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
This paper introduces a resilient trajectory planning method that combines hybrid A*, soft constraints smoothing, and dynamic Voronoi maps to improve collision-free navigation in unknown, obstacle-dense environments, ensuring real-time performance.
Contribution
The paper proposes a novel resilient trajectory replanning approach integrating hybrid A*, soft constraints smoothing, and dynamic Voronoi maps for improved navigation in complex environments.
Findings
20% reduction in traverse distance and time in high obstacle density environments
Enhanced trajectory planning consistency and convergence speed
Effective real-time navigation in unstructured terrains
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…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Control and Dynamics of Mobile Robots · Robotics and Automated Systems
