scholarly journals The Obstacle-Avoidance Path Planning for UAV Based on IOCAD

2020 ◽  
Vol 38 (2) ◽  
pp. 238-245
Author(s):  
Qiqian Zhang ◽  
Weiwei Xu ◽  
Honghai Zhang ◽  
Han Li
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Rough Set Theory ◽  
Grid Method ◽  
Flight Path ◽  
Ray Method ◽  
Detection Model ◽  
Safety Distance ◽  
Planning Algorithm ◽  
Grid Length

To plan the path for UAV flying in the complex, dense and irregular obstacles environment, this paper proposed an obstacle collision-avoidance detection model and designed an UAV path planning algorithm based on irregular obstacles collision-avoidance detection (IOCAD), which includes irregular obstacles pretreatment method. The proposed method uses the grid method to model the environment. Rough set theory and convexity filling are used to pretreat the obstacles, and the ray method is used to select the available points. The intersection detection and the distance detection are held for the obstacle to the flight path. The objective function minimizes the distance from the obstacle to the flight path to get planned paths. The simulation results show that the proposed method can effectively plan the paths with the constraints of the assumed environment and UAV performances. It is shown that the performance of the proposed method is sensitive to the grid length and safety distance. The optimized values for the grid length and safety distance are 0.5 km and 0.4 km respectively.

Bi-level Flight Path Planning of UAV Formations with Collision Avoidance

2018 ◽  
Vol 93 (1-2) ◽  
pp. 193-211 ◽  
Author(s):  
Egidio D’Amato ◽  
Massimiliano Mattei ◽  
Immacolata Notaro
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Flight Path ◽  
Level Flight

Autonomous Collision Imminent Steering Maneuver in Presence of Surrounding Obstacles

2016 ◽  
Author(s):  
Rouhollah Jafari ◽  
Shuqing Zeng ◽  
Nikolai Moshchuk
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Geometric Method ◽  
Target Vehicle ◽  
Virtual Target ◽  
Planning Algorithm ◽  
Simulation Results ◽  
Collision Avoidance System ◽  
Path Planning Algorithm

In this paper, a collision avoidance system is proposed to steer away from a leading target vehicle and other surrounding obstacles. A virtual target lane is generated based on an object map resulted from perception module. The virtual target lane is used by a path planning algorithm for an evasive steering maneuver. A geometric method which is computationally fast for real-time implementations is employed. The algorithm is tested in real-time and the simulation results suggest the effectiveness of the system in avoiding collision with not only the leading target vehicle but also other surrounding obstacles.

scholarly journals A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

2019 ◽  
Vol 9 (7) ◽  
pp. 1470 ◽  
Author(s):  
Abdul Majeed ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Flight Path ◽  
Computational Time ◽  
Minimum Length ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Path Planning Algorithm

This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.

scholarly journals iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

2021 ◽  
Vol 11 (9) ◽  
pp. 3948
Author(s):  
Aye Aye Maw ◽  
Maxim Tyan ◽  
Tuan Anh Nguyen ◽  
Jae-Woo Lee
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Real World ◽  
Mission Planning ◽  
Planning System ◽  
Local Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

Path Planning for Collision Avoidance Maneuver

2013 ◽  
Author(s):  
Nikolai Moshchuk ◽  
Shih-Ken Chen ◽  
Chad Zagorski ◽  
Amy Chatterjee
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Optimal Path ◽  
Lateral Acceleration ◽  
Centripetal Acceleration ◽  
Target Vehicle ◽  
Acceleration Rate ◽  
Planning Algorithm ◽  
Path Planning Algorithm ◽  
Jerk Limitation

This paper summarizes the development of an optimal path planning algorithm for collision avoidance maneuver. The goal of the optimal path is to minimize distance to the target vehicle ahead of the host vehicle subject to vehicle and environment constraints. Such path constrained by allowable lateral (centripetal) acceleration and lateral acceleration rate (jerk). Two algorithms with and without lateral jerk limitation, are presented. The algorithms were implemented in Simulink and verified in CarSim. The results indicate that the lateral jerk limitation increases time-to-collision threshold and leads to a larger distance to the target required for emergency lane change. Collision avoidance path without lateral jerk limitation minimizes the distance to the target vehicle and is suitable for path tracking control in real-time application; however tracking such a path requires very aggressive control.

Path planning of collision avoidance for unmanned ground vehicles: A nonlinear model predictive control approach

2020 ◽  
pp. 095965182093784
Author(s):  
Peng Hang ◽  
Sunan Huang ◽  
Xinbo Chen ◽  
Kok Kiong Tan
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Nonlinear Model Predictive Control ◽  
Unmanned Ground Vehicles ◽  
Lateral Stability ◽  
Ground Vehicles ◽  
Moving Obstacles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In addition to the safety of collision avoidance, the safety of lateral stability is another critical issue for unmanned ground vehicles in the high-speed condition. This article presents an integrated path planning algorithm for unmanned ground vehicles to address the aforementioned two issues. Since visibility graph method is a very practical and effective path planning algorithm, it is used to plan the global collision avoidance path, which can generate the shortest path across the static obstacles from the start point to the final point. To improve the quality of the planned path and avoid uncertain moving obstacles, nonlinear model predictive control is used to optimize the path and conduct second path planning with the consideration of lateral stability. Considering that the moving trajectories of moving obstacles are uncertain, multivariate Gaussian distribution and polynomial fitting are utilized to predict the moving trajectories of moving obstacles. In the collision avoidance algorithm design, a series of constraints are taken into consideration, including the minimum turning radius, safe distance, control constraint, tracking error, etc. Four simulation conditions are carried out to verify the feasibility and accuracy of the comprehensive collision avoidance algorithm. Simulation results indicate that the algorithm can deal with both static and dynamic collision avoidance, and lateral stability.

Sampling based path planning algorithm for UAV collision avoidance

Sadhana ◽  
2021 ◽  
Vol 46 (3) ◽  
Author(s):  
A Saravanakumar ◽  
A Kaviyarasu ◽  
R Ashly Jasmine
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Planning Algorithm ◽  
Path Planning Algorithm

Research on Path Planning of Mine Rescue Robots Based on Fuzzy Control

2010 ◽  
Vol 44-47 ◽  
pp. 3593-3600
Author(s):  
Yan Hua Liang ◽  
Cheng Tao Cai
Keyword(s):  
Path Planning ◽  
Fuzzy Control ◽  
Collision Avoidance ◽  
Coal Mine ◽  
Fuzzy Inference ◽  
Coal Dust ◽  
Planning Algorithm ◽  
Rescue Robots ◽  
Avoidance Problem ◽  
Mine Rescue

After a coal mine disaster, especially a gas and coal dust explosion, the space-restricted and unstructured underground terrain and explosive gas require coal mine rescue robots with good obstacle-surmounting performance. This paper tackles path-planning or collision avoidance problem. The collision avoidance problem in path planning of mobile robots was inspired from fuzzy control concept. The deduction of the path-planning algorithm is followed. Fuzzy rules and fuzzy inference based on experiences is built, it constructed a reasonable and applicable control reactive rule table. The simulation results are presented to validate the approach. And it is also proved that the control arithmetic is available in both static and dynamic obstacles in coal mine environment.

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