Collision Avoidance and Path Following Control of Unmanned Aerial Vehicle in Hazardous Environment

2018 ◽  
Vol 95 (1) ◽  
pp. 193-210 ◽  
Author(s):  
Zhixiang Liu ◽  
Youmin Zhang ◽  
Chi Yuan ◽  
Laurent Ciarletta ◽  
Didier Theilliol
Keyword(s):  
Collision Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Path Following ◽  
Path Following Control ◽  
Aerial Vehicle

scholarly journals Path Following Control for Miniature Fixed Wing Unmanned Aerial Vehicle Under Uncertainties and Disturbances：A Two-Layered Framework

2021 ◽  
Author(s):  
Weinan Wu ◽  
Yao Wang ◽  
Chunlin Gong ◽  
Dan Ma
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Feedback Linearization ◽  
Vector Fields ◽  
Control Level ◽  
Path Following ◽  
Control Effort ◽  
Path Following Control ◽  
Straight Line ◽  
Control Scheme ◽  
Aerial Vehicle

Abstract In this paper a solution to the path following control problem for miniature fixed wing unmanned aerial vehicle (MAV) in the presence of inaccuracy modelling parameters and environmental disturbances is presented. We introduce a two-layered framework to collaborate guidance level with control level. A modified vector fields based path following methodology is proposed in the kinematics phase to track a Dubins path with straight line segments and circle ones. Then a Proportional-Integral-Derivative (PID) controller based on feedback linearization and gain scheduling techniques is designed such that the MAV can reject nonlinear dynamics, system uncertainties and disturbances by using a robust fuzzy control scheme. Eventually, by giving comparison test with control effort and track error as assessment metrics, both the practicality of the framework and the outperformance of the proposed algorithm are well demonstrated.

Path Planning Scheme for Collision Avoidance in Unmanned Aerial Vehicle Traffic Management System

2021 ◽  
Author(s):  
Amaanullah ◽  
Muhammed Ahmed Lamba ◽  
Surya Prakash S ◽  
Shrikant S. Tangade ◽  
Syed Sehraab Nawaz ◽  
...  
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Traffic Management ◽  
Management System ◽  
Vehicle Traffic ◽  
Planning Scheme ◽  
Aerial Vehicle ◽  
Traffic Management System

Optimized artificial potential field algorithm to multi-unmanned aerial vehicle coordinated trajectory planning and collision avoidance in three-dimensional environment

2019 ◽  
Vol 233 (16) ◽  
pp. 6032-6043 ◽  
Author(s):  
Jun Tang ◽  
Jiayi Sun ◽  
Cong Lu ◽  
Songyang Lao
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Planning ◽  
Potential Field ◽  
Three Dimensional ◽  
Artificial Potential Field ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Vehicle Trajectory

Multi-unmanned aerial vehicle trajectory planning is one of the most complex global optimum problems in multi-unmanned aerial vehicle coordinated control. Results of recent research works on trajectory planning reveal persisting theoretical and practical problems. To mitigate them, this paper proposes a novel optimized artificial potential field algorithm for multi-unmanned aerial vehicle operations in a three-dimensional dynamic space. For all purposes, this study considers the unmanned aerial vehicles and obstacles as spheres and cylinders with negative electricity, respectively, while the targets are considered spheres with positive electricity. However, the conventional artificial potential field algorithm is restricted to a single unmanned aerial vehicle trajectory planning in two-dimensional space and usually fails to ensure collision avoidance. To deal with this challenge, we propose a method with a distance factor and jump strategy to resolve common problems such as unreachable targets and ensure that the unmanned aerial vehicle does not collide into the obstacles. The method takes companion unmanned aerial vehicles as the dynamic obstacles to realize collaborative trajectory planning. Besides, the method solves jitter problems using the dynamic step adjustment method and climb strategy. It is validated in quantitative test simulation models and reasonable results are generated for a three-dimensional simulated urban environment.

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