署。
原文摘要 · Abstract (English)
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.
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