Problem
Research questions and friction points this paper is trying to address.
Multi-Agent Autonomous Aerial Vehicles
Collision Avoidance
Adversarial Environments
Safety Protocols
A Temporal Barrier Framework for Collision Avoidance in Multi-Agent Autonomous Aerial Vehicles
🤖 AI Summary
This study addresses the safety-efficiency imbalance in multi-UAV close-proximity flight within dynamic uncertain environments by proposing an adversarial time-to-collision risk metric embedded within a Control Barrier Function framework. A differentiable neural network surrogate is employed to enable real-time computation, allowing agents to predictively modulate velocity based on temporal risk for proactive collision avoidance. Experimental results demonstrate that, compared to traditional distance-based baselines, this approach significantly enhances mission efficiency while maintaining safety guarantees. Specifically, it achieves a twofold increase in waypoint progression and a 50% reduction in collision rates, effectively facilitating safe and efficient cooperative flight in dynamic settings.
📝 Abstract
Operating teams of autonomous aircraft in dynamic, uncertain, and potentially adversarial environments requires safety protocols that are reliable yet selective, and allow agents to fly in close proximity while making progress toward mission objectives. We introduce adversarial time-to-collision (aTTC), a risk metric that quantifies, for a given agent, how quickly any surrounding agent could reach it assuming adversarial intent. We embed aTTC into the control barrier function (CBF) framework, defining the barrier directly in time rather than distance or velocity. The resulting aTTC-CBF is inherently anticipatory: agents modulate their own velocity based not on whether a peer is on a collision course, but on how quickly one could reach collision given its dynamical constraints. A differentiable neural-network surrogate makes the aTTC computable in real time within a standard CBF quadratic program. Across long time-horizon simulations of 3D independent-pursuit and formation-flight scenarios, the aTTC-CBF achieves up to twice the waypoint progress at half the collision rate of a higher-order distance-based CBF baseline.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adversarial Time-to-Collision
Control Barrier Function
Differentiable Neural Network Surrogate
Multi-Agent Collision Avoidance
Real-Time Quadratic Programming
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