Meteorology-Aware Path Planning for the UAV Based on the Improved Intelligent Water Drops Algorithm

Abstract

A wide range of applications of the unmanned aerial vehicle (UAV) have been observed in the past few years, and path planning is one of the most critical issues that require to be resolved. UAVs are still prone to meteorological impediments such as thunderstorms, ice accumulation, and severe convective weather for the safety of flights. This paper proposes a meteorology-aware path planning method based on the improved intelligent water drops (IIWD) algorithm. The algorithm consists of both static and dynamic path planning. In the static path planning phase, ice accumulation and the Richardson number are considered when determining the trajectory with low risk. In the dynamic path planning phase, the latest forecast products are adopted to modify the planned path, and the virtual potential force is applied to adjust the flight direction of the UAV when encountered with severe convective weather. The validation and efficiency of the proposed algorithm are verified via simulations in comparison with the ant colony optimization, genetic algorithm, and Q-learning algorithm, where the quality of our algorithm's performance in terms of flight time and risk degree is determined. Meanwhile, the risk degree of the UAV flight path at different altitudes is analyzed. The simulation results show that the average flight speed decreases, and the risk degree increases along with the descending of the flight altitude, respectively, which is found to be in consensus with the theory of meteorology.INDEX TERMS meteorology-aware path planning, UAV, dynamic environment, improved intelligent water drops algorithm.

Multiobjective Modular Strategic Planning Framework for Low-Altitude Missions Within the Urban Air Mobility Ecosystem

“…If the path to wn from its nearest neighbor nΓ(wn) is feasible (line 17), wn is selected as the new point to add to the tree, i.e., xw. If it is not, the last feasible point of the segment connecting nΓ(wn) to wn (i.e., xc) is used as the central point to perform a random extraction from a gaussian distribution (lines [20][21]. The random point xr becomes the next candidate point to add to the tree (i.e., xw) if the segment going from nΓ(xr) to xr is collision free (lines [22][23][24].…”

“…However also models involving multi-information risk assessment, thus combining population information with urban environment topology have been used in [7], [8]. Weather-based path definition has been tackled both in strategical and tactical phase in [20]. Here, wind module information has been used either to optimize the overall flight time [21], [22] or to minimize the energy consumption of the UAV [22]- [24].…”

Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022)

“…Linear 25 4,10,14,28,29,30,32,33,35,36,37,39,43,44,45,46,48,50,51,53,56,61,63,64,66 Nonlinear 43 1,2,3,5,6,7,8,9,11,12,13,15,16,17,18,19,20,21,22,23,24,25,26,27,31,34,38,40,…”

Improved Ant Colony Algorithm-based Automated Guided Vehicle Path Planning Research for Sensor-aware Obstacle Avoidance

“…(18) Nishi et al considered path planning for obstacle avoidance under AGV acceleration or deceleration conditions, established a continuous-time model, and proposed a heuristic algorithm based on column generation. (19) Duan et al proposed an operator for finely tuning paths to make path fragments shorter and avoid obstacles, and realized dynamic path planning based on a GA. (20) Ahmed et al proposed a collision prediction method based on vertex attributes and real time location information combined with graph theory, established a MIP model, and proposed an improved particle swarm optimization (PSO) algorithm suitable for optimizing collision avoidance decisions of multi-AGV systems. (21) Hu et al established a MIP model by analyzing the obstacles between sections and nodes and proposed an induced ant colony particle swarm algorithm.…”