Route planning of mixed ant colony algorithm based on Dubins path

Cheng, Jiagen; Hu, Xin; Xiao, Jin; Zhang, Guofeng; Zhou, Qing

doi:10.1109/iciea51954.2021.9516227

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…However, the route planning algorithms proposed in the literature [14,[22][23][24][25] do not consider the influence of turning radius. The literature [26][27][28] proposed to use the DUBINS curve to solve the influence of the turning radius on the route, but the DUBINS curve has limitations, limiting the forward direction of the target and the speed direction at the beginning and ending. Therefore, the optimal solution in the DUBINS set is only the optimal solution in the specific beginning and ending speed directions.…”

Section: Discussionmentioning

confidence: 99%

“…Radius Effects [22] A simple heuristic greedy method NO [14] An optimization method based on an adaptive pseudo parallel genetic algorithm NO [23] Combined the K-means clustering algorithm and simulated annealing algorithm NO [24] An ant colony algorithm integrating genetic algorithm NO [25] Combined particle swarm optimization algorithm and genetic algorithm NO [26] A UAV path planning approach based on modified Ant colony algorithm and DUBINS curve YES [27] Mixed ant colony Algorithm Based on DUBINS Path YES [28] A variable radius trajectory generation and waypoint planning method based on DUBINS curve. YES…”

Section: Ref Proposed Methods Consider Turningmentioning

confidence: 99%

“…At the same time, this method can accurately generate the optimal route and improve the flight efficiency. It can be seen from Table 1 that the route planning algorithms proposed in the literature [14,[22][23][24][25] do not consider the influence of the turning radius, and the literature [26][27][28] proposed to use the DUBINS curve to solve the influence of the turning radius on the route. The DUBINS curve, however, has limitations, limiting the direction in which the target is heading and the speed direction at the beginning and ending.…”

Section: Methodsmentioning

confidence: 99%

“…Cheng, J. and Hu, X. [27] proposed an improved ant colony algorithm that can solve feasible paths and speed up the convergence speed. At the same time, the DUBINS curve is used to curve the solution path, so that the solution path can meet the requirements of the UAV flight curvature.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genetic Algorithm and Greedy Strategy-Based Multi-Mission-Point Route Planning for Heavy-Duty Semi-Rigid Airship

Wang

Zhang

et al. 2022

Sensors

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The large volume and windward area of the heavy-duty semi-rigid airship (HSA) result in a large turning radius when the HSA passes through every mission point. In this study, a multi-mission-point route planning method for HSA based on the genetic algorithm and greedy strategy is proposed to direct the HSA maneuver through every mission point along the optimal route. Firstly, according to the minimum flight speed and the maximum turning slope angle of the HSA during turning, the minimum turning radius of the HSA near each mission point is determined. Secondly, the genetic algorithm is used to determine the optimal flight sequence of the HSA from the take-off point through all the mission points to the landing point. Thirdly, based on the optimal flight sequence, the shortest route between every two adjacent mission points is obtained by using the route planning method based on the greedy strategy. By determining the optimal flight sequence and the shortest route, the optimal route for the HSA to pass through all mission points can be obtained. The experimental results show that the method proposed in this study can generate the optimal route with various conditions of the mission points using simulation studies. This method reduces the total voyage distance of the optimal route by 18.60% on average and improves the flight efficiency of the HSA.

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