Technology Category
Intelligent Robots: Motion and Path PlanningPlanning, Routing, and Scheduling: Replanning and Plan RepairNatural Language Processing: Safety and Robustness
Point Cloud-Based Control Barrier Functions for Model Predictive Control in Safety-Critical Navigation of Autonomous Mobile Robots
🤖 AI Summary
To address the challenge of joint avoidance of dynamic and static obstacles in safety-critical navigation, this paper proposes a real-time motion planning framework based on LiDAR point clouds. Methodologically, it integrates dynamic obstacle tracking and mapping: point cloud segmentation and Kalman filtering jointly estimate and predict dynamic object states; these predictions are innovatively fused with static environment geometry to construct a forward-looking spatiotemporal occupancy map. Real-time collision checking over this map enables the synthesis of Control Barrier Function (CBF)-based safety constraints, which are embedded into a Nonlinear Model Predictive Control (NMPC) formulation for optimal trajectory generation. Experiments in both simulation and real-world deployments demonstrate significant improvements in obstacle avoidance safety and robustness over state-of-the-art baselines. The implementation is open-sourced.
Technology Category
Application Category
📝 Abstract
In this work, we propose a novel motion planning algorithm to facilitate safety-critical navigation for autonomous mobile robots. The proposed algorithm integrates a real-time dynamic obstacle tracking and mapping system that categorizes point clouds into dynamic and static components. For dynamic point clouds, the Kalman filter is employed to estimate and predict their motion states. Based on these predictions, we extrapolate the future states of dynamic point clouds, which are subsequently merged with static point clouds to construct the forward-time-domain (FTD) map. By combining control barrier functions (CBFs) with nonlinear model predictive control, the proposed algorithm enables the robot to effectively avoid both static and dynamic obstacles. The CBF constraints are formulated based on risk points identified through collision detection between the predicted future states and the FTD map. Experimental results from both simulated and real-world scenarios demonstrate the efficacy of the proposed algorithm in complex environments. In simulation experiments, the proposed algorithm is compared with two baseline approaches, showing superior performance in terms of safety and robustness in obstacle avoidance. The source code is released for the reference of the robotics community.
Problem
Research questions and friction points this paper is trying to address.
Ensuring autonomous robot safety in dynamic environments
Integrating real-time obstacle tracking with predictive control
Avoiding both static and dynamic obstacles using CBF constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates real-time dynamic obstacle tracking and mapping
Uses Kalman filter to predict dynamic point cloud motion
Combines control barrier functions with nonlinear model predictive control
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