Optimal Collision Avoidance Trajectories via Direct Orthogonal Collocation for Unmanned/Remotely Piloted Aircraft Sense and Avoid Operations

Smith, Norman F.; Cobb, Richard; Pierce, Scott J.; Raska, Vincent

doi:10.2514/6.2014-0966

Cited by 23 publications

(9 citation statements)

References 18 publications

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“…the other drones and obstacles. These techniques rely on static objects, with known locations and sizes, for calculating the efficient route within a finite time period [89], [90]; and 4) sense-and-avoid methods that mainly focus on reducing the computational cost, with short response time, by simplifying the process of collision avoidance to an individual detection and avoidance of obstacles for each drone and deviating the drone from its original path when needed, independently of the other drones' plans [36], [91], [92]. Each method is explained in more detail in the following sections.…”

Section: Figure 6: Deliberative Collision Avoidancementioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs. INDEX TERMS autonomous aerial vehicles, autonomous vehicles, collision avoidance, active and passive sensors, optimisation-based, force-field based, sense and avoid, geometry based

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Section: Figure 6: Deliberative Collision Avoidancementioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

et al. 2020

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“…Typically, algorithms for collision avoidance can be divided into three generic classes [25], [26]: 1) force-field methods that work on the principle of applying attractive/repulsive electric forces existing amongst charged objects; each drone in a swarm is considered a charged particle, and attractive or repulsive forces between drones and the obstacles are used to generate and choose the routes to be taken [27], [28]; 2) sense-andavoid based methods, where the process of collision avoidance is simplified into individual detection and avoidance of the objects and obstacles, resulting in short response times and reducing the computational power needed [29], [30]; and 3) optimization based methods which focus on providing the optimal or near-optimal solutions for path planning and motion characteristics of each drone with respect to the other drones and obstacles. In order to calculate efficient routes within a finite time horizon, these methods rely on static objects with known locations and dimensions [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Formation Morphing for Collision Avoidance in a Swarm of Drones

et al. 2020

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Two important aspects in dealing with autonomous navigation of a swarm of drones are collision avoidance mechanism and formation control strategy; a possible competition between these two modes of operation may have negative implications for success and efficiency of the mission. This issue is exacerbated in the case of distributed formation control in leader-follower based swarms of drones since nodes concurrently decide and act through individual observation of neighbouring nodes' states and actions. To dynamically handle this duality of control, a plan of action for multi-priority control is required. In this paper, we propose a method for formation-collision co-awareness by adapting the thin-plate splines algorithm to minimize deformation of the swarm's formation while avoiding obstacles. Furthermore, we use a non-rigid mapping function to reduce the lag caused by such maneuvers. Simulation results show that the proposed methodology maintains the desired formation very closely in the presence of obstacles, while the response time and overall energy efficiency of the swarm is significantly improved in comparison with the existing methods where collision avoidance and formation control are only loosely coupled. Another important result of using non-rigid mapping is that the slowing down effect of obstacles on the overall speed of the swarm is significantly reduced, making our approach especially suitable for time critical missions.

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“…et al who implemented a nonlinear filter for a collision avoidance application for UA V using the measurements gathered by radar [5]. Smith, N. E. et al used a nonlinear filter (particle filter) to estimate the current and future position of the intruder and use the particles to model the probability regions for the optimal control problem [6].…”

Section: Yifengniumentioning

confidence: 99%

A particle filter based intruder state estimation method for UAV collision avoidance

Ling

Niu

2015

2015 Chinese Automation Congress (CAC)

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A critical safety concern with the rapid increase in UA Vs is developing algorithms that can solve the pressing airborne collision avoidance problem. A fundamental necessity in solving the airborne collision avoidance problem is the need to estimate the state of the intruder using the onboard sensor measurement which is usually noisy. Advanced mtering algorithm, such as Particle Filters can provide very accurate estimates of the target state.However, unlike the way that sensors located off-board, the measurements made by the onboard sensor are relative measurements between the UAV aircraft and the intruder. The accuracy of the UAV's state will also affect the location of the Intruder. In this paper, we will take the accuracy of the UA V's state into consideration and use the SIR particle mter to estimate the state of the Intruder. The feasibility and performance of the proposed approach is not only mathematically analyzed, but also verified through simulation.Through the simulation results, we can see that our approach is more accurate in estimating the state of the intruder.

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Optimal Collision Avoidance Trajectories via Direct Orthogonal Collocation for Unmanned/Remotely Piloted Aircraft Sense and Avoid Operations

Cited by 23 publications

References 18 publications

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches

Energy-Efficient Formation Morphing for Collision Avoidance in a Swarm of Drones

A particle filter based intruder state estimation method for UAV collision avoidance

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