In this paper we introduce a novel approach for efficient decision making under uncertainty and belief space planning, in high dimensional state spaces. While recently developed methods focus on sparsifying the infere...
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In this paper we introduce a novel approach for efficient decision making under uncertainty and belief space planning, in high dimensional state spaces. While recently developed methods focus on sparsifying the inference process, the sparsification here is done in the context of efficient decision making, with no impact on the state inference. By identifying state variables which are uninvolved in the decision, we generate a sparse version of the state's information matrix, to be used in the examination of candidate actions. This sparse approximation is action-consistent, i.e. has no influence on the action selection. Overall we manage to maintain the same quality of solution, while reducing the computational complexity of the problem. The approach is put to the test in a SLAM simulation, where a significant improvement in runtime is achieved. Nevertheless, the method is generic, and not tied to a specific type of problem.
Real-time obstacle detection is a key component of autonomous vehicles. In this context, low obstacles are particularly challenging, as they are often discarded by traditional algorithms. Curb detection methods that c...
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ISBN:
(纸本)9781509037636
Real-time obstacle detection is a key component of autonomous vehicles. In this context, low obstacles are particularly challenging, as they are often discarded by traditional algorithms. Curb detection methods that can potentially be suitable for the problem usually target roads with clearly defined curbs and sidewalks. We propose a real-time algorithm for the detection of low obstacles (including, but not restricted to curbs), merging 2-D and 3-D information from stereo imaging. A set of candidate object lines is extracted based on their combined 2-D and 3-D features, tracked over time and clustered according to a novel similarity metric. Finally, a 3rd order polynomial spline is fitted to each cluster to represent the obstacle. The proposed system can deal with noisy and incomplete point clouds and keeps the model assumptions to a minimum. To evaluate the algorithm, a new stereo dataset is provided and made available online. We present experiments in different scenarios and lighting conditions, illustrating the applicability of the method.
To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a p...
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To enable safe and efficient human-robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative replanning with the above cost function. The key to our approach is thus to learn a cost function which “explains” the motion of the human. To do this, we gather example trajectories from two participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict a human's motion for a given task by iteratively replanning a trajectory for a 23 DoF human kinematic model using the STOMP algorithm with the learned cost function in the presence of a moving collaborator. Our results suggest that our method outperforms baseline methods and generalizes well for tasks similar to those that were demonstrated.
We define the virtual infinite capacitor (VIC) as a nonlinear capacitor that has the property that for an interval of the charge Q (the operating range), the voltage V remains constant. We propose a lossless zero-volt...
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We define the virtual infinite capacitor (VIC) as a nonlinear capacitor that has the property that for an interval of the charge Q (the operating range), the voltage V remains constant. We propose a lossless zero-voltage switching realization for the VIC using a switched power converter and capacitors. This circuit is simple but it requires a complex control algorithm that we describe. There are two controllers needed to operate a VIC: the voltage controller acts fast to maintain the desired terminal voltage, while the charge controller acts more slowly and maintains the charge Q in the desired operating range by influencing the incoming current. The VIC is useful as a filter capacitor for various applications, for example power factor compensators (PFC), as we describe. In spite of using small capacitors, the VIC can replace a very large capacitor in applications that do not require substantial energy storage. We give simulation results for a PFC working in critical conduction mode with a VIC for output voltage filtering.
We define the virtual infinite capacitor (VIC) as a nonlinear capacitor that has the property that for an interval of the charge Q (the operating range), the voltage V remains constant. We propose a lossless approxima...
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We define the virtual infinite capacitor (VIC) as a nonlinear capacitor that has the property that for an interval of the charge Q (the operating range), the voltage V remains constant. We propose a lossless approximate realization for the VIC using a switched power converter and capacitors. This circuit is simple but it requires a complex control algorithm that we describe. There are two controllers needed to operate a VIC: the voltage controller acts fast to maintain the desired terminal voltage, while the charge controller acts more slowly and maintains the charge Q in the desired operating range by influencing the incoming current. The VIC is useful as a filter capacitor for various applications, for example power factor compensators (PFC), as we describe. In spite of using small capacitors, the VIC can replace a very large capacitor in applications that do not require substantial energy storage. We give simulation results for a PFC working in critical conduction mode with a VIC for output voltage filtering.
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