The work contained in this paper concerns a novel approach to the n-vehicle collision avoidance problem. The vehicle model used here allows for three-dimensional movement and represents a wide range of vehicles. The algorithm works in conjunction with any desired controller to guarantee all vehicles remain free of collisions while attempting to follow their desired control. This algorithm is reactive and distributed, making it well suited for real time applications, and explicitly accounts for actuation limits. A robustness analysis is presented which provides a means to account for delays and unmodeled dynamics. Robustness to an adversarial vehicle is also presented. Results are demonstrated in simulation.

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Distributed reactive collision avoidance

This paper presents an integrated approach for trajectory generation and time coordination for multiple vehicles, combined with collision avoidance. Pythagorean Hodograph Bezier curves are used to generate desired feasible trajectories that satisfy the dynamic constraints of the vehicles, and guarantee spatial separation between the paths for safe operation. Time coordination ensures that the temporal requirements of the mission are met. Collision avoidance with dynamic obstacles is achieved by properly adjusting the speed profiles of the vehicles along the corresponding paths. A simulation scenario is presented where three quadrotors are coordinating their positions along pre-computed paths, while at the same time avoiding moving objects. The results show that the integrated approach achieves time coordination along feasible paths, and that the collision avoidance algorithm allows for safe operation in the presence of moving obstacles.

Trajectory generation and collision avoidance for safe operation of cooperating UAVs

A novel analytical framework is presented addressing the need for a unified approach to aircraft separation assurance and Unmanned Aircraft System (UAS) Sense-and-Avoid (SAA). A brief review of the state-of-research in Separation Assurance and Collision Avoidance (SA&CA) technologies is included to highlight the benefits offered by the unified approach. In this approach, the employment of Adaptive Boolean Decision Logics (ABDL) allows automated selection of sensors/systems including passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance – Broadcast (ADS-B). This system performance based selection approach supports trusted autonomous operations during all flight phases. After describing a SA&CA reference architecture, the mathematical models employed in the unified approach to compute the overall uncertainty volume in the airspace surrounding an intruder/obstacle are described. The algorithms support the translation of navigation errors affecting the host aircraft platform and tracking errors affecting the intruder sensor measurements to unified range and bearing uncertainty descriptors. Simulation case studies are presented to evaluate the performance of the unified approach on a representative UAS host platform and a number of intruder platforms in both cooperative and non-cooperative scenarios. The results confirm the validity of the proposed unified methodology in providing a pathway for certification of SA&CA systems. The significance of this approach is also discussed in the Communication, Navigation and Surveillance/Air Traffic Management and Avionics (CNS + A) context, with a focus on the evolving UAS Traffic Management (UTM) requirements.

A Unified Analytical Framework for Aircraft Separation Assurance and UAS Sense-and-Avoid

A collision avoidance algorithm is developed and implemented that is applicable to many different unmanned aerial systems ranging from a single platform with the ability to perform all collision avoidance functions independently to multiple vehicles performing functions as a cooperative group with collision avoidance commands computed at a ground station. The algorithm leverages advances in several theoretical fields including robotics, homing guidance, and airspace management, and considers several approaches to conflict detection and resolution including the collision cone approach. The collision avoidance system is exercised and tested using operational hardware and platforms. Novel developments using an aggregated collision cone approach allows each unmanned aircraft to detect and avoid collisions with two or more other aircraft simultaneously. The collision avoidance system is implemented using a miniature unmanned aircraft with an onboard autopilot. Various simulation and flight test cases are used to demonstrate the algorithm’s robustness to different collision encounters at various engagement angles. The flight test results are compared with ideal, software-in-the-loop, and hardware-in-the-loop tests. The results presented are the first known flight tests of a global, three-dimensional, geometric collision avoidance system on an unmanned aircraft system.

UAS Collision Avoidance Algorithm Minimizing Impact on Route Surveillance

The airworthiness certification of aerospace cyber-physical systems traditionally relies on the probabilistic safety assessment as a standard engineering methodology to quantify the potential risks associated with faults in system components. This paper presents and discusses the probabilistic safety assessment of detect and avoid (DAA) systems relying on multiple cooperative and non-cooperative tracking technologies to identify the risk of collision of unmanned aircraft systems (UAS) with other flight vehicles. In particular, fault tree analysis (FTA) is utilized to measure the overall system unavailability for each basic component failure. Considering the inter-dependencies of navigation and surveillance systems, the common cause failure (CCF)-beta model is applied to calculate the system risk associated with common failures. Additionally, an importance analysis is conducted to quantify the safety measures and identify the most significant component failures. Results indicate that the failure in traffic detection by cooperative surveillance systems contribute more to the overall DAA system functionality and that the probability of failure for ownship locatability in cooperative surveillance is greater than its traffic detection function. Although all the sensors individually yield 99.9% operational availability, the implementation of adequate multi-sensor DAA system relying on both cooperative and non-cooperative technologies is shown to be necessary to achieve the desired levels of safety in all possible encounters. These results strongly support the adoption of a unified analytical framework for cooperative/non-cooperative UAS DAA and elicits an evolution of the current certification framework to properly account for artificial intelligence and machine-learning based systems.

Probabilistic Safety Assessment for UAS Separation Assurance and Collision Avoidance Systems

3Collision encounter modeling and simulation tools are developed for analyzing and evaluating unmanned aerial vehicle collision avoidance systems. All of the necessary components of a collision avoidance system simulation-based evaluation in three dimensions are discussed, including encounter trajectory generation for many collision scenarios, detection algorithms, and avoidance algorithms. The encounter trajectory generator uses statistical parameters for aircraft performance and maneuvers and statistical scenario parameters that create many diverse encounters in short order. Detection algorithms consist of a geometric sensor model, the collision cone approach, and a prioritization scheme that detect and determine the collision potential between the unmanned aircraft and multiple threat aircraft. Avoidance algorithms consist of proportional navigation guidance that uses information from the collision cone algorithm to command three dimensional avoidance maneuvers. Simulation results are provided to demonstrate the utility of the tools for collision avoidance system evaluations, and to demonstrate the effectiveness of the proposed algorithms. Simulation component interfaces are discussed to promote compatibility with many proposed algorithms and other evaluation tools.

Dennis M. Coulter

Papers

UAS Collision Encounter Modeling and Avoidance Algorithm Development for Simulating Collision Avoidance