Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

Liu, Qingli; Zhang, Yang; Li, Mengqian; Zhang, Zhenya; Cao, Na; Shang, Jiale

doi:10.1109/access.2021.3109879

Cited by 59 publications

(33 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Calculate the fitness value of Harris hawks; Set the parameter X prey as the best position of the prey; for (each Harris hawks (X i )) do Update the initial energy E 0 and jump strength J using Equations ( 10) and (15); Update E using Equation ( 9); if (|E| ≥ 1) then // Exploration phase Update the location vector using Equations ( 11) and (22); if (|E| < 1) then // Exploitation phase if (u ≥ 0.5 and |E| ≥ 0.5) then // Soft besiege Update the location vector using Equation (13); if (u ≥ 0.5 and |E| < 0.5) then // Hard besiege Update the location vector using Equation (16); if (u < 0.5 and |E| ≥ 0.5) then // Soft besiege with progressive rapid dives Update the location vector using Equation (17); if (u < 0.5 and |E| < 0.5) then // Hard besiege with progressive rapid dives Update the location vector using Equation (20); end Update the location vector using Equation (24); end end end Initialize the starting position of the search agents using the final position obtained by the Harris Hawks optimizer;…”

Section: Algorithm 1 Schhomentioning

confidence: 99%

“…Step 3: calculate the prey energy according to Equation (10). If |E| < 1, perform an exploration according to Equation (11) and perform Cauchy variation according to Equation (22) for the global optimal solution produced by Equation (11). If |E| ≥ 1, enter local exploitation and judge the besiege mechanisms according to the prey energy E and the prey escape probability u.…”

Section: Path Planning Based On Improved Schhomentioning

confidence: 99%

“…Zhang et al [ 21 ] propose an improved adaptive grey wolf optimization algorithm that propose an adaptive convergence factor adjustment strategy and an adaptive weight factor to update the individual’s position, establish the environmental map model by integrating digital elevation map and equivalent mountain threat model, and the performance evaluation function is established by fitting the calculated track length. Liu et al [ 22 ] propose a fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network that use SSA to find a series of nodes with the lowest comprehensive cost on the safe surface and when the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path re-planning to achieve the purpose of dynamic obstacle avoidance. Tong et al [ 23 ] propose an improved method of path planning and autonomous formation for UAVs that devise the mathematical model for UAV path planning as a multi-objective optimization with three indices and develop the method integrated by pigeon-inspired optimization and mutation strategies of differential evolution to optimize feasible paths.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization

Zhang

Ding

et al. 2022

Sensors

View full text Add to dashboard Cite

In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the Sine-cosine Algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.

show abstract

Section: Algorithm 1 Schhomentioning

confidence: 99%

Section: Path Planning Based On Improved Schhomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization

Zhang

Ding

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The Explorer has a high energy reserve and a high fitness value, which mainly provides foraging areas and directions for followers. Followers follow the explorer with the best fitness value to find food to gain their own energy reserves and increase their fitness value [ 23 , 24 ]. Some followers may also constantly monitor the explorer, compete for food, and alert when they are aware of the danger and move quickly to safe areas to get a better location, while sparrows in the middle of the population walk randomly close to other sparrows, known as anti-predatory behavior [ 25 , 26 ].…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

Membrane Fouling Prediction Based on Tent-SSA-BP

et al. 2022

View full text Add to dashboard Cite

In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.

show abstract

“…Several studies have been proposed in this context, where 3D path planning algorithms use cubic Bezier spiral curves to satisfy the curvature constraint are presented in [12]- [14], while [15] proposes a seventh-order Bézier curve as a continuous curvature path approximation, which does not exceed the kinematic constraints of an aerial vehicle. The authors of [16] have performed a fusion between two heuristic methodologies, with the aim of solving the smooth path planning problem in a mountainous environment, for which the characteristics of B-spline curves are exploited.…”

Section: Introductionmentioning

confidence: 99%

Smooth Three-Dimensional Route Planning for Fixed-Wing Unmanned Aerial Vehicles With Double Continuous Curvature

Vanegas¹,

Armesto²,

Girbés-Juan³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

This paper presents a smooth flight path planner for maneuvering in a 3D Euclidean space, which is based on two new space curves. The first one is called "Elementary Clothoid-based 3D Curve (ECb3D)", which is built by concatenating two symmetric Clothoid-based 3D Curves (Cb3D). The combination of these curves allows to reach an arbitrary orientation in 3D Euclidean space. This new curve allows to generate continuous curvature and torsion profiles that start and finish with a null value, which means that they can be concatenated with other curves, such as straight segments, without generating discontinuities on those variables. The second curve is called "Double Continuous Curvature 3D Curve (DCC3D)" which is built as a concatenation of three straight line segments and two ECb3D curves, allowing to reach an arbitrary configuration in position and orientation in the 3D Euclidean space without discontinuities in curvature and torsion. This trajectory is applied for autonomous path planning and navigation of unmanned aerial vehicles (UAVs) such as fixed-wing aircrafts. Finally, the results are validated on the FlightGear 2018 flight simulator with the UAV kadett 2400 platform.

show abstract

Multi-UAV Path Planning Based on Fusion of Sparrow Search Algorithm and Improved Bioinspired Neural Network

Cited by 59 publications

References 30 publications

UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization

UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization

Membrane Fouling Prediction Based on Tent-SSA-BP

Smooth Three-Dimensional Route Planning for Fixed-Wing Unmanned Aerial Vehicles With Double Continuous Curvature

Contact Info

Product

Resources

About