Among various sensors commonly used in localization, inertial sensors are less affected by the surrounding environment. It achieved more efficient re-grabbing by waste cranes than the conventional automated system.ĭNN-Based Velocity Estimator Using Inertial Sensor for Robot LocalizationĪbstract: This paper presents an inertial sensor-based localization method using a deep neural network (DNN)-based velocity estimator. We confirmed the proposed method could learn re-grabbing decisions from the autonomously collected data. The effectiveness is verified in a robotic waste crane system with an in-bucket camera. Moreover, the weight estimator model and the re-grabbing policy model are designed based on a Bayesian manner for data efficiency. To simplify the decision process of re-grabbing, we separate the re-grabbing decision system from the in-bucket image into grabbed garbage weight estimation and the re-grabbing decision policy based on estimated garbage weight. For this limitation, we propose a re-grabbing decision system with feedback from in-bucket camera images for the efficiency of waste crane automation by introducing the vision sensor in the bucket. The lifting motion after grabbing makes the crane motion inefficient. However, the current automation system has to lift up the bucket to measure grabbed garbage weight to decide re-grab. When deciding whether to re-grab garbage by a waste crane to grab more garbage, human operators are efficiently making decisions based on visual information. In particular, data-driven learning approaches are desirable for waste crane automation. Keywords: Intelligent robotics, Perception and sensingĪbstract: The automation of waste cranes has been demanded to perform garbage incineration work with fewer workers efficiently. Learning Re-Grabbing Policies Based on Grabbed Garbage Weight Estimation Using In-Bucket Images for Waste Cranes
Finally, the exploration performance of the proposed controller is demonstrated by using a polishing skill on an unknown 3D surface, where the robot is observed to autonomously investigate the unknown surface from top to bottom along the edges and corners. Second, we use the proposed controller to autonomously investigate the unknown environment via the local curvature observer, designed to be a dynamic process. First, we develop a unified force-impedance control approach in which the force controller significantly improves the geometry following performance due to the ensured contact. for updating predefined tactile skill policies accordingly. To address this, we propose a tactile exploration technique to observe the local curvatures of the physical constraints such as corners, edges, etc. Such robots should therefore be designed to be adaptive to environmental uncertainties such as changing geometry and contact-loss. Keywords: Intelligent robotics, Autonomous robotic systems, Robotics technologyĪbstract: Tactile robots can perform complex interaction skills, e.g., polishing. Tactile Exploration Using Unified Force-Impedance Control We show results in simulation for the surface reconstruction and volume estimate of topographic data.
Our algorithm finds feasible trajectories which minimize the uncertainty of the volume estimate. A Gaussian Process-based model incorporates information collected from measurements given statistical prior information about the terrain, from which the volume estimate is computed. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. Keywords: Information and sensor fusion, Intelligent robotics, Mobile robotsĪbstract: Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. The answer to the latter question is clearly more important for engineering, for design and for control!ĭrone-Based Volume Estimation in Indoor Environments “But if resilience is a quality the essential question becomes just “how does resilience come about”. Whether resilience is one or the other also leads to different questions, if resilience is assumed to be a quantity, the essential question is simply “how much resilience is there”. Namely, that we cannot measure something unless we fully understand it first. quantitative knowledge is almost always better.” Since 1891, We have therefore conventionally but wrongly assumed that we cannot understand something unless we can measure it, even though it actually is the other way around. It is essential because we tend tacitly to accept the statement of William Thompson (aka Lord Kelvin) that:”Qualitative knowledge is real, but. Keywords: Cognitive aspects of automation systems and humansĪbstract: The question is essential and not just rhetorical.
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