Robotic Coverage Path Planning With Real-Time Pattern Adaptation
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Solution Overview
Problem
Current robotic devices face inefficiencies in path planning, particularly in adapting to dynamic environments and optimizing movement paths to minimize time and energy expenditure, often resulting in suboptimal coverage and increased U-turns.
Innovation Solution
A robotic path planning method that utilizes real-time sensory data to dynamically adjust coverage patterns, employing a topological graph with vertices and edges whose properties are determined at runtime based on environmental inputs, allowing the robot to iteratively evolve more efficient movement paths by maximizing rewards and minimizing penalties such as collisions and U-turns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional path planning methods are used, then the robot can complete basic coverage tasks, but the path efficiency is low with increased U-turns and time expenditure
Solution Approach 1:
The path planning system dynamically adapts the coverage pattern based on real-time sensor data and environmental conditions. The robot transitions between different movement patterns (e.g., boustrophedon, spiral, random) and adjusts parameters like strip width and turning radius dynamically, rather than following a fixed predetermined path, thereby reducing unnecessary U-turns and improving coverage efficiency
Solution Approach 2:
The system continuously receives feedback from sensors (clutter detection, boundary detection, odometry) and uses this information to iteratively optimize the coverage path. The path planning algorithm processes sensor data to identify high-value areas, adjust movement parameters, and minimize U-turns based on actual environmental conditions, creating a closed-loop control system that improves productivity while reducing time loss
2Adaptability or versatility
If the robot follows a predetermined path pattern, then the path planning is simple, but the adaptability to dynamic environments and obstacles is poor
Solution Approach 1:
The coverage area is segmented into multiple strips or regions, and the robot plans paths for each segment independently based on local environmental conditions. This allows different path patterns to be applied to different segments, improving adaptability to obstacles and dynamic changes while keeping the overall system manageable through modular path planning
Solution Approach 2:
The path planning system changes key parameters such as movement speed, turning radius, strip width, and pattern type based on environmental feedback. When obstacles or high-value areas are detected, the system adjusts these parameters dynamically to optimize coverage, thereby improving environmental adaptability without requiring complete redesign of the path planning algorithm
Data Source
AI summary
Provided is a robot-implemented, real-time, process to plan a coverage path, the process including: obtaining environment-sensor data indicating distances from the robot to surfaces in a portion of a working environment; obtaining odometry-sensor data; based on the environment-sensor data and the odometry-sensor data, determining at least a part of a coverage path of the robot through the working environment; and commanding an electric-motor driver to move the robot along the at least part of the path.