mlsanityData to 14 Oct 2026

Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots

arXiv 2508.07941 ↗First posted 11 Aug 2025cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 23 references that are in the map. First posted on arXiv 11 Aug 2025.

Builds on

5 of 23 references.

References · 5

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 118
  3. 2022Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cellscited by 4
  4. 2020Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shapingcited by 2
  5. 2014Distributed Computing by Mobile Robots: Uniform Circle Formationcited by 1

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  1. 2024Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning
  2. 2017Uncertainty-Aware Reinforcement Learning for Collision Avoidance
  3. 2024Neural-Network-Driven Reward Prediction as a Heuristic: Advancing Q-Learning for Mobile Robot Path Planning
  4. 2024Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
  5. 2024Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning
  6. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
  7. 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
  8. 2024Research on Autonomous Robots Navigation based on Reinforcement Learning