scholarly journals Research on an Obstacle Avoidance Method for UAV

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhenqi He ◽  
Lu Yao
Keyword(s):  
Kalman Filter ◽  
Obstacle Avoidance ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Local Planning ◽  
Living Body ◽  
Sigma Point ◽  
Local Path ◽  
Global Planning ◽  
Local Programming

With the continuous development of UAV technology, UAV has been widely used in various industries. In the flight process of UAV, UAV often changes the given path because of obstacles (including static nonliving body and moving living body). According to the properties of obstacles and the characteristics of UAV, standard Kalman filter is used for nonmaneuvering targets, and sigma point Kalman filter is used for maneuvering targets. In the aspect of obstacle avoidance, the minimum search method is used to get the initial population of local programming. Then, the improved genetic algorithm is run. Combined with the predicted obstacle features, the local planning path can be obtained. Finally, the local planning path and global planning path are combined to generate the planning path with new obstacles. At the end of the paper, the obstacle avoidance strategies of static and moving obstacles are simulated. The simulation results show that this method has fast convergence speed and good feasibility and can flexibly deal with the obstacle avoidance and local path planning of various new obstacles.

scholarly journals Rollover-Free Path Planning for Off-Road Autonomous Driving

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 614
Author(s):  
Xingyu Li ◽  
Bo Tang ◽  
John Ball ◽  
Matthew Doude ◽  
Daniel W. Carruth
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Autonomous Driving ◽  
Planning Method ◽  
Local Planning ◽  
Planning Stage ◽  
Local Path Planning ◽  
Global Route ◽  
Local Path ◽  
Global Planning

Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving.

Adaptive exact linearization control of batch polymerization reactors using a Sigma-Point Kalman Filter

2008 ◽  
Vol 18 (7-8) ◽  
pp. 663-675 ◽  
Author(s):  
Marc-André Beyer ◽  
Wolfgang Grote ◽  
Gunter Reinig
Keyword(s):  
Kalman Filter ◽  
Exact Linearization ◽  
Sigma Point

Recent Advances in Robot Trajectory Planning in a Dynamic Environment

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Rongzijun Shu ◽  
Guangsen Li
Keyword(s):  
Trajectory Planning ◽  
Dynamic Environment ◽  
Local Planning ◽  
Robot Trajectory ◽  
Recent Advances ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Task Collaboration ◽  
Global Planning ◽  
Robot Trajectory Planning

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

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