Open-Source, Cost-Aware Kinematically Feasible Planning for Mobile and Surface Robotics (2401.13078v1)

Abstract: This paper introduces the Smac Planner, an openly available search-based planning framework with multiple algorithm implementations including 2D-A*, Hybrid-A*, and State Lattice planners. This work is motivated by the lack of performant and available feasible planners for mobile and surface robotics research. This paper contains three main contributions. First, it briefly describes a minimal open-source software framework where search-based planners may be easily added. Further, this paper characterizes new variations on the feasible planners - dubbed Cost-Aware - specific to mobile roboticist's needs. This fills the gap of missing kinematically feasible implementations suitable for academic, extension, and deployed use. Finally, we provide baseline benchmarking against other standard planning frameworks. Smac Planner has further significance by becoming the standard open-source planning system within ROS 2's Nav2 framework which powers thousands of robots in research and industry.

• The paper presents the Smac Planner, an open-source framework implementing 2D-A*, Hybrid-A*, and State Lattice planners to enhance robotic path planning.
• It introduces cost-aware variations that use environmental cost maps to optimize trajectories while ensuring kinematic feasibility.
• Benchmark results demonstrate increased efficiency with shorter computation times, supporting integration with ROS 2’s Nav2 framework for versatile robotic applications.

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

The paper in discussion presents the Smac Planner, an open-source framework providing a suite of search-based planning algorithms tailored to mobile and surface robotics. Addressing the limitations of available solutions in navigation research, the authors introduce implementations of 2D-A*, Hybrid-A*, and State Lattice planners, emphasizing kinematic feasibility and cost-awareness.

Contributions

The paper contributes significantly to the field through three primary avenues:

1. Open-Source Framework: The Smac Planner framework is designed to facilitate the integration and implementation of search-based planners. Significantly, its architecture allows developers to introduce performant planners with minimal code overhead, fostering extensibility and customization.
2. Cost-Aware Variations: The authors pioneer cost-aware variations of feasible planners to enhance path planning according to specific robotic needs. This approach incorporates environmental contexts such as cost maps or grids, which influence the planner’s decision-making, a departure from traditional methods focusing solely on obstacle avoidance.
3. Benchmarking and Performance: Providing comparative analysis, the paper benchmarks Smac Planner against standard planning frameworks, offering evidence of its efficiency and responsiveness. These evaluations underscore the potential of Smac Planners to perform proficiently across different environments, notably highlighting their shorter path computation times and enhanced feasibility.

Technical Insights

The Smac Planner operates on a templated A* search methodology, which efficiently abstracts the variances in graph types and search heuristics. Each planner’s unique functionalities are realized through a node-based template that handles collision states, traversal costs, and neighbor selection policies.

A notable innovation is the Cost-Aware Obstacle Heuristic, which steers planners away from high-cost areas by utilizing soft constraints embedded within cost maps. This method effectively balances path cost optimization with path length and amplitude of unnecessary maneuvers, mitigating the need for extensive path smoothing.

Further, penalty functions for traversal, change in direction, and reverse movements add flexibility to the planner, aligning its operations with the nuanced behaviors expected in mobile robotic applications. The adaptation of motion primitives to grid resolution enables the Smac Planner to maintain feasibility across diverse robotic platforms, from warehouse units to large non-circular and legged robots.

Implications and Future Directions

The introduction of Smac Planner into ROS 2's Nav2 framework is a significant stride, facilitating broader adoption in both research and industry. This accessibility is likely to accelerate advancements in robotic navigation, particularly in sectors demanding kinematic compatibility with dynamic and constrained environments.

Future work might focus on refining heuristic calculations and optimizing penalty functions to better handle complex obstacle-laden environments. Additionally, expanding the framework to integrate with other motion planning libraries could enhance its versatility and applicability across new robotic domains.

Conclusion

The Smac Planner offers a robust, extensible solution to the constraints present in current robotic path planning frameworks. By prioritizing kinematic feasibility and cost-awareness, this initiative not only strengthens existing methodologies but also lays a foundation for future innovations in mobile and surface robotics planning.