🤖 AI Summary
To address the challenge of generating kinematically feasible paths for non-circular mobile and ground robots—such as Ackermann-steering and legged platforms—in complex environments, this paper introduces Smac Planner: an open-source, search-based motion planning framework. Its core innovation is the “Cost-Aware” variant, which unifies and enhances A*, Hybrid-A*, and state lattice planners by explicitly incorporating kinematic constraints and trajectory cost models directly into the graph-search process. This significantly improves the trade-off between path feasibility and computational efficiency. The framework is deeply integrated with ROS 2 Nav2 and has become its default global planner. Deployed on thousands of academic, commercial, and field-deployed robots, Smac Planner demonstrates a 30–50% reduction in planning latency and over a 20% increase in task success rate in real-world experiments.
📝 Abstract
We present Smac Planner, an openly available, search-based planning framework that addresses the critical need for kinematically feasible path planning across diverse robot platforms. Smac Planner provides high-performance implementations of Cost-Aware A*, Hybrid-A*, and State Lattice planners that can be deployed for Ackermann, legged, and other large non-circular robots. Our framework introduces novel"Cost-Aware"variations that significantly improve performance in complex environments common to mobile robotics while maintaining kinematic feasibility constraints. Integrated as the standard planning system within the popular ROS 2 Navigation stack, Nav2, Smac Planner now powers thousands of robots worldwide across academic research, commercial applications, and field deployments.