Open-Source, Cost-Aware Kinematically Feasible Planning for Mobile and Surface Robotics

TL;DR

The paper introduces Smac Planner, an open-source, cost-aware, search-based path planning framework that ensures kinematic feasibility for various robot types, significantly improving performance in complex environments.

Contribution

It presents a novel cost-aware planning framework integrated into ROS 2, supporting diverse robot platforms with high-performance, kinematically feasible planning algorithms.

Findings

Successfully deployed on thousands of robots worldwide.

Significantly improved planning efficiency in complex environments.

Supports multiple robot types with a unified framework.

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.