Nonholonomic Robot Coverage Paths Under Turning Radius Constraints
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Solution Overview
Problem
Prior art algorithms fail to effectively handle non-holonomic robots with limited turning radius, leading to incomplete field coverage and incorrect path generation due to mislabeled cells and inability to link lanes, especially when transitioning between adjacent cells and navigating around obstacles.
Innovation Solution
The method generates a path for nonholonomic robots to cover a field by using lane linking, cell traversal, and lane wrapping algorithms, ensuring the robot follows a straight line as much as possible while avoiding obstacles and completing tasks efficiently, with the aid of sensors like GPS, RADAR, and LIDAR for navigation and obstacle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior art algorithms are used for field coverage, then the field coverage path can be generated, but the robot cannot properly navigate with limited turning radius and the coverage is incomplete
Solution Approach 1:
The field is divided into multiple cells using cellular decomposition, and the robot navigates through these cells in a systematic manner. Each cell is processed independently, allowing the path planning algorithm to account for the robot's turning constraints within each cell while ensuring complete coverage of the entire field.
Solution Approach 2:
The patent introduces the robot's turning radius as an additional dimensional constraint in the path planning process. By incorporating the minimum turning radius requirement into the cellular decomposition and lane linking algorithms, the system transforms a 2D field coverage problem into a 3D problem that includes the robot's kinematic constraints, enabling complete and feasible path generation.
2Productivity
If the robot follows straight lines as much as possible to minimize turns, then navigation efficiency is improved, but the robot must make more complex turns when necessary
Solution Approach 1:
The algorithm pre-calculates the optimal turning points and lane switching positions during the path planning phase. By determining in advance where the robot should switch between parallel lanes and how to navigate around obstacles, the system minimizes the number of turns required during execution while ensuring all turns are feasible within the robot's turning radius constraints.
3Manufacturing precision
If the robot needs to avoid obstacles and cover complex field boundaries, then coverage accuracy is improved, but the path generation becomes more difficult
Solution Approach 1:
The patent segments the field into cells and further divides each cell into parallel lanes. This hierarchical segmentation allows the algorithm to systematically handle complex boundaries and obstacles by processing each segment independently, ensuring accurate coverage while managing computational complexity through modular processing.
Solution Approach 2:
The cellular decomposition structure serves as an intermediary framework between the robot's simple parallel lane following capability and the complex field boundaries and obstacles. By introducing this intermediate cellular structure, the algorithm can systematically navigate complex environments while maintaining relatively simple control logic within each cell.
Data Source
AI summary
A software product and methods determine a field coverage method for a nonholonomic robot to process a field using parallel lanes. A cellular decomposition algorithm divides the field into a plurality of cells, each having a plurality of parallel lanes. Permutations of lane processing orders are determined for each cell, based upon a minimum turning radius of the robot. A cell graph is generated to determine a shortest path for single-time processing each lane in each cell without violating the minimum turning radius of the robot. A step list defining movement of the nonholonomic robot along each lane in each cell of the shortest path through the cell graph is generated, and transits between the lanes, and laps around the field and any obstacles are added. A path program to control the nonholonomic robot to process the field is generated based upon the step list.