The invention discloses an improved A* robot optimal path planning method suitable for a warehouse environment. The improved A* robot optimal path planning method comprises the steps of: firstly, designing an effective warehouse model capable of flexible expansion, including the design of shelf distribution and a road operating rule; simplifying a path planning problem into path planning problems among nodes according to the road operating rule in the warehouse mode; then adopting an improved A* algorithm to search for an optimal path between two nodes, wherein the calculation of a heuristic function includes estimation of a steering price, a Manhattan distance and bypass distance; and finally, adding a path between an initial position of a robot and an initial node as well as a path between a target position and a target node into a previous node list to form a complete path list. The nodes in the path list correspond to positions in an actual warehouse, the optimal operating path of the robot is obtained, and the operating paths of the robot are extended into an operating state list of the robot.

Improved A* robot optimal path planning method suitable for warehouse environment

A storage robot path program method based on linear temporal logic theory comprises the steps of modeling a storage environment into a weight switching system based on assignment nodes; describing complex assignment tasks of the storage system by using the linear temporal logic language (LTL); then, switching the task formula into a Buchi automatic machine through an LTL2BA tool kit; conducting cartesian product to the extensible weight switching system and the Buchi automatic machine to form a task feasible network topology; employing SPFA(Shortest Path Faster Algorithm) on the topology to search an optimal path, wherein the task feasible network topology includes storage environment information and package-taking task information, guaranteeing that the searched obtained path is the optimal path and isn't influenced by the order of the selected assignment node; mapping the obtained optimal path from the task feasible network topology to the corresponding optimal path in the storage environment, and sending the optimal path to a storage robot by radio communication through a PC to finish the packaging-taking and assignment task.

Storage robot path program method based on linear temporal logic theory

The invention discloses an algorithm for positioning control of an automatic guided vehicle (AGV) robot based on fuzzy proportion integration differentiation (PID). The algorithm comprises the following steps: a step S1 of operating environment presetting; a step S2 of establishment of an environmental map; a step S3 of AGV robot positioning; a step S4 of AGV robot line operation; and a step S5 of AGV robot operation line correction. The algorithm is more suitable for control over the AGV robot; through a mode of adding the fuzzy PID control, the response speed and control operation accuracy of the AGV robot can be improved, and the AGV robot positioning accuracy is higher.

Algorithm for positioning control of automatic guided vehicle (AGV) robot based on fuzzy proportion integration differentiation (PID)

The invention discloses a robot positioning method and system. The method comprises the steps as follows: S100, a robot advances according to a first positioning module and whether the current position of the robot is in a preset area or not is judged; S200, when the robot enters the preset areas, the robot advances in a second positioning mode, and the reference distance between the current position and a preset reference position is acquired according to the current position; S300, the coordinate information of the current position of the robot is acquired according to the reference distanceand coordinate information of the preset reference position; S400, the coordinate information of the current position of the robot is compared with coordinate information corresponding to a preset advancing route, and the advancing route of the robot is updated according to the comparison result; S500, the robot advances to a coordinate information point of a target position according to the updated advancing route.

Robot positioning method and system

The invention discloses an LTL-A*-A*optimal path planning method applicable to dynamic environment. The advantage of meeting complex mission requirements in actual application of linear temporal logic (LTL) and the advantage of being high in search efficiency of the A*algorithm are combined, and the method mainly includes LTL-A*global path optimization and local path optimization based on the A*algorithm. First, the optimal path meeting the task requirement in the environment is partially searched out through LTL-A*global path optimization; then, a robot tracks the global optimal path obtained through searching and detects environment information; finally, when local environment changes, a part segment in the global optimal path cannot continue allowing passage, the local optimal path is searched for by adopting the A*algorithm, the segment not allowing passage is bypassed, and operation continues along the global optimal path to complete an appointed task.

LTL-A*-A*optimal path planning method applicable to dynamic environment

Provided is a single mobile robot optimal itineration control method based on a target tracking control strategy. The method comprises seven steps of map construction, task description, topology construction, optimal path searching, task decomposition, target tracking control, and pose information feedback. The method comprises: firstly, based on a linear temporal logic theory, combining a constructed weighting switching system and a linear temporal logic task formula, establishing task feasible network topology; then, using an optimal path searching method based on a Dijkstra algorithm to search an optimal path, and storing discrete coordinate points in environment corresponding to the optimal path in cache, and sending the discrete coordinate points to a target tracking controller in order; then, using a target tracking control method to give a controlled quantity to control a robot to move to a target point according to distance between the robot and the target point and deviation of angles; and finally, in an operation process of the robot, using a global camera to acquire present position coordinate and orientation information of the robot in real time, and feeding back to the target tracking controller, so as to realize closed-loop control.

Single mobile robot optimal itineration control method based on target tracking control strategy

Provided is a robot optimal itineration control method satisfying complex requirements. The method comprises six steps of establishing a weighting switching system, establishing a task chart of a Buchi automata, establishing task feasible network topology of a Product automata, searching an optimal path, performing fuzzy logic control, and feeding back pose information. The method comprises: firstly, based on linear temporal logic, combining with robot operation environment information, programming the optimal path satisfying task requirements; and then, by using a fuzzy control strategy, combining with present position coordinate and orientation information of the robot fed back in real time, according to angular deviation [alpha] and distance deviation d between a present mobile robot and a target point, giving controlled quantities of speed vl and vr of a left wheel and a right wheel of the robot, so as to realize optimal path tracking control. The method can plan the optimal path which conforms to robot operation environment and satisfies complex itineration task requirements, and the method can effectively control the robot to realize optimal path tracking, so as to complete appointed tasks.

Optimal itineration control method satisfying complex requirement

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Single mobile robot optimal itineration control method based on target tracking control strategy

Storage robot path program method based on linear temporal logic theory

Optimal itineration control method satisfying complex requirement