在语义不确定环境中，实现任务与运动规划一体化的高效算法。
Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty
- 将语义不确定性建模为部分可观测随机混合系统
- 算法在不确定环境下持续优于基线方法
- 适合需要动态决策的机器人任务规划场景
本文解决不确定环境中集成任务与动力学运动规划的问题。考虑一个具有非线性动力学的机器人，在部分可观测环境中执行有限轨迹上的线性时序逻辑（LTLf）规范。环境状态变量存在语义标签不确定性。我们将问题建模为部分可观测随机混合系统，融合机器人动力学、LTLf 任务与环境状态不确定性。提出一种任意时算法，利用混合系统的结构，结合决策方法与采样式运动规划的优势。证明了算法的正确性与渐近最优性。实验表明该算法在不确定环境中有效，且持续优于基线方法。
原文摘要 · Abstract (English)
This paper tackles the problem of integrated task and kinodynamic motion planning in uncertain environments. We consider a robot with nonlinear dynamics tasked with a Linear Temporal Logic over finite traces ($\ltlf$) specification operating in a partially observable environment. Specifically, the uncertainty is in the semantic labels of the environment. We show how the problem can be modeled as a Partially Observable Stochastic Hybrid System that captures the robot dynamics, $\ltlf$ task, and uncertainty in the environment state variables. We propose an anytime algorithm that takes advantage of the structure of the hybrid system, and combines the effectiveness of decision-making techniques and sampling-based motion planning. We prove the soundness and asymptotic optimality of the algorithm. Results show the efficacy of our algorithm in uncertain environments, and that it consistently outperforms baseline methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。