Most existing approaches to autonomous driving fall into one of two categories: Modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to contr...
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Combination of optimal control methods and machine learning approaches allows to profit from complementary benefits of each field in control of robotic systems. Data from optimal trajectories provides valuable informa...
Combination of optimal control methods and machine learning approaches allows to profit from complementary benefits of each field in control of robotic systems. Data from optimal trajectories provides valuable information that can be used to learn a near-optimal state-dependent feedback control policy. To obtain high-quality learning data, careful selection of optimal trajectories, determined by a set of start states, is essential to achieve a good learning performance. In this paper, we extend previous work with new comple-menting strategies to generate start points. These methods complement the existing approach, as they introduce new criteria to identify relevant regions in joint state space that need coverage by new trajectories. It is demonstrated that the extensions significantly improve the overall performance of the previous method in simulation on full nonlinear dynamics model of the industrial Manutec r3 robot arm. Further, it is demonstrated that it suffices to learn a policy that reaches the proximity of the goal state, from where a PI controller can be used for stable control reaching the final system state.
Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computervision. In this paper, we presented a traffic sign classification system implemented using a hybrid qu...
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Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computervision. In this paper, we presented a traffic sign classification system implemented using a hybrid qu...
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In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both La...
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In this paper, we present a generalized framework for robustly operating previously unknown cabinets in kitchen environments. Our framework consists of the following four components: (1) a module for detecting both Lambertian and non-Lambertian (i.e. specular) handles, (2) a module for opening and closing novel cabinets using impedance control and for learning their kinematic models, (3) a module for storing and retrieving information about these objects in the map, and (4) a module for reliably operating cabinets of which the kinematic model is known. The presented work is the result of a collaboration of three PR2 beta sites. We rigorously evaluated our approach on 29 cabinets in five real kitchens located at our institutions. These kitchens contained 13 drawers, 12 doors, 2 refrigerators and 2 dishwashers. We evaluated the overall performance of detecting the handle of a novel cabinet, operating it and storing its model in a semantic map. We found that our approach was successful in 51.9% of all 104 trials. With this work, we contribute a well-tested building block of open-source software for future robotic service applications.
The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to robotics and Artificial Intelligence (RAI) in order to tackle lifecycle ser...
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The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.
This paper addresses the problem of moving obstacle detection for autonomous mobile robots in unknown urban environments through the fusion of (vehicle-mounted) forward looking laser and vision sensors. In this approa...
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This paper addresses the problem of moving obstacle detection for autonomous mobile robots in unknown urban environments through the fusion of (vehicle-mounted) forward looking laser and vision sensors. In this approach we reparameterize the 2D gaussian distribution of the laser free-configuration eigenspaces by vision saliency gaussian kernel function. The approach uses bi-sensor paradigm to achieve greater effective mapping of the environment and improved accuracy in obstacle position estimation. Where the laser lower dimensional manifolds provide an eigenvector which corresponds to the free configuration space of the high order geometric representation of the environment and vision based edge detection followed by the saliency mapping provides the road detection and existance of dynamic obstacles on the road. We have shown that while the vectorial combination of eigenvectors at discrete time scan-frames of laser data manifest a trajectory, and once followed and fused with the vision sensor data, enables mobile robot to build an efficient and accurate online environment map free of obstacles. We demonstrated this process using real-time NAVLAB CMU (Autonomous Jeep's) data-set which is a good representation of autonomous mobile robot's navigation in an urban environment.
We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-...
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作者:
Vladimir PopovUral Federal University
Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg Russia
Different planning problems for mobile robots are of considerable interest for many years. Frequently, such problems require to solve different hard computational problems. Multi-robot forest coverage is one of such c...
Different planning problems for mobile robots are of considerable interest for many years. Frequently, such problems require to solve different hard computational problems. Multi-robot forest coverage is one of such computational problems. In this paper, we consider an efficient approach to solve the problem of multi-robot forest coverage for unweighted terrain. In particular, we consider an explicit reduction from the decision version of the problem of multi-robot forest coverage for unweighted terrain to the satisfiability problem. For different satisfiability algorithms, the results of computational experiments are presented.
Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level a...
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