Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners

2025-10-23

AI Summary

Real-time robot replanning in dynamic environments incurs high computational overhead and relies heavily on explicit change detection and graph updates. Method: This paper proposes a novel incremental planning paradigm that eliminates the need for explicit reuse or update of historical paths. It decouples dynamic replanning into a sequence of independent, asymptotically optimal sampling-based planning problems—thereby avoiding dependence on obstacle change perception and dense graph reconstruction. The approach leverages almost-surely asymptotically optimal algorithms, including Effort-Informed Trees* (EIT*) and Asymptotically Optimal RRT-Connect (AORRTC), to balance rapid initial solution generation with continuous path refinement. Contribution/Results: Simulation results show that EIT*-generated paths achieve significantly shorter median lengths than those produced by mainstream reactive planners. Physical experiments on a robotic manipulator demonstrate AORRTC’s effectiveness and robustness in complex, dynamic task scenarios.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Robots operating in changing environments either predict obstacle changes and/or plan quickly enough to react to them. Predictive approaches require a strong prior about the position and motion of obstacles. Reactive approaches require no assumptions about their environment but must replan quickly and find high-quality paths to navigate effectively. Reactive approaches often reuse information between queries to reduce planning cost. These techniques are conceptually sound but updating dense planning graphs when information changes can be computationally prohibitive. It can also require significant effort to detect the changes in some applications. This paper revisits the long-held assumption that reactive replanning requires updating existing plans. It shows that the incremental planning problem can alternatively be solved more efficiently as a series of independent problems using fast almost-surely asymptotically optimal (ASAO) planning algorithms. These ASAO algorithms quickly find an initial solution and converge towards an optimal solution which allows them to find consistent global plans in the presence of changing obstacles without requiring explicit plan reuse. This is demonstrated with simulated experiments where Effort Informed Trees (EIT*) finds shorter median solution paths than the tested reactive planning algorithms and is further validated using Asymptotically Optimal RRT-Connect (AORRTC) on a real-world planning problem on a robot arm.

Problem

• Developing real-time reactive planning for dynamic robot environments
• Eliminating computational costs of updating dense planning graphs
• Achieving optimal paths without explicit plan reuse requirements

Innovation

• Fast ASAO planners solve incremental planning independently
• EIT* algorithm finds shorter paths than reactive planners
• AORRTC validates approach on real-world robot arm