Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm

Chen, Yongbo; Mei, Yuesong; Yu, Jianqiao; Su, Xiaolong; Xu, Nuo

doi:10.1016/j.neucom.2017.05.059

Cited by 97 publications

(22 citation statements)

References 14 publications

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“…In this paper, a new fitness function is constructed according to the maximum likelihood estimation formula of the parameters a and b of the GO model. The specific method is to substitute the first term in formula (7) into the second term and carry out mathematical transformation to construct a formula only related to parameter b, as shown below:…”

Section: Methods Description a Construction Of Fitness Functionmentioning

confidence: 99%

“…To solve the limitations of existing evolutionary algorithms in parameter identification, Li and Wu proposed a novel and efficient oppositional Wolf Pack Algorithm (OWPA), which has a good balance of exploitation and exploration, to estimate the parameters of Lorenz chaotic system [6]. In the literature [7], YongBo et al applied the modified wolf pack search (WPS) algorithm to compute the quasi-optimal trajectories for the rotor wing UAVs in the complex threedimensional (3D) spaces including the real and fake 3D spaces. In the literature [8], Xu et al proposed an improved Wolf Pack Algorithm to solve the optimization problem of logistics distribution center location.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Yang

Wang

et al. 2020

IEEE Access

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Software reliability is estimated and predicted based on software reliability model and software failure data. As a new optimization method, swarm intelligence algorithm has been widely used in solving the parameter optimization of the model. WPA (Wolf Pack Algorithm) and PSO (Particle Swarm Optimization) are two typical swarm intelligence algorithms. WPA has a strong global optimization ability, fast convergence speed and various optimization strategies, but the algorithm is relatively complex. PSO algorithm has a simple structure and fast convergence speed, but it is easy to fall into premature, which leads to low accuracy of solution. Considering the advantages and disadvantages of the two algorithms, a hybrid method of WPA and PSO is proposed, and a fitness function is constructed on maximum likelihood estimation, then the parameters of software reliability model are estimated and predicted based on the hybrid algorithm (WPA-PSO). Five sets of data from industry are used to estimate the parameters of GO model and make predictions. The simulation results show that the hybrid algorithm has higher accuracy of parameter estimation, better optimization performance, better accuracy of prediction and algorithm stability than single algorithm, and show obvious advantages than the single algorithm in the case of limited data.

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Section: Methods Description a Construction Of Fitness Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Yang

Wang

et al. 2020

IEEE Access

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show abstract

“…There are several path-planning approaches for UAVs that adopt these kinds of methods. In [36], the Wolf Pack Search (WPS) algorithm is modified with the concepts of Genetic Algorithms (GAs) to optimize the points of the B-splines that conform to the path. The workspace is discretized by uniformly dispersed waypoints marked as feasible or unfeasible.…”

Section: Introductionmentioning

confidence: 99%

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

et al. 2020

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This work presents a novel path-planning approach for Unmanned Aerial Vehicles (UAVs) in continuous 3D environments. This proposal aims to minimize the path length while avoiding collisions through the suitable adjusting of control points (the points that take the UAV from a start position to a target location). The above is stated as a constrained global optimization problem. This problem considers the overall length of the path as the single objective function. Regarding the problem constraints, they are related to the collision of the obstacles with the 3D shape of a path. The assignment of the path shape is also proposed in this work to streamline the planning process. Due to the optimization problem features (high nonlinearity, multimodality, non-differentiability, and the lack of an initial guess solution), a constraintshandling mechanism is used in meta-heuristics to find suitable optimized paths. Also, an enhanced pathsearch mechanism is included in these algorithms to deal with complex planning scenarios. The enhanced mechanism incorporates a path computed by a variant of the A-Star method (the Pruned A-Star) in the first set of candidate solutions of the meta-heuristics. The proposed approach is tested through six complex scenarios. Moreover, the performance of three well-known meta-heuristics, Differential Evolution (DE), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA), is studied to find a potential candidate to solve the path-planning problem. In this way, the paths found by DE show outstanding performance. The paths obtained by the Pruned A-Star technique are adopted as a point of comparison to determine the advantages and drawbacks of the proposal. INDEX TERMS Path-planning, optimization problem, aerial vehicles, meta-heuristics, continuous spaces.

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“…Kumar and Kumar [24] design a robot path planning algorithm to avoid collision under four warehouse models but the complexity of the algorithm does not seem to be taken into account compared with other algorithms. YongBo et al [25] studied the problem of UAV path planning in 3D space by minimizing the multi-objective cost function. They improved the traditional wolf pack search algorithm (WPS) by introducing the concepts of chromosome and gene in GA. Simulation results show that the trajectory generated by the improved WPS algorithm is far superior to traditional WPS algorithm, GA and random search methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Path Planning Algorithm for Warehouse Robots Based on a Two-Dimensional Grid Model

Yang

Wang

et al. 2020

IEEE Access

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In this paper, the path planning problem of goods transportation is formulated as a traveling salesman problem (TSP). A novel path planning algorithm for warehouse robots based on a two-dimensional (2D) grid model is proposed to solve this type of TSP. Firstly, we simplified the traditional pile type warehouse as a node-based 2D grid model. Then, a new concept called the largest convex polygon (LCP) is introduced to illustrate the shortest path to traverse all goods locations in an ideal condition. Next, the remaining locations are classified by their relationship with LCP and designed path planning rules separately. Finally, we merged the paths of different types of cargo locations to get the final path. The experimental results show that, compared with ant colony optimization (ACO) and genetic algorithm (GA), our proposed algorithm could effectively reduce the computation time and total path length. INDEX TERMS Warehouse robots, path planning, TSP, 2D grid model, largest convex polygon.

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Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm

Cited by 97 publications

References 14 publications

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

A Novel Path Planning Algorithm for Warehouse Robots Based on a Two-Dimensional Grid Model

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