The intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that1were founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisc...
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The computational framework, based on the conformal camera, is developedfor processingvisual information during smooth pursuit movements of a robotic eye. During smooth pursuit, the image of the tracked object remains...
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Across history and cultures, robots have been envisioned as assistants working alongside people. Following this vision, an emerging family of products-collaborative manufacturing robots-is enabling human and robot wor...
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ISBN:
(纸本)9781450331456
Across history and cultures, robots have been envisioned as assistants working alongside people. Following this vision, an emerging family of products-collaborative manufacturing robots-is enabling human and robot workers to work side by side as collaborators in manufacturing tasks. Their introduction presents an opportunity to better understand people's interactions with and perceptions of a robot "co-worker" in a real-world setting to guide the design of these products. In this paper, we present findings from an ethnographic field study at three manufacturing sites and a Grounded Theory analysis of observations and interviews. Our results show that, even in this safety-critical manufacturing setting, workers relate to the robot as a social entity and rely on cues to understand the robot's actions, which we observed to be critical for workers to feel safe when near the robot. These findings contribute to our understanding of interactions with robotic products in real-world settings and offer important design implications.
The promise of "smart" homes, workplaces, schools, and other environments has long been championed. Unattractive, however, has been the cost to run wires and install sensors. More critically, raw sensor data...
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ISBN:
(纸本)9781450331456
The promise of "smart" homes, workplaces, schools, and other environments has long been championed. Unattractive, however, has been the cost to run wires and install sensors. More critically, raw sensor data tends not to align with the types of questions humans wish to ask, e.g., do I need to restock my pantry? Although techniques like computervision can answer some of these questions, it requires significant effort to build and train appropriate classifiers. Even then, these systems are often brittle, with limited ability to handle new or unexpected situations, including being repositioned and environmental changes (e.g., lighting, furniture, seasons). We propose Zensors, a new sensing approach that fuses real-time human intelligence from online crowd workers with automatic approaches to provide robust, adaptive, and readily deployable intelligent sensors. With Zensors, users can go from question to live sensor feed in less than 60 seconds. Through our API, Zensors can enable a variety of rich end-user applications and moves us closer to the vision of responsive, intelligent environments.
Visually impaired people can struggle to use everyday appliances with inaccessible control panels. To address this problem, we present ApplianceReader-a system that combines a wearable point-of-view camera with on-dem...
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ISBN:
(纸本)9781450331463
Visually impaired people can struggle to use everyday appliances with inaccessible control panels. To address this problem, we present ApplianceReader-a system that combines a wearable point-of-view camera with on-demand crowdsourcing and computervision to make appliance interfaces accessible. ApplianceReader sends photos of appliance interfaces that it has not seen previously to the crowd, who work in parallel to quickly label and describe elements of the interface. computervisiontechniques then track the user's finger pointing at the controls and read out the labels previously provided by the crowd. This enables visually impaired users to interactively explore and use appliances without asking the crowd repetitively. ApplianceReader broadly demonstrates the potential of hybrid approaches that combine human and machine intelligence to effectively realize intelligent, interactive access technology today.
Nowadays, chestnuts selection process in Peru is done by hand, therefore some important problems occur. People who work in this area could make a lot of mistakes because their personal situation or environment variabl...
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A novel pursuing algorithm, together with a new experiment method, is introduced to drive a nonholonomic mobile robot tracking a dynamic target in the framework of pursuit-evasion game. Specifically, a pan-tilt with a...
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A novel pursuing algorithm, together with a new experiment method, is introduced to drive a nonholonomic mobile robot tracking a dynamic target in the framework of pursuit-evasion game. Specifically, a pan-tilt with an on-board camera is attached to the mobile robot to construct a tracking system, which greatly improves the maneuverability compared with the traditional method. To keep the target close enough to the vision center of the camera, a control technique is devised according to its kinematic model and Lyapunov theory. By utilizing game theory, the viability set is determined. A tracking controller is employed to guarantee persistent tracking for the dynamic target, where the relative position between the target and the robot is successfully acquired from the visual measurements by the utilization of geometric analysis. Both simulation and experimental results are provided to illustrate the performance of the controller.
The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor i...
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ISBN:
(纸本)9780819494351
The main purpose of this paper is to use machine learning method and Kinect and its body sensation technology to design a simple, convenient, yet effective robot remote control system. In this study, a Kinect sensor is used to capture the human body skeleton with depth information, and a gesture training and identification method is designed using the back propagation neural network to remotely command a mobile robot for certain actions via the Bluetooth. The experimental results show that the designed mobile robots remote control system can achieve, on an average, more than 96% of accurate identification of 7 types of gestures and can effectively control a real e-puck robot for the designed commands.
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