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Titlebook: Robotics Research; Volume 1 Antonio Bicchi,Wolfram Burgard Book 2018 Springer International Publishing AG 2018 Robotics future.Robotics Res

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51#
發(fā)表于 2025-3-30 10:47:05 | 只看該作者
52#
發(fā)表于 2025-3-30 15:22:25 | 只看該作者
53#
發(fā)表于 2025-3-30 18:16:36 | 只看該作者
54#
發(fā)表于 2025-3-30 22:39:21 | 只看該作者
Computer-Aided Compositional Design and Verification for Modular Robots We present a design framework that facilitates rapid creation of new configurations and behaviors through composition of existing ones, and tools to verify configurations and behaviors as they are being created. New configurations are created by combining existing sub-configurations, for example co
55#
發(fā)表于 2025-3-31 04:07:01 | 只看該作者
56#
發(fā)表于 2025-3-31 07:49:35 | 只看該作者
Design, Sensing, and Planning: Fundamentally Coupled Problems for Continuum Robotson were initially thought of as decoupled problems for continuum robots. However, a body of literature is beginning to emerge showing advantages in solving various combinations of two of these three problems simultaneously. In this paper we argue that all three of these problems are fundamentally co
57#
發(fā)表于 2025-3-31 10:55:37 | 只看該作者
58#
發(fā)表于 2025-3-31 13:39:00 | 只看該作者
Analytical SLAM Without Linearizationements and radial measurements to optical flows and time-to-contact measurements. As shown in simulations, the proposed algorithm is simple and fast, and it can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context.
59#
發(fā)表于 2025-3-31 21:26:10 | 只看該作者
Exploiting Photometric Information for Planning Under Uncertaintyles (MAVs) that perform perception-aware path planning in real-time during exploration. We show significantly reduced pose uncertainty over trajectories planned without considering the perception of the robot.
60#
發(fā)表于 2025-4-1 01:14:24 | 只看該作者
Optimal-State-Constraint EKF for Visual-Inertial Navigation vector. To this end, for each sliding window, we perform structure and motion using only the available camera measurements and subsequently marginalize out the structure (features) to obtain the optimal motion constraints that will be used in the EKF update. The proposed approach is validated in the proof-of-concept, real-world experiments.
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