Resilient Timed Elastic Band Planner for Collision-Free Navigation in Unknown Environments
Geesara Kulathunga, Abdurrahman Yilmaz, Zhuoling Huang, Ibrahim Hroob, Hariharan Arunachalam, Leonardo Guevara, Alexandr Klimchik, Grzegorz Cielniak, Marc Hanheide
TL;DR
The paper addresses robust autonomous navigation in unknown environments by ensuring recovery from infeasible local plans. It proposes the Resilient Timed Elastic Band (RTEB), which augments the Timed Elastic Band with a Hybrid A* recovery planner and a soft-constraint trajectory smoothing driven by a dynamic Voronoi field. The planning problem uses a mobile robot state $\mathbf{x}= [x,y,\theta]^T$ and controls $\mathbf{u}= [\phi,s]^T$, seeking to minimize $J_{total}= J_{obs}+J_{cur}+J_{path}$ where obstacle costs are encoded via $F_v$ and a goal-alignment mechanism leverages an intermediate pose at distance $d_i$ from the goal. It is validated in ROS2 with both simulation and field trials, showing up to around 20% reductions in traverse distance, time, and control effort in obstacle-dense settings, indicating improved reliability and efficiency for field robotics. These results highlight RTEB's practical value for autonomous navigation in unknown, cluttered environments.
Abstract
In autonomous navigation, trajectory replanning, refinement, and control command generation are essential for effective motion planning. This paper presents a resilient approach to trajectory replanning addressing scenarios where the initial planner's solution becomes infeasible. The proposed method incorporates a hybrid A* algorithm to generate feasible trajectories when the primary planner fails and applies a soft constraints-based smoothing technique to refine these trajectories, ensuring continuity, obstacle avoidance, and kinematic feasibility. Obstacle constraints are modelled using a dynamic Voronoi map to improve navigation through narrow passages. This approach enhances the consistency of trajectory planning, speeds up convergence, and meets real-time computational requirements. In environments with around 30\% or higher obstacle density, the ratio of free space before and after placing new obstacles, the Resilient Timed Elastic Band (RTEB) planner achieves approximately 20\% reduction in traverse distance, traverse time, and control effort compared to the Timed Elastic Band (TEB) planner and Nonlinear Model Predictive Control (NMPC) planner. These improvements demonstrate the RTEB planner's potential for application in field robotics, particularly in agricultural and industrial environments, where navigating unstructured terrain is crucial for ensuring efficiency and operational resilience.