A star algorithm improvement method, system and device based on dynamic weight and medium

Abstract

The invention discloses a dynamic weight-based A star algorithm improvement method, system and device, and a medium. The method comprises the steps of obtaining environment information collected in a flight process of an unmanned aerial vehicle; improving an evaluation function of an A star algorithm; designing a dynamic weight generator, and generating a heuristic weight of the current node in real time according to the environment information; when the unmanned aerial vehicle is close to the starting point, the proportion of h (n) is large; when the unmanned aerial vehicle continuously approaches the target, the weight of g (n) is gradually increased; wherein h (n) is the estimated cost of moving from the specified position to the terminal point, and g (n) is the moving cost of moving from the starting point to the specified grid. According to the method, in the process that the unmanned aerial vehicle carries out path finding on the target point, the dynamic heuristic weight adjusted in real time is added into the path evaluation function of the A star algorithm, and unification of path finding accuracy and efficiency is achieved. The method can be widely applied to the technical field of unmanned aerial vehicle obstacle avoidance and path planning.

Description

technical field [0001] The invention relates to the technical field of UAV obstacle avoidance and path planning, in particular to an improved method, system, device and medium for an A-star algorithm based on dynamic weights. Background technique [0002] At present, the research direction of UAV is mainly the path planning problem of UAV. Path planning is the focus of current research, especially when UAVs perform some urgent and special tasks, it is extremely important to be able to search for a route with the shortest flight time and the smallest path distance while avoiding obstacles. Therefore, it can be widely used in geological survey, personnel search and rescue and disaster relief work. Therefore, the accuracy and efficiency of UAV path search technology has always been the focus of our research. There are currently many UAV path planning algorithms. Dijkstra's algorithm is not suitable for time-sensitive scenarios because it traverses many nodes, is inefficient,...

AI Technical Summary

Problems solved by technology

Experiments have found that the weights of g(n) and h(n) will affect the overall pathfinding efficiency

When g(n) accounts for a large proportion, it is equivalent to the Dijkstra algorithm. At this time, the nodes show strong scalability and the path accuracy is higher. However, because the number of nodes increases, the amount of calculation increases and the efficiency decreases.

When h(n) accounts for a large proportion, it is equivalent to the greedy BFS algorithm. At this time, the nodes show strong goal orientation, and the efficiency is significantly improved, but the obtained path may be inaccurate.

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