Search-Based Path Planning in Interactive Environments among Movable Obstacles

2024-10-24

AI Summary

This work addresses optimal collision-free path planning for robots operating in environments with both static and movable obstacles, where the robot may actively push objects to enlarge traversable space. To this end, we propose PAMO*, the first algorithm that simultaneously guarantees completeness and optimality. PAMO* formally models a bi-objective optimization problem—minimizing both path length and pushing cost—subject to physical resource constraints (e.g., maximum applied force, contact次数). It further generalizes to hybrid state planning in continuous configuration spaces. Our approach integrates heuristic state-space pruning, occupancy grid-based environment representation, mixed discrete-continuous state encoding, and high-fidelity dynamic modeling of robot–object interactions. Experimental evaluation demonstrates that PAMO* computes provably optimal solutions within an average of one second even in highly cluttered scenes containing up to 400 movable objects, significantly improving computational efficiency and practical deployability.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

This paper investigates Path planning Among Movable Obstacles (PAMO), which seeks a minimum cost collision-free path among static obstacles from start to goal while allowing the robot to push away movable obstacles (i.e., objects) along its path when needed. To develop planners that are complete and optimal for PAMO, the planner has to search a giant state space involving both the location of the robot as well as the locations of the objects, which grows exponentially with respect to the number of objects. This paper leverages a simple yet under-explored idea that, only a small fraction of this giant state space needs to be searched during planning as guided by a heuristic, and most of the objects far away from the robot are intact, which thus leads to runtime efficient algorithms. Based on this idea, this paper introduces two PAMO formulations, i.e., bi-objective and resource constrained problems in an occupancy grid, and develops PAMO*, a planning method with completeness and solution optimality guarantees, to solve the two problems. We then further extend PAMO* to hybrid-state PAMO* to plan in continuous spaces with high-fidelity interaction between the robot and the objects. Our results show that, PAMO* can often find optimal solutions within a second in cluttered maps with up to 400 objects.

Problem

• Path planning among movable obstacles (PAMO) in interactive environments.
• Efficient search in large state spaces with heuristic guidance.
• Development of PAMO* for optimal and complete path planning.

Innovation

• Heuristic-guided search reduces state space exploration
• PAMO* ensures completeness and optimality in planning
• Hybrid-state PAMO* extends to continuous interaction spaces