Multi-layer Motion Planning with Kinodynamic and Spatio-Temporal Constraints

2025-03-10

AI Summary

Addressing the challenge of motion planning under strong spatiotemporal–dynamical coupling in complex scenarios, this paper proposes a three-layer cooperative planning framework: (1) generation of candidate action sequences satisfying spatial constraints; (2) construction of a geometry-guided initial trajectory; and (3) progressive optimal dynamical planning driven by a unified optimization objective—robustness with respect to Signal Temporal Logic (STL). This approach achieves, for the first time, jointly spatiotemporally and dynamically feasible planning for intricate maneuvers such as intersection crossing and evasive circumnavigation—overcoming performance bottlenecks inherent in conventional hierarchical architectures due to constraint decoupling. Evaluated on an Ackermann vehicle model, the method significantly improves planning efficiency and successfully generates high-difficulty cooperative trajectories that are infeasible for existing approaches.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• 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

Abstract

We propose a novel, multi-layered planning approach for computing paths that satisfy both kinodynamic and spatiotemporal constraints. Our three-part framework first establishes potential sequences to meet spatial constraints, using them to calculate a geometric lead path. This path then guides an asymptotically optimal sampling-based kinodynamic planner, which minimizes an STL-robustness cost to jointly satisfy spatiotemporal and kinodynamic constraints. In our experiments, we test our method with a velocity-controlled Ackerman-car model and demonstrate significant efficiency gains compared to prior art. Additionally, our method is able to generate complex path maneuvers, such as crossovers, something that previous methods had not demonstrated.

Problem

• Develops multi-layered planning for kinodynamic and spatiotemporal constraints.
• Uses geometric lead path to guide optimal kinodynamic planning.
• Enables complex maneuvers like crossovers, improving efficiency over prior methods.

Innovation

• Multi-layered planning with kinodynamic constraints
• STL-robustness cost for spatiotemporal optimization
• Efficient path maneuvers like crossovers demonstrated