Robot Destination Control Using Clustered Obstruction Areas
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Solution Overview
Problem
Existing robot navigation systems fail to handle situations where a robot cannot reach its preset destination due to obstacles, leading to potential safety hazards and disruptions in commercial environments.
Innovation Solution
A method and system that dynamically determine an alternative destination by clustering adjacent areas around the obstruction, considering the robot's size and location, to enable efficient rerouting without stopping operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot stops its movement when unable to reach the destination, then the robot can inform the user of the obstruction state, but safety accidents may occur and facility views are spoiled
Solution Approach 1:
The system performs preliminary action by proactively determining an alternative destination before the robot actually encounters the obstruction. The control server predicts potential arrival obstructions based on the robot's current destination and proactively provides alternative destinations, enabling the robot to continue operation without stopping even when obstructions are detected.
Solution Approach 2:
The control server acts as an intermediary between the robot and the destination environment. It receives information about obstructions, determines alternative destinations by clustering adjacent areas, and provides these alternatives to the robot, enabling the robot to maintain continuous operation without direct interaction with the obstruction problem.
2Measurement precision
If the robot calculates alternative destinations using complex algorithms, then accurate alternative routing can be provided, but computational load increases
Solution Approach 1:
The system segments the destination determination process into two parts: the control server performs the complex clustering and alternative destination calculation using facility map data, while the robot only needs to receive and follow the provided alternative destinations. This segmentation reduces the computational load on the robot while maintaining accurate alternative destination determination.
Solution Approach 2:
The control server performs the computationally intensive alternative destination calculation in advance, before the robot actually needs to navigate around an obstruction. By pre-calculating alternative destinations using facility map data and clustering algorithms, the system reduces real-time computational requirements for the robot.
Data Source
AI summary
There is provided a method for controlling a destination of a robot. The method includes the steps of: when information on obstruction of arrival at a first destination of a robot is acquired, determining an obstruction area associated with the arrival obstruction information by clustering adjacent areas around the first destination, determining a destination candidate area around the obstruction area with reference to a size of the robot, and determining an area in the destination candidate area, which is specified on the basis of a location of the robot, as a second destination of the robot.