Path Planning & SLAM for Real Robots

    1

    Choose, tune and validate robot path planners and 2D/3D SLAM, with simulation benchmarks and ATE/RPE metrics.

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    Path Planning & SLAM for Real Robots

    Example session with this skill installed

    I have a differential drive robot with a 2D Lidar and IMU. I need to set up a SLAM stack for a 5000sqm warehouse and a planner that handles dynamic obstacles. Recommend a stack and metrics.

    • Read your context and instructions
    • Compiled the path planning

    I have analyzed your requirements for a differential drive robot in a warehouse environment. Given the 5000sqm scale and sensor suite, a 2D occupancy grid approach is most efficient.

    Recommended Navigation & SLAM Stack

    ComponentRecommendationReasoning
    SLAM Stackslam_toolbox (Asynchronous)Better suited for large maps than Gmapping; handles loop closure effectively in long corridors.
    Global PlannerD* LiteEfficiently handles re-planning when warehouse layouts change or paths are blocked.
    Local PlannerTEB Local PlannerRespects differential kinematics while accounting for dynamic obstacle avoidance.
    Sensor Fusionrobot_localization (EKF)Fuses wheel odometry, IMU, and Lidar-based scan matching to minimize drift.

    Benchmarking Metrics

    To validate this configuration, we will monitor the following KPIs during simulation runs:

    SLAM ATE (Absolute Trajectory Error): Measured via evo using ground truth or loop-closure consistency. Target: < 0.15m.

    • Planning Latency: p95 response time for global re-planning when a dynamic obstacle blocks the primary route.
    • Path Clearance: Minimum distance maintained from obstacles relative to the robot's 0.6m x 0.8m footprint.

    Success Rate: Percentage of navigation tasks completed without collision or localization loss across 50 randomized scenarios.

    Next steps

    1. Configure slam_toolbox in asynchronous mode to prevent processing lag during the initial 5000sqm mapping run.
    2. Calibrate the Lidar-to-IMU extrinsics to ensure the EKF does not diverge during rapid rotations.
    3. Run a simulation benchmark in a high-density dynamic obstacle scenario to tune the TEB costmap inflation layers.

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    About this skill

    From occupancy grids to loop closure — choose, tune and validate.

    Implement and tune robot path planning and SLAM - grid and sampling planners (A*, D* Lite, Hybrid-A*, RRT*, lattice), trajectory optimisation, and 2D/3D SLAM (slam_toolbox, Cartographer, RTAB-Map, ORB-SLAM3, LIO-SAM/FAST-LIO) - with simulation benchmarks and metrics (ATE/RPE, success rate, path quality). Use when the user needs a robot or drone to plan paths, build maps, localize, or when a planner is slow/unsafe or a map drifts.

    Includes an EXPERT.md reference (exact parameters, thresholds, failure diagnosis).

    Acceptable use: civil and commercial applications only. Any military, paramilitary or weapon-related use is prohibited, as stated in the skill itself.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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