Rapid Human‐Assisted Creation of Bounding Models for Obstacle Avoidance in Construction

Abstract

Abstract: State‐of‐the‐art construction equipment control technology creates the opportunity to implement automated and semiautomated object avoidance for improved safety and efficiency during operation; however, methods for constructing models of local objects or volumes in real‐time are required. A practical, interactive method for doing so is described here. The method: (1) exploits a human operator's ability to quickly recognize significant objects or clusters of objects in a scene, (2) exploits the operator's ability to acquire sparse range point clouds of the objects quickly, and then (3) renders models, such as planes, boxes, and generalized convex hulls, to be displayed graphically as visual feedback during equipment operation and/or for making proximity calculations in an obstacle detection system. Experiments were performed in which test subjects were asked to model objects of varying complexity and clutter. These models were then compared to control models using a ray‐tracing algorithm to determine the operator's ability to create conservative models that are critical to construction operations. To demonstrate the applicability of the modeling method to obstacle avoidance, a scripted motion robot simulation was conducted using an artificial potential formulation that monitors position (closest point on manipulator link to nearest obstacle) as well as velocity (link inertia). Experimental results indicate that bounding models can be created rapidly and with sufficient accuracy for obstacle avoidance with the aid of human intelligence and that human‐assisted modeling can be very beneficial for real‐time construction equipment control.