Obstacle Magnification for 2-D Collision and Occlusion Avoidance of Autonomous Multirotor Aerial Vehicles

Lim, Jeonggeun; Pyo, Sangjin; Kim, Namyun; Lee, Jehong; Lee, Jong-Ho

doi:10.1109/tmech.2020.2975573

Cited by 14 publications

(3 citation statements)

References 49 publications

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“…Additionally, a deep-reinforcement-learning-based reactive online decision-making mechanism is applied in [ 25 ] to figure out the problem of obstacle avoidance, but this research only involves a single UAV. Some research [ 26 , 27 , 28 ] only considers a sparse obstacle environment or only stays in a two-dimensional environment, which is a disparate scenario compared with the environment in which a UAV swarm executes missions in reality. Unlike an insect swarm or a terrestrial animal (e.g., horse or wolf) swarm that consist of hundreds of individuals, a flock of starlings always consists of thousands of members, which makes starling flocks more valuable to explore.…”

Section: Introductionmentioning

confidence: 99%

Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

Zhang

Miao

2022

Biomimetics

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For the sake of accomplishing the rapidity, safety and consistency of obstacle avoidance for a large-scale unmanned aerial vehicle (UAV) swarm in a dynamic and unknown 3D environment, this paper proposes a flocking control algorithm that mimics the behavior of starlings. By analyzing the orderly and rapid obstacle avoidance behavior of a starling flock, a motion model inspired by a flock of starlings is built, which contains three kinds of motion patterns, including the collective pattern, evasion pattern and local-following pattern. Then, the behavior patterns of the flock of starlings are mapped on a fixed-wing UAV swarm to improve the ability of obstacle avoidance. The key contribution of this paper is collective and collision-free motion planning for UAV swarms in unknown 3D environments with dynamic obstacles. Numerous simulations are conducted in different scenarios and the results demonstrate that the proposed algorithm improves the speed, order and safety of the UAV swarm when avoiding obstacles.

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Section: Introductionmentioning

confidence: 99%

Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

Zhang

Miao

2022

Biomimetics

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“…Collision-free navigation algorithms have been studied for nonholonomic vehicles, self-driving cars, unmanned aerial vehicles, and surface vehicles [1]- [5]. Safe navigation of a vehicle inside an obstacle field forms a multi-objective control problem with analytic solutions that are computable only for some of the simplest cases [6].…”

Section: Introductionmentioning

confidence: 99%

Exponential Barrier Functions for Safe Steering of Nonholonomic Vehicles With Actuator Time-Delay

Ghaffari

Desai

2022

IEEE Access

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This work provides a provably safe feedback control for nonholonomic vehicles that autonomously operate in an obstacle field. A barrier function with a tunable, exponential decay rate is used to obtain a safe steering envelope for the vehicle. The safe steering envelope adapts, in real-time, to the vehicle's velocity and its distance to the static obstacles. The safety control corrects steering commands provided by a nominal tracking control and prevents collisions between the vehicle and the obstacles. The safety and stability of the algorithm are proved analytically and verified via multiple experiments. The resulting safety control is modular and can work well with obstacles of different footprints. Since quick steering control is essential for successful vehicle navigation, a two-layer predictor is proposed to compensate for the time-delay in the vehicle dynamics. The two-layer predictor improves the control response time by as much as a factor of four. The safety and tracking control act on the vehicle kinematics, and the two-layer predictor improves the vehicle's dynamic performance. The proposed control structure has a closed-form with eight tunable parameters, which facilitates control calibration and tuning in large systems of vehicles. Extensive experiments are carried out on a nonholonomic vehicle to verify the effectiveness of the proposed algorithm.INDEX TERMS Collision avoidance, vehicle safety, unmanned autonomous vehicles.

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“…Obstacle avoidance has become an integral part of nonholonomic mobile robots, self-driving cars, unmanned aerial vehicles, and surface vehicles [1]- [5]. Prominent methods include potential field [6], [7], collision cone [8], [9], path planning [10], [11], receding horizon control [12]- [14], and fuzzy neural networks [15].…”

Section: Introductionmentioning

confidence: 99%

Safety-Control of Mobile Robots Under Time-Delay Using Barrier Certificates and a Two-Layer Predictor

Ghaffari,

Desai

2021

Preprint

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Performing swift and agile maneuvers is essential for the safe operation of autonomous mobile robots. Moreover, the presence of time-delay restricts the response time of the system and hinders the safety performance. Thus, this paper proposes a modular and scalable safety-control design that utilizes the Smith predictor and barrier certificates to safely and consistently avoid obstacles with different footprints. The proposed solution includes a two-layer predictor to compensate for the time-delay in the servo-system and angle control loops. The proposed predictor configuration dramatically improves the transient performance and reduces response time. Barrier certificates are used to determine the safe range of the robot's heading angle to avoid collisions. The proposed obstacle avoidance technique conveniently integrates with various trajectory tracking algorithms, which enhances design flexibility. The angle condition is adaptively calculated and corrects the robot's heading angle and angular velocity. Also, the proposed method accommodates multiple obstacles and decouples the control structure from the obstacles' shape, count, and distribution. The control structure has only eight tunable parameters facilitating control calibration and tuning in large systems of mobile robots. Extensive experimental results verify the effectiveness of the proposed safety-control.

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Obstacle Magnification for 2-D Collision and Occlusion Avoidance of Autonomous Multirotor Aerial Vehicles

Cited by 14 publications

References 49 publications

Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles

Exponential Barrier Functions for Safe Steering of Nonholonomic Vehicles With Actuator Time-Delay

Safety-Control of Mobile Robots Under Time-Delay Using Barrier Certificates and a Two-Layer Predictor

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