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.

翻译：在拥挤环境中运行的移动机器人需要能够在人类和周围障碍物之间高效导航，同时遵守安全标准和社会合规行为规范。机器人导航问题的这一层面可归类为局部路径规划和轨迹优化问题。本研究提出了一种力传感器阵列，作为触觉感知层来补充激光雷达的使用，旨在增强对激光雷达未检测到的、移动机器人紧邻范围内与任何周围物体接触的感知能力。通过集成触觉感知层，机器人可以在移动中承担更多风险，甚至可以紧贴障碍物或墙壁，并轻柔地挤过狭窄空间。此外，我们通过Pybullet构建了一个集成机器人操作系统（ROS）与强化学习（RL）的仿真平台。在该平台上训练了一个触觉感知神经网络模型，以创建基于强化学习的动态避障局部路径规划器。我们提出的方法在全向移动机器人上成功验证，该机器人能够在拥挤环境中以高敏捷性和运动多样性进行导航，同时对未接触的邻近障碍物不过度敏感。