Robot Coverage Planning with Maximum Envelope Regions
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Solution Overview
Problem
Existing full-coverage path planning methods for cleaning robots suffer from low efficiency, weak adaptability, and limited application scenarios due to human error in manual teaching and environmental uncertainty in boundary-based methods.
Innovation Solution
A method and apparatus that involves acquiring a training trajectory and environment map, generating a maximum envelope region for autonomous task completion, and controlling the robot to traverse this region until tasks are finished, using a robot with a memory and processor to execute computer-executable instructions for efficient and adaptable task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual teaching method is used for full coverage path planning, then the robot can adapt to complex structured scenarios, but the cleaning efficiency decreases due to path duplication and human error
Solution Approach 1:
The system performs preliminary actions by first teaching only the boundary path of the cleaning area, rather than the complete cleaning trajectory. This preliminary boundary teaching enables the robot to subsequently autonomously generate the full cleaning path, reducing manual intervention while maintaining adaptability to complex scenarios.
Solution Approach 2:
The robot employs self-service by autonomously generating its cleaning path based on the taught boundary and environmental sensors. The path planning algorithm automatically computes the optimal cleaning trajectory without human intervention, eliminating path duplication errors while maintaining adaptability to complex structured scenarios.
2Productivity
If boundary full coverage method is used for autonomous cleaning, then the cleaning efficiency improves, but the adaptability to complex structured scenarios becomes limited
Solution Approach 1:
The system applies dynamics by implementing a dynamic path planning algorithm that adapts to environmental features in real-time. The robot modifies its cleaning trajectory based on sensor feedback and detected obstacles, enabling it to handle complex structured scenarios while maintaining high cleaning efficiency through autonomous adaptation.
Solution Approach 2:
The robot uses feedback from environmental sensors and obstacle detection systems to continuously adjust its cleaning path. This feedback mechanism enables the robot to adapt to complex structured scenarios autonomously, overcoming the limitations of static boundary-based methods while maintaining high cleaning efficiency.
3Adaptability or versatility
If manual teaching method is used for path planning, then the adaptability to environment is improved, but the teaching time and complexity increase
Solution Approach 1:
The system performs only preliminary boundary teaching instead of complete path teaching, significantly reducing teaching time. The robot then autonomously generates the full cleaning trajectory based on the boundary constraints and environmental sensors, maintaining environmental adaptability while minimizing manual intervention time.
Solution Approach 2:
The system extracts only the essential boundary information through manual teaching, separating this from the complete path planning task. The robot then autonomously computes the detailed cleaning trajectory by extracting and processing environmental features, reducing teaching complexity while preserving adaptability.
Data Source
AI summary
The present application discloses a robot task execution method, apparatus, robot and storage medium. The method comprises: acquiring a training trajectory and an environment map in a training mode; generating a target region for tasks to be performed by a robot based on the environment map and the training trajectory, wherein the target region is a maximum envelope region in which the robot can complete tasks autonomously; controlling the robot to traverse the target region until the robot completes the tasks to be performed. By adopting the above technical solution, the robot can perform tasks stably and efficiently in various environmental regions, thereby being able to be applied to various application scenarios.