Nuisance-free Automatic Ground Collision Avoidance System Design: Merging Exponential-CBF and Adaptive Sliding Manifolds
The significance of the automatic ground collision avoidance system (Auto-GCAS) has been proven by considering the fatal crashes that have occurred over decades. Even though extensive efforts have been put forth to address the ground collision avoidance in the literature, the notion of being nuisance-free has not been sufficiently addressed. At this point, in this study, the Auto-GCAS design is formulated by merging exponential control barrier functions with sliding manifolds to manipulate the barrier function dynamics. The adaptive properties of the sliding manifolds are tailored to the key and governing flight parameters, ensuring that the nuisance-free requirement is satisfied. Furthermore, to ensure all safety requirements are met, a flight envelope protection algorithm is designed using control barrier functions to assess the commands generated by the Auto-GCAS. Eventually, the performance of the proposed methodology is demonstrated, focusing on authority-sharing, collision avoidance capability, and nuisance-free operation through various scenarios and Monte Carlo simulations.
Code (0)
등록된 구현이 없습니다.
Tasks
Collision AvoidanceSimilar Papers 제목 키워드 기반
An Automatic Ground Collision Avoidance System with Reinforcement Learning
This article evaluates an artificial intelligence (AI)-based Automatic Ground Collision Avoidance System (AGCAS) designed for advanced jet trainers to enhance operational effectiveness. In the continuously evolving field…
Reinforcement LearningCollision AvoidanceSAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning
Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcti…
Collision Avoidancereinforcement-learningReinforcement Learning (RL)Trajectory PlanningAn LGMD Based Competitive Collision Avoidance Strategy for UAV
Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of…
Collision AvoidanceOptimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning
New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations. This paper explores how unmanned free-flight traffic may operate in den…
Collision AvoidanceDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1A Collision-Free Sway Damping Model Predictive Controller for Safe and Reactive Forestry Crane Navigation
Forestry cranes operate in dynamic, unstructured outdoor environments where simultaneous collision avoidance and payload sway control are critical for safe navigation. Existing approaches address these challenges separat…
Collision Avoidance