Safety-Critical Motion Planning and Control in Autonomous Systems

The field of autonomous systems is moving towards developing more sophisticated safety-critical motion planning and control algorithms. Recent developments focus on ensuring collision-free navigation, optimal path planning, and robust control in complex environments. One of the key directions is the integration of control barrier functions and artificial potential fields to guarantee safety and efficiency. Another important area of research is the development of adaptive and dynamic shielding techniques that can handle changing safety specifications and operating conditions. These advancements have the potential to significantly enhance the capability of autonomous systems to operate safely and efficiently in real-world scenarios. Noteworthy papers in this area include: The paper 'Navigating Polytopes with Safety: A Control Barrier Function Approach' which proposes a systematic method for generating control barrier function candidates for collision-free navigation. The paper 'Efficient Dynamic Shielding for Parametric Safety Specifications' which introduces dynamic shields for parametric safety specifications, enabling fast adaptation to changing safety requirements.

Sources

Navigating Polytopes with Safety: A Control Barrier Function Approach

Autonomous Circular Drift Control for 4WD-4WS Vehicles Without Precomputed Drifting Equilibrium

Enhanced SIRRT*: A Structure-Aware RRT* for 2D Path Planning with Hybrid Smoothing and Bidirectional Rewiring

Efficient Dynamic Shielding for Parametric Safety Specifications

Enhancing Lifelong Multi-Agent Path-finding by Using Artificial Potential Fields

Redundancy Parameterization of the ABB YuMi Robot Arm

UPP: Unified Path Planner with Adaptive Safety and Optimality

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