The application discloses an improved fast search
random tree path
planning method based on
energy consumption constraint, and the
algorithm is as follows: modeling according to actual geographic information, drawing a
digital elevation model of a required map, determining a sampling mode of sampling points according to whether an initial feasible path can be found from a starting point to an ending point, determining a node expansion step length according to a dynamic step length strategy, generating a new expansion node, introducing a slope proportion coefficient by using a dimension reduction idea, adding an
energy consumption constraint condition to a
heuristic function, generating a logical
path length as an optimal path judgment standard, reconnecting and
rewriting a node parent node, and reaching the target point when a distance from the new node to the target node is less than a threshold value. The application effectively reduces randomness and
blindness of the sampling points, reduces a search space and a range of a search tree, considers an
energy consumption influence of an elevation slope on a
robot driving process, makes a planned path have the characteristics of being relatively flat and low in energy consumption, and improves a working efficiency of the
robot.