Tactile Aware Dynamic Obstacle Avoidance in Crowded Environment with Deep Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
Mobile robots operating in crowded environments require the ability to navigate among humans and surrounding obstacles efficiently while adhering to safety standards and socially compliant mannerisms. This scale of the robot navigation problem may be classified as both a local path planning and trajectory optimization problem. This work presents an array of force sensors that act as a tactile layer to complement the use of a LiDAR for the purpose of inducing awareness of contact with any surrounding objects within immediate vicinity of a mobile robot undetected by LiDARs. By incorporating the tactile layer, the robot can take more risks in its movements and possibly go right up to an obstacle or wall, and gently squeeze past it. In addition, we built up a simulation platform via Pybullet which integrates Robot Operating System (ROS) and reinforcement learning (RL) together. A touch-aware neural network model was trained on it to create an RL-based local path planner for dynamic obstacle avoidance. Our proposed method was demonstrated successfully on an omni-directional mobile robot who was able to navigate in a crowded environment with high agility and versatility in movement, while not being overly sensitive to nearby obstacles-not-in-contact.
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 6 of its 26 references that are in the map. First posted on arXiv 19 Jun 2024.
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6 of 26 references.
References · 6
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
- 2017Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learningcited by 23
- 2016Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebocited by 19
- 2017Modeling Cooperative Navigation in Dense Human Crowdscited by 10
- 2018A Comparative Study of Bug Algorithms for Robot Navigationcited by 8
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- 2024Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
- 2025Learning Fast, Tool aware Collision Avoidance for Collaborative Robots
- 2025Improving Robotic Manipulation: Techniques for Object Pose Estimation, Accommodating Positional Uncertainty, and Disassembly Tasks from Examples
- 2025Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles
- 2026RAY-TOLD: Ray-Based Latent Dynamics for Dense Dynamic Obstacle Avoidance with TDMPC