PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
Wenhao Yu,Jie Peng,Quecheng Qiu,Hanyu Wang,Lu Zhang,Jianmin Ji,Wenhao Yu,Jie Peng,Quecheng Qiu,Hanyu Wang,Lu Zhang,Jianmin Ji
Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation methods primarily focus on training a policy that directly commands the robot with low-level controls, like linear and angular velocities, which leads to unstable speeds and unsmooth trajectories of the robot during the l...
