Autonomous Mobile Robot Escape Path Control in Complex Obstacles
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Solution Overview
Problem
Autonomous mobile robots face inefficiencies in escaping complex areas due to poor targeting in their escape strategies, leading to reduced user experience and potential damage.
Innovation Solution
The method involves receiving near-field obstacle information from sensors, generating an obstacle position map, and searching for a passable direction based on this map to create an escape path, utilizing collision, lifting, and non-contact sensing information to guide the robot out of trapped scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous mobile robot adopts conventional escape strategies (moving backward, rotating, and moving forward), then the robot can attempt to escape from complex areas, but the escape process has poor targeting resulting in low escape efficiency
Solution Approach 1:
The robot performs preliminary actions by generating an obstacle position map before executing escape maneuvers. The controller uses sensor data to pre-analyze the environment and identify passable directions, allowing the robot to plan its escape path in advance rather than reacting randomly. This preliminary mapping and analysis enables targeted escape movements based on pre-processed spatial information.
Solution Approach 2:
The system implements feedback by continuously receiving near-field obstacle information from sensors during the escape process. The controller compares real-time sensor data with the pre-generated obstacle position map, and adjusts the escape path dynamically based on this feedback. This closed-loop control ensures the robot maintains accurate targeting throughout the escape maneuver by constantly verifying its position and obstacle locations.
2Extent of automation
If the robot works autonomously without human operation, then the robot can operate automatically in complex areas, but the robot may become trapped and unable to continue moving
Solution Approach 1:
The robot performs self-service by autonomously generating its own escape path without human intervention. The controller uses the obstacle position map and sensor feedback to independently determine passable directions and execute escape maneuvers. The system serves itself by processing its own spatial data and making autonomous decisions to resolve trapping situations, maintaining mobility continuity through self-directed problem-solving.
3Ease of operation
If the robot uses random escape movements without environmental awareness, then the robot can attempt to free itself, but the targeting is poor and user experience is affected
Solution Approach 1:
The robot performs preliminary environmental assessment by generating an obstacle position map using sensor data before executing escape maneuvers. This pre-processing of spatial information ensures the robot maintains environmental awareness throughout the escape process. The controller uses this pre-analyzed map to identify passable directions, preventing information loss while maintaining simple automated execution of the escape path.
Data Source
AI summary
A control method for an autonomous mobile robot and an autonomous mobile robot. In an automatic working method, near-field obstacle information sent by at least one sensor is received, an obstacle position map around the autonomous mobile robot is generated based on the near-field obstacle information; an escape path is generated in response to receiving the near-field obstacle information multiple times within a limited time or space, wherein generating an escape path includes: searching for a passable direction based on a current position of the autonomous mobile robot and the obstacle position map. By adopting the method, the autonomous mobile robot can quickly escape, improving working efficiency.