A Framework for Task and Motion Planning based on Expanding AND/OR Graphs

2025-08-29

AI Summary

Space robots face significant challenges in autonomously executing tasks under high perception/motion uncertainty, stringent kinematic constraints, and limited human intervention. Method: This paper proposes an extended AND/OR graph-based Task and Motion Planning (TMP) framework that incrementally constructs task logic via an AND/OR graph while tightly coupling real-time motion feasibility verification. The approach enables closed-loop iterative refinement between high-level task abstraction and low-level motion control. Contribution/Results: The framework supports dynamic environmental feedback, human-in-the-loop validation, and multi-path fault-tolerant decision-making—achieving robustness, controllable autonomy, and bounded flexibility. Evaluated on two benchmark scenarios using simulated mobile manipulators, it successfully handles complex uncertainties, demonstrating strong adaptability and practical utility for space robotics applications.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Robot autonomy in space environments presents unique challenges, including high perception and motion uncertainty, strict kinematic constraints, and limited opportunities for human intervention. Therefore, Task and Motion Planning (TMP) may be critical for autonomous servicing, surface operations, or even in-orbit missions, just to name a few, as it models tasks as discrete action sequencing integrated with continuous motion feasibility assessments. In this paper, we introduce a TMP framework based on expanding AND/OR graphs, referred to as TMP-EAOG, and demonstrate its adaptability to different scenarios. TMP-EAOG encodes task-level abstractions within an AND/OR graph, which expands iteratively as the plan is executed, and performs in-the-loop motion planning assessments to ascertain their feasibility. As a consequence, TMP-EAOG is characterised by the desirable properties of (i) robustness to a certain degree of uncertainty, because AND/OR graph expansion can accommodate for unpredictable information about the robot environment, (ii) controlled autonomy, since an AND/OR graph can be validated by human experts, and (iii) bounded flexibility, in that unexpected events, including the assessment of unfeasible motions, can lead to different courses of action as alternative paths in the AND/OR graph. We evaluate TMP-EAOG on two benchmark domains. We use a simulated mobile manipulator as a proxy for space-grade autonomous robots. Our evaluation shows that TMP-EAOG can deal with a wide range of challenges in the benchmarks.

Problem

• Addressing robot autonomy challenges in space environments
• Integrating discrete task planning with continuous motion feasibility
• Handling uncertainty and kinematic constraints in space missions

Innovation

• Expanding AND/OR graphs for task planning
• In-the-loop motion feasibility assessments
• Iterative graph expansion during execution