4 results about "Rapidly exploring random tree" patented technology

The global path planning method based on potential energy guidance fast search random tree algorithm belongs to the technical field of intelligent vehicle path planning, and solves the technical problem that the traditional fast search random tree algorithm can cause the planned trajectory to travel close to the obstacles. First, the position of the obstacles and the start and end positions are determined through the two-dimensional space map planned by the vehicle. For any point in the map, the potential energy value thereof is calculated, and the potential energy values of all the obstacles are superimposed to obtain the potential energy value of the arbitrary point. The potential energy values of all the arbitrary points in the two-dimensional space map are obtained in the same way. Then, the potential energy values of each point of the whole map are normalized to obtain a potential energy map matrix. The potential energy map matrix is introduced into the fast search random tree algorithm, a potential energy threshold is set, and the randomly generated points are screened. After screening, the potential energy values are introduced into the cost function of the fast search random tree algorithm, and rewiring optimization is performed, so as to perform vehicle path planning.

The invention discloses a global path planning method based on a potential energy guided fast search random tree algorithm. The method belongs to the technical field of intelligent automobile path planning. The technical problem that a traditional fast search random tree algorithm causes a planned track to run close to an obstacle is solved. The method comprises the following steps of: determining the positions of obstacles and the starting point and terminal point positions through a two-dimensional space map planned by a vehicle, calculating the potential energy value of any point in the map, superposing the potential energy values of all the obstacles to obtain the potential energy value of any point, and obtaining the potential energy values of all the points in the two-dimensional space map according to the same method; normalizing the potential energy value of each point of the whole map to obtain a potential energy map matrix; and introducing the potential energy map matrix into a fast search random tree algorithm, setting a potential energy threshold, screening random generation points, introducing the potential energy value into a cost function of the fast search random tree algorithm after screening, and performing rewiring optimization, thereby performing vehicle path planning.

A kind of seven degrees of freedom manipulator motion planning method based on hybrid multi-policy RRT, adaptive step growth strategy is introduced to fast search random tree, the node growth step length of fast search random tree in different subspace in the process of generating path is obtained;Subspace incremental sampling strategy is introduced to fast search random tree, so that fast search random tree tends to sample in unexplored area;The breadth-first search strategy is introduced to fast search random tree, the search result is refined, and invalid nodes are removed.The present application can generate different length of node growth step length according to the different obstacle density in the environment, quickly cover unknown area and get the effective motion path between initial point and target point by introducing three strategies.The present application significantly improves the motion planning efficiency of manipulator in unstructured environment, can meet the high efficiency, fast response demand in the process of precision operation, and provides convenience for subsequent speed planning and control of manipulator.

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.