Sampling-based Task and Kinodynamic Motion Planning under Semantic Uncertainty
This solves the problem of robust robot planning in uncertain environments for robotics applications, representing an incremental improvement by combining decision-making and sampling-based methods.
The paper addresses integrated task and kinodynamic motion planning for robots with nonlinear dynamics under semantic label uncertainty, modeled as a Partially Observable Stochastic Hybrid System, and proposes an anytime algorithm proven sound and asymptotically optimal, showing consistent outperformance over baselines.
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.