Optimal itineration control method satisfying complex requirement
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A technology for optimal itinerary and complex requirements, applied in the fields of mobile robot control, path planning, computer image processing, and communications, and can solve problems such as the inability to meet complex task requirements.
Inactive Publication Date: 2015-08-12
ZHEJIANG UNIV OF TECH
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[0006] In order to overcome the defect that the traditional mobile robot control system cannot meet the requirements of complex tasks, the present invention will design and implement a mobile robot control method that meets the requirements of complex tasks
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Embodiment 1
[0081] by Figure 5 The switching system is an embodiment, according to the following steps to obtain the optimal path to meet the task requirements, the steps are as follows:
[0082] step 1, Figure 5 The medium switching system contains 5 nodes, the arrows between the nodes represent the conversion relationship, and the values on the connecting lines between the nodes are the weights, where q 0 As the initial node, the weighted switching system T is expressed as follows:
[0091] exist image 3 The control system shown and Figure 12 In the running environment of the robot shown, the task requirement “first reach q 2 and q 6 , and then iterate through q 15 ,q 34 and q 37 "Three nodes" is an embodiment that realizes the mobile robot tour control that meets the task requirements, and the steps are as follows:
[0092] Step 1, obtain the operating environment of the robot through the network camera, and abstract it into a corresponding switching system T 3 Such as Figure 12 shown. switch system T 3 Contains 42 nodes, node q 0 is the starting point of the robot, the connection between nodes represents the conversion relationship in the switching system, and the distance between nodes is the weight, where node q 9 ,q 11 ,q 10 ,q 18 ,q 19 ,q 30 ,q 31 is the obstacle point, node q 2 ,q 6 ,q 15 ,q 34 ,q 37 It is the node that may need to tour in the robot task;
[0093] Step 2, based on the task requirement "reach q first 2 and q...
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Abstract
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.
Description
technical field [0001] This system involves computer image processing, path planning, mobile robot control, communication and other fields, especially mobile robot tour control for complex sequential tasks. Background technique [0002] Mobile robot control methods involve many related technologies such as environment perception, dynamic decision-making and planning, behavior control and execution. Among them, intelligent mobile robot control methods that meet the needs of complex tasks have significant advantages in exploring unknown and dangerous areas that humans cannot reach, and have a wide range of application prospects, such as robot mining, security inspections in specific factory environments, and disaster relief in disaster areas. [0003] Traditional mobile robot control methods mainly focus on the research of simple task requirements such as go-to-goal, and mainly use roadmap method, cell segmentation method, artificial potential field method, artificial neural n...
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