Augmenting GRIPS with Heuristic Sampling for Planning Feasible Trajectories of a Car-Like Robot
Kinodynamic motion planning for non-holomonic mobile robots is a challenging problem that is lacking a universal solution. One of the computationally efficient ways to solve it is to build a geometric path first and then transform this path into a kinematically feasible one. Gradient-informed Path Smoothing (GRIPS) is a recently introduced method for such transformation. GRIPS iteratively deforms the path and adds/deletes the waypoints while trying to connect each consecutive pair of them via the provided steering function that respects the kinematic constraints. The algorithm is relatively fast but, unfortunately, does not provide any guarantees that it will succeed. In practice, it often fails to produce feasible trajectories for car-like robots with large turning radius. In this work, we introduce a range of modifications that are aimed at increasing the success rate of GRIPS for car-like robots. The main enhancement is adding the additional step that heuristically samples waypoints along the bottleneck parts of the geometric paths (such as sharp turns). The results of the experimental evaluation provide a clear evidence that the success rate of the suggested algorithm is up to 40% higher compared to the original GRIPS and hits the bar of 90%, while its runtime is lower.
Code (0)
등록된 구현이 없습니다.
Tasks
Motion PlanningSimilar Papers 제목 키워드 기반
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models
Providing natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting. Recent work has aimed to improve such prompts via manual rewriti…
Generative Adversarial Network based Heuristics for Sampling-based Path Planning
Sampling-based path planning is a popular methodology for robot path planning. With a uniform sampling strategy to explore the state space, a feasible path can be found without the complex geometric modeling of the confi…
Generative Adversarial NetworkConformalized Non-uniform Sampling Strategies for Accelerated Sampling-based Motion Planning
Sampling-based motion planners (SBMPs) are widely used to compute dynamically feasible robot paths. However, their reliance on uniform sampling often leads to poor efficiency and slow planning in complex environments. We…
Motion PlanningDirection Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Informed Trees (EIT*), employ effort heurist…
Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uniform or heuristic sampling often produce…
Reinforcement LearningAutonomous VehiclesAutonomous DrivingMotion Planning