RPT*: Global Planning with Probabilistic Terminals for Target Search in Complex Environments
Yunpeng Lyu, Chao Cao, Ji Zhang, Howie Choset, Zhongqiang Ren
TL;DR
The paper introduces HPP-PT, a probabilistic-termination variant of the Hamiltonian Path Problem, and presents RPT*—an A*-like planner that reformulates the problem to a Markovian state space to handle history-dependent expected costs. It provides a bounded-suboptimal variant (F-RPT*) with focal search to scale to larger graphs, and proves completeness and optimality guarantees. The hierarchical HATS framework couples RPT*/F-RPT* with perception, mapping, and dual planning modes for lifelong and unknown-environment target search, demonstrating improved average search efficiency and robustness to misleading priors in simulations and real robot experiments. The work shows that the RPT*-based planning balances exploitation and exploration effectively, enabling faster target localization under uncertainty, with practical implications for autonomous search in complex environments. Potential extensions include multi-robot coordination and integrating semantic mapping to derive probabilistic targets from richer priors.
Abstract
Routing problems such as Hamiltonian Path Problem (HPP), seeks a path to visit all the vertices in a graph while minimizing the path cost. This paper studies a variant, HPP with Probabilistic Terminals (HPP-PT), where each vertex has a probability representing the likelihood that the robot's path terminates there, and the objective is to minimize the expected path cost. HPP-PT arises in target object search, where a mobile robot must visit all candidate locations to find an object, and prior knowledge of the object's location is expressed as vertex probabilities. While routing problems have been studied for decades, few of them consider uncertainty as required in this work. The challenge lies not only in optimally ordering the vertices, as in standard HPP, but also in handling history dependency: the expected path cost depends on the order in which vertices were previously visited. This makes many existing methods inefficient or inapplicable. To address the challenge, we propose a search-based approach RPT* with solution optimality guarantees, which leverages dynamic programming in a new state space to bypass the history dependency and novel heuristics to speed up the computation. Building on RPT*, we design a Hierarchical Autonomous Target Search (HATS) system that combines RPT* with either Bayesian filtering for lifelong target search with noisy sensors, or autonomous exploration to find targets in unknown environments. Experiments in both simulation and real robot show that our approach can naturally balance between exploitation and exploration, thereby finding targets more quickly on average than baseline methods.