arXiv cs.AI· Vin\'icius Ara\'ujo de Oliveira, Gabriel Ferreira Mendes Louren\c{c}o, Paulo L. J. Drews-Jr, Armando Alves Neto, Douglas G. Macharet·· 3 小时前AI 评分23
Reward-Guided RRT*：面向预算约束任务与运动规划的新方法
Reward-Guided RRT* for Budgeted Task and Motion Planning
AI 导读
论文提出 Reward-Guided RRT*，一种在连续状态空间中直接进行局部任务选择与运动规划联合的采样式框架，无需预计算任务转移图，并通过返回可行性流程保证任务访问不超出任务预算。实验显示其解与 VNS-PRM* 相当，但所需采样状态显著更少。该方法还扩展到 Dubins 转向，并在 CoppeliaSim 的缩比车式机器人平台上验证了对车辆运动学约束的处理能力。
正文 · 原文
Abstract:Robotic missions involving spatially distributed tasks often require jointly selecting rewarding locations and planning feasible motions under limited travel budgets. This paper proposes Reward-Guided RRT*, a new sampling-based framework that constructs a budget-feasible mission through a sequence of locally joint task-selection-and-motion-planning stages, evaluated directly in the continuous state space, without requiring a precomputed task-transition graph. The method incrementally builds a mission using reward-guided RRT* searches, while a return-feasibility procedure ensures that each selected task can be visited without violating the mission budget. Experiments with increasing obstacle complexity and number of tasks show solutions comparable to VNS-PRM* while requiring substantially fewer sampled states. We also extend the framework to Dubins steering and evaluate it in CoppeliaSim with a scaled car-like robotic platform, demonstrating its ability to incorporate vehicle kinematic constraints.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.35289 [cs.RO] |
| (or arXiv:2610.35289v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35289 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Douglas G. Macharet [view email]
[v1]
Mon, 26 Oct 2026 12:00:13 UTC (7,214 KB)
来源：arXiv cs.AI · arxiv.org