Collision Avoidance Based on Line-of-Sight Angle

Cichella, Venanzio; Marinho, Thiago; Stipanović, Dušan M.; Hovakimyan, Naira; Kaminer, Isaac; Trujillo, Anna C.

doi:10.1007/s10846-017-0517-6

Cited by 8 publications

(9 citation statements)

References 31 publications

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“…In this experiment an unknown object produces a zig-zag inducing two reactive evading maneuvers, the first of which lasts from t = 1s until t = 4s, and the second maneuver lasts from t = 6 to t = 7.5 seconds, as observed in the time-to-collision plot in Figure 9. This illustrates the fact that the controller is able to engage and disengage the avoidance behavior without the knowledge of distance, overcoming the limitation of the algorithm proposed in [14]. Figure 8 shows the successful collision avoidance, and the vehicle returning to the desired trajectory.…”

Section: B Scenariomentioning

confidence: 91%

“…The proposed algorithm is suited for any unicycle model, which represents a large class of vehicles, such as constant altitude fixed wing UAVs and differential drive ground robots. The collision avoidance component of the control is added to the heading tracking controller angle as introduced in [14], [15]. The structure of the heading control law is defined as:…”

Section: Problem Formulationmentioning

confidence: 99%

“…This work follows from recent efforts to provide theoretical results for collision avoidance without distance measurement [14] and [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Biologically Inspired Collision Avoidance Without Distance Information

Marinho¹,

Amrouche²,

Stipanović³

et al. 2021

Preprint

Self Cite

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Biological evidence shows that animals are capable of evading eminent collision without using depth information, relying solely on looming stimuli. In robotics, collision avoidance among uncooperative vehicles requires measurement of relative distance to the obstacle. Small, low-cost mobile robots and UAVs might be unable to carry distance measuring sensors, like LIDARS and depth cameras. We propose a control framework suitable for a unicycle-like vehicle moving in a 2D plane that achieves collision avoidance. The control strategy is inspired by the reaction of invertebrates to approaching obstacles, relying exclusively on line-of-sight (LOS) angle, LOS angle rate, and time-to-collision as feedback. Those quantities can readily be estimated from a monocular camera vision system onboard a mobile robot. The proposed avoidance law commands the heading angle to circumvent a moving obstacle with unknown position, while the velocity controller is left as a degree of freedom to accomplish other mission objectives. Theoretical guarantees are provided to show that minimum separation between the vehicle and the obstacle is attained regardless of the exogenous tracking controller.

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Section: B Scenariomentioning

confidence: 91%

Section: Problem Formulationmentioning

confidence: 99%

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Biologically Inspired Collision Avoidance Without Distance Information

Marinho¹,

Amrouche²,

Stipanović³

et al. 2021

Preprint

Self Cite

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show abstract

“…Misestimations of the TTC may lead to wrong avoidance timing. Cichella et al proposed a relative-angle-based collision avoidance method for UAVs [33]. The line of sight (LOS) represents the angle between the velocity vector of the host aircraft and the distance vector from the intruder.…”

Section: Vision-based Reactive Avoidance Methods For Uavsmentioning

confidence: 99%

Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment

Lai,

Lin

2024

Remote Sensing

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With the increasing demand for unmanned aerial vehicles (UAVs), the number of UAVs in the airspace and the risk of mid-air collisions caused by UAVs are increasing. Therefore, detect and avoid (DAA) technology for UAVs has become a crucial element for mid-air collision avoidance. This study presents a collision avoidance approach for UAVs equipped with a monocular camera to detect small fixed-wing intruders. The proposed system can detect any size of UAV over a long range. The development process consists of three phases: long-distance object detection, object region estimation, and collision risk assessment and collision avoidance. For long-distance object detection, an optical flow-based background subtraction method is utilized to detect an intruder far away from the host. A mask region-based convolutional neural network (Mask R-CNN) model is trained to estimate the region of the intruder in the image. Finally, the collision risk assessment adopts the area expansion rate and bearing angle of the intruder in the images to conduct mid-air collision avoidance based on visual flight rules (VFRs) and conflict areas. The proposed collision avoidance approach is verified by both simulations and experiments. The results show that the system can successfully detect different sizes of fixed-wing intruders, estimate their regions, and assess the risk of collision at least 10 s in advance before the expected collision would happen.

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“…This agreement protocol is used in competing speeds in a common interval 55 . As a navigation query, collision detection and avoidance in agents 56 or multi-agent [57][58][59] scenarios has also been addressed. Theoretically, chain collisions can be avoided or decreased in severity by reducing the time between an emergency occurrence and the moment when approaching vehicles are in a chain collision.…”

Section: Chain Collision or Mvcs Descriptionmentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

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Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

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Collision Avoidance Based on Line-of-Sight Angle

Cited by 8 publications

References 31 publications

Biologically Inspired Collision Avoidance Without Distance Information

Biologically Inspired Collision Avoidance Without Distance Information

Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

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