Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment

Yao, Ping; Wang, Honglun; Su, Zikang

doi:10.1016/j.ast.2015.09.037

Cited by 148 publications

(65 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, the proposed method is compared with a recently developed Lyapunov guidance vector field (LGVF)-based approach for planning the cooperative tracking path [18]. Figure 10 and Figure 11 illustrate the results of using the LGVF-based approach for two scenarios.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Coordinated Target Tracking via a Hybrid Optimization Approach

Wang

Cao

2017

Sensors

View full text Add to dashboard Cite

Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult to maintain the tracked object always within the sensor coverage area by using a single UAV. Hence, it is necessary to deploy a group of UAVs to improve the robustness of the tracking. This paper investigates the problem of tracking ground moving objects with a group of UAVs using gimbaled sensors under flight dynamic and collision-free constraints. The optimal cooperative tracking path planning problem is solved using an evolutionary optimization technique based on the framework of chemical reaction optimization (CRO). The efficiency of the proposed method was demonstrated through a series of comparative simulations. The results show that the cooperative tracking paths determined by the newly developed method allows for longer sensor coverage time under flight dynamic restrictions and safety conditions.

show abstract

Section: Simulation Results and Analysismentioning

confidence: 99%

Coordinated Target Tracking via a Hybrid Optimization Approach

Wang

Cao

2017

Sensors

View full text Add to dashboard Cite

show abstract

“…We use an elliptical shape with radius a, b and height h, as shown in Fig.2-a,b which provides a suitable space for the UAV in dense spaces by modeling the obstacle with minimum extra space. A dilatation factor is required because of the limited UAV maneuverability where it is not permitted to fly very close to any obstacle, so , Greater than one introduced to Equ.3 [33] and chooses p, q, r equal to one and apply the dilatation factor as shown in Equ.4.…”

Section: B Modeling Of Threatening Areas and Obstaclesmentioning

confidence: 99%

“…The obstacles can normally be described using two methods: one considers the obstacle boundaries as it is, the more the complex shape the more memory is required, the other method models the obstacles with the standard convex polyhedrons e.g. cylinder, cone, sphere or cube [33] as in Equ.3 (3) where the values of a, b, c and p, q, r determine the shape and size of the obstacle and (x0, y0, z0) is the center of the obstacle where Γ (P) =1 describes the surface equation, Γ(P)>1 describes the area outside the obstacle, Γ(P)<1 is the region inside the obstacle. In this work for solving the problem of obstacle avoidance, the UAV is modeled as a mass point at the center of an elliptical cylinder that bounds the UAV.…”

Section: B Modeling Of Threatening Areas and Obstaclesmentioning

confidence: 99%

High-Performance Path Planning of UAV under Complex Environment

Abozied¹,

Qin²

2016

IJMO

View full text Add to dashboard Cite

Abstract-This paper presents a high-performance path planning algorithm against resource and physical constraints under complex environment based on a modified RRTs algorithm so as to generate a fast and near optimal 3D UAV collision-free path with good smoothness and optimality. The advantages of the proposed algorithm lie in its low computational complexity and small memory to model complicated spaces with a large number of obstacles thus it can meet the requirements of implementation using low cost embedded processors. A series of simulation experiments and comparative analysis are carried out about four benchmark scenarios with VPB-RRT and DRRT algorithms to verify the predominance of our algorithm. The implementation results with hardware in the loop on a low cost embedded board based on Atmel SAM3X8E ARM Cortex-M3 CPU validated its performance effect. Meanwhile, various results demonstrated its robustness and effectiveness to play an important role in the online UAV mission generation.Index Terms-Path planning, rapidly-exploring random tree (RRT), unmanned aerial vehicle (UAV), Obstacle avoidance

show abstract

“…In consequence, it is important for USVs to adopt the collision avoidance control algorithm in the CAS. To date, the collision avoidance control algorithm has been developed in mobile robots [23], unmanned vehicles [24,25], and unmanned aerial vehicles (UAVs) [26], but there are few studies on USVs. A CAS for USVs emerged in relevant literature [8], in which the fuzzy estimator method was developed for collision avoidance control.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments

et al. 2018

View full text Add to dashboard Cite

Abstract:In recent years, unmanned surface vehicles (USVs) have received notable attention because of their many advantages in civilian and military applications. To improve the autonomy of USVs, this paper describes a complete automatic navigation system (ANS) with a path planning subsystem (PPS) and collision avoidance subsystem (CAS). The PPS based on the dynamic domain tunable fast marching square (DTFMS) method is able to build an environment model from a real electronic chart, where both static and dynamic obstacles are well represented. By adjusting the Saturation, the generated path can be changed according to the requirements for security and path length. Then it is used as a guidance trajectory for the CAS through a dynamic target point. In the CAS, according to finite control set model predictive control (FCS-MPC) theory, a collision avoidance control algorithm is developed to track trajectory and avoid collision based on a three-degree of freedom (DOF) planar motion model of USV. Its target point and security evaluation come from the planned path and environmental model of the PPS. Moreover, the prediction trajectory of the CAS can guide changes in the dynamic domain model of the vessel itself. Finally, the system has been tested and validated using the situations of three types of encounters in a realistic sea environment.

show abstract

Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment

Cited by 148 publications

References 19 publications

Coordinated Target Tracking via a Hybrid Optimization Approach

Coordinated Target Tracking via a Hybrid Optimization Approach

High-Performance Path Planning of UAV under Complex Environment

An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments

Contact Info

Product

Resources

About