Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field

Wang, Xiaohua; Liang, Yan; Liu, Shun; Xu, Linfeng

doi:10.3390/s19010031

Cited by 10 publications

(7 citation statements)

References 26 publications

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“…The APF method is applied to the field of obstacle avoidance and collision avoidance of UAV, and satisfactory results are obtained in some researches. Zhenhua Pan et al [18] combined the improved APF with PID algorithm, Daoyong Wang et al [19] combined the improved APF with collision prediction model, or realized the obstacle avoidance of UAV with bearings only measurement [20]. If the UAV companion is regarded as a dynamic obstacle [21,22], or a dynamic APF [23] is established, the collision avoidance between UAVs can be solved in the improved APF [24].…”

Section: Introductionmentioning

confidence: 99%

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Sun

Huang

et al. 2020

IEEE Access

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In this paper, a method of multi UAV cluster control based on improved artificial potential field (APF) is proposed. The k-means method is used to integrate and optimize the attractive force between UAVs, and the concept of virtual core is introduced to realize the cluster control and adaptive formation flight of multiple UAVs. The attractive disturbance component of the target point is introduced and the backtracking-filling method is proposed to solve the local minimum problem in the APF. The repulsion force in the APF can realize obstacle avoidance and collision avoidance, and the virtual core can control the UAV cluster to fly to the target point under the attractive force of potential field, so as to realize the track planning and multi aircraft cooperative task. In the process of cluster flight when the UAV fails, merges or dispatches, the method can realize cluster reconfiguration and the cluster control effect and task execution success rate can be improved. The simulation experiments in virtual APF and urban environment APF show the effectiveness of this method.INDEX TERMS Cluster control, virtual core algorithm, cluster reconstruction, track planning, improved APF

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Section: Introductionmentioning

confidence: 99%

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Sun

Huang

et al. 2020

IEEE Access

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“…In 2018, Li, A et al proposed a dynamic trajectory planning method based on ACT-R (ideal rational adaptive control) cognitive model [14] and is used for active obstacle avoidance in electric vehicles. In 2019, Wang, X et al proposed a method for obstacle avoidance with pure azimuth measurement in the absence of a model of obstacle motion [15]. Zhang, W et al proposed a dynamic collision avoidance method based on collision risk assessment and improved speed barrier method [16] for uncertain dynamic obstacle environments in 2017.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

Zhou

et al. 2020

Applied Sciences

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In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.

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“…Some good two-dimensional path planning modeling methods are still in use now, such as the grid method [11], topological method [12], visibility graph method [13], and so on. There are some typical path planning algorithms, such as the Dijkstra algorithm [14], Prim algorithm [15], simulated annealing algorithm [16], A* algorithm [17], artificial potential field method [18], and the fuzzy logic-control algorithm [19], which are widely used in engineering practice.…”

Section: Introductionmentioning

confidence: 99%

“…Traditional path planning includes methods based on virtual potential field or heuristic rules and methods based on mathematical optimization. The former methods are widely used in 2D path planning, especially the harmonic potential field method [ 21 , 22 ] and artificial potential field method [ 18 , 23 ], which have been successfully extended to 3D space. Methods based on mathematical optimization mainly contain the nonlinear classification method of the support vector machine [ 24 ], linear programming method, mixed integer linear programming method [ 25 ], etc.…”

Section: Introductionmentioning

confidence: 99%

3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

Wang

Kan

Guo

et al. 2019

Sensors

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Path planning is a fundamental issue in the aspect of robot navigation. As robots work in 3D environments, it is meaningful to study 3D path planning. To solve general problems of easily falling into local optimum and long search times in 3D path planning based on the ant colony algorithm, we proposed an improved the pheromone update and a heuristic function by introducing a safety value. We also designed two methods to calculate safety values. Concerning the path search, we designed a search mode combining the plane and visual fields and limited the search range of the robot. With regard to the deadlock problem, we adopted a 3D deadlock-free mechanism to enable ants to get out of the predicaments. With respect to simulations, we used a number of 3D terrains to carry out simulations and set different starting and end points in each terrain under the same external settings. According to the results of the improved ant colony algorithm and the basic ant colony algorithm, paths planned by the improved ant colony algorithm can effectively avoid obstacles, and their trajectories are smoother than that of the basic ant colony algorithm. The shortest path length is reduced by 8.164%, on average, compared with the results of the basic ant colony algorithm. We also compared the results of two methods for calculating safety values under the same terrain and external settings. Results show that by calculating the safety value in the environmental modeling stage in advance, and invoking the safety value directly in the path planning stage, the average running time is reduced by 91.56%, compared with calculating the safety value while path planning.

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Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field

Cited by 10 publications

References 26 publications

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Multi UAV Cluster Control Method Based on Virtual Core in Improved Artificial Potential Field

Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

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