Sensor Placement Optimization for Robot Workspace Detection
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Solution Overview
Problem
Industrial robots in manufacturing environments face challenges in detecting objects within undetectable zones due to sensor limitations and workspace configurations, leading to incomplete object accommodation during automated tasks.
Innovation Solution
A method and system that generate a digital workspace model, simulate sensor operations, identify undetectable areas, and iteratively modify sensor configurations using a sensor placement algorithm to optimize sensor placement and minimize undetectable zones, ensuring comprehensive object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are placed to cover maximum workspace area, then detection coverage is improved, but sensor placement complexity and optimization difficulty increase
Solution Approach 1:
The system performs preliminary simulation of sensor operations and identification of undetectable areas before actual sensor deployment. By pre-analyzing the workspace model and robot trajectories, the optimization algorithm can determine optimal sensor placements in advance, avoiding complex trial-and-error adjustments during actual deployment.
Solution Approach 2:
The system creates a digital twin (workspace model) that replicates the physical workspace geometry, robot positions, and sensor characteristics. This virtual copy allows for iterative optimization of sensor placements without affecting the actual physical system, reducing deployment complexity while maximizing detection coverage.
2Measurement precision
If multiple sensors are deployed to eliminate undetectable zones, then object detection accuracy is improved, but system cost and complexity increase
Solution Approach 1:
The optimization algorithm adjusts sensor parameters (position, orientation, field of view angles) to maximize detection coverage with minimal sensors. By changing these parameters iteratively through simulation, the system achieves high detection accuracy without necessarily increasing the number of sensors.
Solution Approach 2:
The system considers dynamic robot trajectories and varying robot poses when optimizing sensor placements. Sensors are positioned to maintain detection coverage across multiple robot positions and orientations, ensuring consistent object detection accuracy throughout the workspace without requiring sensors at every possible location.
3Reliability
If sensor field of view is increased to cover more area, then detection coverage is improved, but detection precision in specific areas may deteriorate
Solution Approach 1:
The optimization algorithm assigns different priorities to different workspace regions based on object detection requirements. Critical areas receive enhanced sensor coverage with optimized positioning, while less critical areas use broader coverage sensors. This local quality differentiation maintains high detection precision where needed while achieving overall comprehensive coverage.
Data Source
AI summary
A method includes generating a workspace model having a robot based on at least one of an environment parameter and a regulated space. The workspace model is a digital model of a workspace. The method includes simulating a sensor operation of a sensor within the workspace model based on sensor characteristics of the sensor. The method includes identifying an undetectable area within the workspace model having the robot based on the simulated sensor operation. The method includes determining whether the undetectable area satisfies one or more detection metrics. The method includes performing a sensor placement control in response to the undetectable area not satisfying the one or more detection metrics to obtain a sensor placement scheme of the workspace. The sensor placement scheme identifies an undetectable area within the workspace when applicable.