UAV path planning using artificial potential field method updated by optimal control theory

Chen, Yongbo; Luo, Guan-chen; Mei, Yuesong; Yu, Jianqiao; Su, Xiaolong

doi:10.1080/00207721.2014.929191

Cited by 365 publications

(157 citation statements)

References 10 publications

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“…According to the form of the repulsion F c , the change of the function value is shown in Fig. 1 [12]. Thereinto, the value A and B are based on the alterable coefficients k φ and l φ .…”

Section: Single Uav Particle Dynamics In the Path Planning Processmentioning

confidence: 99%

“…Then, the single ith UAV particle dynamics is based on the APF method with the additional control force. The main idea of the APF method is to use repulsive potential fields emanating from the obstacles to force the UAV away and an attractive potential field emanating from the target as to attract the UAV [12].…”

Section: Single Uav Particle Dynamics In the Path Planning Processmentioning

confidence: 99%

“…The resultant force joined by the potential field force and control force is used to control the UAV to avoid collision and reach target. The particle dynamics system of UAV under resultant force is [12,15]:…”

Section: Single Uav Particle Dynamics In the Path Planning Processmentioning

confidence: 99%

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Path Planning for Multi-UAV Formation

Chen

et al. 2014

J Intell Robot Syst

111

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This paper presents an efficient and feasible algorithm for the path planning problem of the multiple unmanned aerial vehicles (multi-UAVs) formation in a known and realistic environment. The artificial potential field method updated by the additional control force is used for establishing two models for the single UAV, which are the particle dynamic model and the path planning optimization model. The additional control force can be calculated by using the optimal control method. Furthermore, the multi-UAV path planning model is established by introducing "virtual velocity rigid body" and "virtual target point". Then, the motion states of the lead plane and wingmen are obtained from the path planning model. Finally, the path following process based on the quadrotor helicopter PID controllers is introduced to verify the rationality of the path planning results. The simulation results show that the artificial potential method with the additional control force improved by the optimal control method has a good path planning ability for the single UAV and the all UAVs formation. At the same time, the path planning results are available and the UAVs can basically track the UAV formation.

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“…According to the form of the repulsion F c , the change of the function value is shown in Fig. 1 [12]. Thereinto, the value A and B are based on the alterable coefficients k φ and l φ .…”

Section: Single Uav Particle Dynamics In the Path Planning Processmentioning

confidence: 99%

Section: Single Uav Particle Dynamics In the Path Planning Processmentioning

confidence: 99%

See 1 more Smart Citation

Path Planning for Multi-UAV Formation

Chen

et al. 2014

J Intell Robot Syst

111

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show abstract

“…The obstacle avoidance potential field is proposed in this paper to help determine the intermediate configuration for path replanning inspired by the methods proposed in [30,31,40]. The obstacle avoidance potential field is a compound repulsive potential field which consists of two components and V. These components are caused by the positions and velocities of the obstacles, respectively.…”

Section: Obstacle Avoidance Potential Field Designmentioning

confidence: 99%

Reactive Path Planning Approach for Docking Robots in Unknown Environment

Cui

Yan

Wang

2017

Journal of Advanced Transportation

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Autonomous robots need to be recharged and exchange information with the host through docking in the long-distance tasks. Therefore, feasible path is required in the docking process to guide the robot and adjust its pose. However, when there are unknown obstacles in the work area, it becomes difficult to determine the feasible path for docking. This paper presents a reactive path planning approach named Dubins-APF (DAPF) to solve the path planning problem for docking in unknown environment with obstacles. In this proposed approach the Dubins curves are combined with the designed obstacle avoidance potential field to plan the feasible path. Firstly, an initial path is planned and followed according to the configurations of the robot and the docking station. Then when the followed path is evaluated to be infeasible, the intermediate configuration is calculated as well as the replanned path based on the obstacle avoidance potential field. The robot will be navigated to the docking station with proper pose eventually via the DAPF approach. The proposed DAPF approach is efficient and does not require the prior knowledge about the environment. Simulation results are given to validate the effectiveness and feasibility of the proposed approach.

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“…UAV can take off like ordinary plane under the wireless remote control or be used launching cradle to blast offin the military, the UAVs not only are widely used and low cost, but also has the advantages of no casualties risk and great viability [2]. UAV plays a very important role in the modern war, and has broad prospects in the civil field.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle (UAV) Cooperative Mission Planning

Liu¹

2017

AJETM

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Unmanned Aerial Vehicle (UAV) is a kind of new operational platform possessing ability to flight autonomously and perform independently a task,which can not only carry out non-attack tasks,such as military reconnaissance, surveillance and search, but also to carry out tasks to air-to-ground attacking, target bombing and so on. With the rapid development of UAVtechnology, more and more UAV will be applied in the future battlefield. An UAV combat troops have seven UAV bases, which are from P01 to P07. Every base has some FY-1 type UAVs. At the same time, FY-1 UAV can be loaded by two kinds of load, which are S-1 and S-2. Now we need to achieve the aim to detect 10 target groupsfromA01 to A10, which are total 68 goals. And each target group has radar station. Under the above condition, this papermakes the best plan for the UAV combat troops, and uses FY-1 UAV to find best route and scheduling strategy of UAV, which including each UAV drone off base, loading, departure time, track and target reconnaissance. The goal is to ensure minimum time summation in a effective probe range to stay defense radar for UAV. First of all, this paper considers only four UAV bases with FY-1 UAV, so the 68 targets are divided into four regions by K-means algorithm; Then the global shortest path model is established, when the local route is the shortest. The route is drawn according to the route. According to the former route, the general shortest path model is established. It is composed of shortest route distance and the distance from UAV to the corresponding area. And then this paper determine which base the UAV will go to. Finally, the minimum time is calculated as 17.52h. The eight UAVs are arranged in this process, which are composed of four UAVs withS-1 and fourUAVs withS-2. The UAVs are offered by P01, P03, P05 and P07.

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UAV path planning using artificial potential field method updated by optimal control theory

Cited by 365 publications

References 10 publications

Path Planning for Multi-UAV Formation

Path Planning for Multi-UAV Formation

Reactive Path Planning Approach for Docking Robots in Unknown Environment

Unmanned Aerial Vehicle (UAV) Cooperative Mission Planning

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