Path Planning and Real-Time Collision Avoidance Based on the Essential Visibility Graph

Abstract

This paper deals with a novel procedure to generate optimum flight paths for multiple unmanned aircraft in the presence of obstacles and/or no-fly zones. A real-time collision avoidance algorithm solving the optimization problem as a minimum cost piecewise linear path search within the so-called Essential Visibility Graph (EVG) is first developed. Then, a re-planning procedure updating the EVG over a selected prediction time interval is proposed, accounting for the presence of multiple flying vehicles or movable obstacles. The use of Dubins curves allows obtaining smooth paths, compliant with flight mechanics constraints. In view of possible future applications in hybrid scenarios where both manned and unmanned aircraft share the airspace, visual flight rules compliant with International Civil Aviation Organization (ICAO) Annex II Right of Way were implemented. An extensive campaign of numerical simulations was carried out to test the effectiveness of the proposed technique by setting different operational scenarios of increasing complexity. Results show that the algorithm is always able to identify trajectories compliant with ICAO rules for avoiding collisions and assuring a minimum safety distance as well. Furthermore, the low computational burden suggests that the proposed procedure can be considered a promising approach for real-time applications.

Autonomous Obstacle Avoidance Algorithm for UAV in 3D Space Based on Improved Potential Field and Fluid Disturbance

“…F att (p) = dU att (P ) dρ (P, P goal ) (6) where U att (P ) is the gravitational field function, ζ is the gravitational coefficient, ρ (P, P goal ) represents the Euclidean distance between them, and P represents the gravity of point.…”

“…The convergence speed and route optimization of these methods is compared with different obstacle distribution scenarios, The results show that the genetic algorithm is less sensitive to time than the increase of the number of cells, the artificial potential field method has a faster convergence speed, but it is easy to fall into local optimization and can not get the optimal route, A* algorithm improves the route optimality by sacrificing the convergence speed. Route planning algorithm usually has a contradiction between the convergence speed and the route optimality [6]. In order to solve this contradiction, some scholars also put forward some obstacle avoidance algorithms which combine various methods [7] and other scholars also consider using deep learning and image processing algorithms to solve the problem [8,9].…”

Combination Strategy of Dynamic Obstacle-Avoidance and Autonomous Tracking for Hazardous Gases Inspection Robot in Man-Machine Environment

“…However, when the gravitation and repulsion are equal, it will fall into a local optimal solution and produce concussion route [13]. The vector field histogram proposed by Janet J et al [14] in the 1990s has high requirements for data storage, which requires the use of sensors to collect data in advance, and the reliability of obstacle avoidance is strictly affected by sensor performance [15]. In order to avoid the original defects of the artificial potential field method, Borenstein et al [16] proposed the VFH vector field histogram method, which verified that the specific candidate direction can successfully guide the robot to deal with the local optimal solution problem of the pure local obstacle avoidance algorithm, but the application occasions are restricted [17].…”

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

“…However, these methods do not include any optimization in terms of image and inspection quality. Other similar methods have also been developed in the last few years for deployed 3D environments, such as Rapidly-exploring Random Tree [ 27 ] and Visibility Graph [ 28 ]. Still, none of these focused on the specific application shown here.…”