LAUSR

At present, the path-planning algorithm based on the grid map is generally adopted in the field of quadruped robot and the obtained environmental information is represented by a standardized grid map. In this paper, the ADFA* algorithm introduces a dilation factor based on the DFA* to solve the path planning problem under the constraint of computing time and provide a path search result related to the time limit. Path-planning algorithms based on raster maps often equate robots with particles, causing problems, such as path blocking. The FA* algorithm adds raster tolerance to expand obstacles. DFA* uses a path-splitting approach that, such as the DA* algorithm, has better dynamic environment processing capabilities than the FA*. However, during the actual operation of the robot, the environmental information acquired is extremely frequent due to its instability. The robot itself is often in a relatively static state. Therefore, compared with obtaining the shortest path, it is more practical to improve the path search efficiency under dynamic map environment. ADFA* will gradually optimize the path and eventually get the optimal solution when time is sufficient. When the time is limited, ADFA* will search for the current optimal solution under the specified search time but may not be able to obtain the shortest path, which is called the second best solution.