Simulation Modeling and Optimization Distribution Method for Large-Scale Deployment of Robot Proximity Sensor Arrays Based on Spatial Segmentation

When deploying a large-scale proximity sensor array on robots, quantifying and evaluating the perception domain becomes a challenging task due to the coupling effects of factors such as sensor type, surface curvature, robot body, robot pose, and surrounding obstacles. This article introduces a simulation modeling and optimization distribution method for the perception domain of a robot’s electronic skin with proximity sensor arrays. The method utilizes spatial segmentation techniques to simulate the perception domain space of proximity sensors. The simulation model considers the spatial morphology of sensor perception domains, robot pose, obstacle, and robot body as factors that couple with the perception domain model. Using this model, the performance of the deployed proximity sensor array on the robot was analyzed and evaluated. The optimization goal is to achieve a larger warning space (WS) coverage with fewer sensors, resulting in a 78% reduction in sensor count compared to traditional high-density distribution schemes, with only a 30% decrease in WS coverage. Experimental testing with 60 human proximity scenarios using the optimized distribution scheme showed an average simulation activation sensor count error of 6.3%. The effective sensing rate reaches 100% for the experimental test scenarios, with at least five sensors providing perceptual data for 95% of the scenarios.