Where Should I Look? Optimized Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments

Abstract

As robots operate in increasingly complex and dynamic environments, fast motion re-planning has become a widely explored area of research. In a real-world deployment, we often lack the ability to fully observe the environment at all times, giving rise to the challenge of determining how to best perceive the environment given a continuously updated motion plan. We provide the first investigation into a 'smart' controller for gaze control with the objective of providing effective perception of the environment for obstacle avoidance and motion planning in dynamic and unknown environments. We detail the novel problem of determining the best head camera behaviour for mobile robots when constrained by a trajectory. Furthermore, we propose a greedy optimisation-based solution that uses a combination of voxelised rewards and motion primitives. We demonstrate that our method outperforms the benchmark methods in 2D and 3D environments, in respect of both the ability to explore the local surroundings, as well as in a superior success rate of finding collision-free trajectories -our method is shown to provide 7.4x better map exploration while consistently achieving a higher success rate for generating collision-free trajectories. We verify our findings on a physical Toyota Human Support Robot (HSR) using a GPU-accelerated perception framework.

Research and Evaluation on an Optical Automatic Detection System for the Defects of the Manufactured Paper Cups

“…The traditional factories of the paper products can only identify the defects through visual detection, and the average manual detection time is about 2 to 3 s. However, the detection qualities are inconsistent among different inspectors, and the detection error rate is as high as 10% because of visual fatigue, which results in the unstable qualities of the manufactured paper products. Recently, with the development of Industry 4.0, only the uses of automated productions are unable to meet the requirements of the modern production processes, while the technologies of machine vision have played a very important role in modern industrial automatic productions for the detection of the products’ surface qualities [ 2 , 3 ]. Therefore, the Automatic Optical Detection (AOD) technologies of the machine visions have been boomed to process the detections of the defects on the manufactured products [ 4 , 5 ].…”