STITCHER: Real-Time Trajectory Planning with Motion Primitive Search
🤖 AI Summary
To address the stringent real-time, safety, and dynamic feasibility requirements of high-speed autonomous navigation in large-scale, complex environments, this paper proposes a non-optimization-based graph-search and trajectory-stitching framework. The method constructs a state graph from a predefined motion primitive library and integrates heuristic graph search, trajectory stitching, smoothing, and multi-constraint feasibility verification—including state, actuator, and collision constraints—thereby avoiding the computational overhead of numerical optimization. It achieves millisecond-level long-horizon trajectory generation in complex scenes spanning tens of meters, with guaranteed dynamic feasibility, collision-free execution, and full-state constraint satisfaction. Compared to two state-of-the-art optimization-based planners, our approach demonstrates significant improvements in real-time performance, robustness, and computational efficiency. This work establishes a new paradigm for highly reliable, real-time motion planning for agile mobile robots.
Application Category
📝 Abstract
Autonomous high-speed navigation through large, complex environments requires real-time generation of agile trajectories that are dynamically feasible, collision-free, and satisfy state or actuator constraints. Most modern trajectory planning techniques rely on numerical optimization because high-quality, expressive trajectories that satisfy various constraints can be systematically computed. However, meeting computation time constraints and the potential for numerical instabilities can limit the use of optimization-based planners in safety-critical scenarios. This work presents an optimization-free planning framework that stitches short trajectory segments together with graph search to compute long range, expressive, and near-optimal trajectories in real-time. Our STITCHER algorithm is shown to outperform modern optimization-based planners through our innovative planning architecture and several algorithmic developments that make real-time planning possible. Extensive simulation testing is conducted to analyze the algorithmic components that make up STITCHER, and a thorough comparison with two state-of-the-art optimization planners is performed. It is shown STITCHER can generate trajectories through complex environments over long distances (tens of meters) with low computation times (milliseconds).
Problem
Research questions and friction points this paper is trying to address.
Robot Path Planning
Real-time Mobility
Complex Environment
Innovation
Methods, ideas, or system contributions that make the work stand out.
STITCHER
Instantaneous Path Planning
Complex Environment
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