11 citations · 12 across the 5 of their papers we have counts for
5 papers
ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments
Jinghao Xin, Zhichao Liang, Zihuan Zhang +2
Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic…
OkayPlan: Obstacle Kinematics Augmented Dynamic Real-time Path Planning via Particle Swarm Optimization
Jinghao Xin, Jinwoo Kim, Shengjia Chu +1
Existing Global Path Planning (GPP) algorithms predominantly presume planning in static environments. This assumption immensely limits their applications to Unmanned Surface Vehicl…
Efficient Real-time Path Planning with Self-evolving Particle Swarm Optimization in Dynamic Scenarios
Jinghao Xin, Zhi Li, Yang Zhang +1
Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precl…
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
Jinghao Xin, Jinwoo Kim, Zhi Li +1
Deep Reinforcement Learning (DRL) has exhibited efficacy in resolving the Local Path Planning (LPP) problem. However, such application in the real world is immensely limited due to…
Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning
Zhi Li, Jinghao Xin, Ning Li
Deep reinforcement learning (DRL) has been widely applied in autonomous exploration and mapping tasks, but often struggles with the challenges of sampling efficiency, poor adaptabi…