For a mechanical system it often arises that its planned motion will need to be corrected either to refine an approximate plan or to deal with disturbances. This paper develops an algorithmic framework allowing for fa...
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ISBN:
(纸本)9783642174513
For a mechanical system it often arises that its planned motion will need to be corrected either to refine an approximate plan or to deal with disturbances. This paper develops an algorithmic framework allowing for fast and elegant path correction for nonholonomic underactuated systems with Lie group symmetries, which operates without the explicit need for control strategies. These systems occur frequently in robotics, particularly in locomotion, be it ground, underwater, airborne, or surgical domains. Instead of reintegrating an entire trajectory, the method alters small segments of an initial trajectory in a consistent way so as to transform it via symmetry operations. This approach is demonstrated for the cases of a kinematic car and for flexible bevel tip needle steering, showing a prudent and simple, yet computationally tractable, trajectory correction.
We present an algorithm for SLAM on planar graphs. We assume that a robot moves from node to node on the graph using odometry to measure the distance between consecutive landmark observations. At each node, the robot ...
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ISBN:
(纸本)9783642003110
We present an algorithm for SLAM on planar graphs. We assume that a robot moves from node to node on the graph using odometry to measure the distance between consecutive landmark observations. At each node, the robot follows a branch chosen at random, without reporting which branch it follows. A low-level process detects (with some uncertainty) the presence of landmarks, such as corners, branches, and bumps, but only triggers a binary flag for landmark detection (i.e., the robot is oblivious to the details or "appearance" of the landmark). Under uncertainties of the robot's odometry, landmark detection, and the current landmark position of the robot, we present an E-M-based SLAM algorithm for two cases: (1) known, arbitrary topology with unknown edge lengths and (2) unknown topology, but restricted to "elementary" 1- and 2-cycle graphs. In the latter case, the algorithm (flexibly and reversibly) closes loops and allows for dynamic environments (adding and deleting nodes).
Motion estimation from stereo imagery, sometimes called visual odometry, is a well-known process. However, it is difficult to achieve good performance using standard techniques. We present the results of several years...
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ISBN:
(纸本)9783642147432
Motion estimation from stereo imagery, sometimes called visual odometry, is a well-known process. However, it is difficult to achieve good performance using standard techniques. We present the results of several years of work on an integrated system to localize a mobile robot in rough outdoor terrain using visual odometry, with an increasing degree of precision. We discuss issues that are important, for real-time, high-precision performance: choice of features, matching strategies;incremental bundle adjustment, and filtering with inertial measurement sensors. Using data with ground truth from an Risk GPS system, we show experimentally that our algorithms can track motion, in off-road terrain, over distances of 10 km, with an error of less than 10 m (0.1%).
Motion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end effector placement for articulated ...
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ISBN:
(纸本)9783642003110
Motion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end effector placement for articulated linkages. It is usually computationally too expensive to apply sampling-based planners to these problems since it is difficult to generate valid configurations. We overcome this challenge by redefining the robot's degrees of freedom and constraints into a new set of parameters, called reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the robot's number of degrees of freedom. In addition to supporting efficient sampling, we show that the RD-space formulation naturally supports planning, and in particular, we design a local planner suitable for use by sampling-based planners. We demonstrate the effectiveness and efficiency of our approach for several systems including closed chain planning with multiple loops, restricted end effector sampling, and on-line planning for drawing/sculpting. We can sample single-loop closed chain systems with 1000 links in time comparable to open chain sampling, and we can generate samples for 1000-link multi-loop systems of varying topology in less than a second.
Digital microfluidic systems (DMFS) are a class of lab-on-a-chip systems that manipulate individual droplets of chemicals on an array of electrodes. The biochemical analyses are performed by repeatedly moving, mixing,...
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ISBN:
(纸本)9783642003110
Digital microfluidic systems (DMFS) are a class of lab-on-a-chip systems that manipulate individual droplets of chemicals on an array of electrodes. The biochemical analyses are performed by repeatedly moving, mixing, and splitting droplets on the electrodes. In this paper, we characterize the tree structure of biochemical analyses and identify their minimum resource requirements, towards the design of cost and space-efficient biochips. Mixers and storage units are two primary functional resources on a DMFS biochip;mixers mix and split droplets while storage units store droplets on the chip for subsequent processing. Additional DMFS resources include input and output units and transportation paths. We present an algorithm to compute, for a given number of mixers M, the minimum number of storage units f(M) for an input analysis using its tree structure, and design a corresponding scheduling algorithm to perform the analysis. We characterize the variation of the M-depth of a tree (i.e., its minimum number of storage units f(M)) with M, and use it to calculate the minimum total size (the number of electrodes) of mixers and storage units. We prove that the smallest chip for an arbitrary analysis uses one mixer and f(I) storage units. Finally, we demonstrate our results on two example biochemical analyses and design the smallest chip for a biochemical analysis with a complete tree structure of depth 4. These are the first results on the least resource requirements of DMFS for biochemical analyses, and can be used for the design and selection of chips for arbitrary biochemical analyses.
This paper describes a novel approach for incremental learning of motion pattern primitives through long-term observation of human motion. Human motion patterns are abstracted into a stochastic model representation, w...
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ISBN:
(纸本)9783642147432
This paper describes a novel approach for incremental learning of motion pattern primitives through long-term observation of human motion. Human motion patterns are abstracted into a stochastic model representation, which can be used for both subsequent motion recognition and generation. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. As new motion patterns are observed, they are incrementally grouped together based on their relative distance in the model space. The resulting representation of the knowledge domain is a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.
We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factori...
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ISBN:
(纸本)9783642174513
We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of batch matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, that combines incremental updates with fluid relinearization of a reduced set of variables for efficiency, combined with fast convergence to the exact solution. We also present a novel strategy for incremental variable reordering to retain sparsity. We evaluate our algorithm on standard datasets in both landmark and pose SLAM settings.
In this paper we study the problem of path planning among movable obstacles, in which a robot is allowed to move the obstacles if they block the robot's way from a start to a goal position. We make the observation...
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ISBN:
(纸本)9783642003110
In this paper we study the problem of path planning among movable obstacles, in which a robot is allowed to move the obstacles if they block the robot's way from a start to a goal position. We make the observation that we can decouple the computations of the robot motions and the obstacle movements, and present a probabilistically complete algorithm, something which to date has not been achieved for this problem. Our algorithm maintains an explicit representation of the robot's configuration space. We present an efficient implementation for the case of planar, axis-aligned environments and report experimental results on challenging scenarios.
This paper presents a numerical modeling technique for dynamically modeling a human hand. We use a strand-based method of modeling the muscles. Our technique represents a compromise between capturing the full dynamics...
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ISBN:
(纸本)9783642003110
This paper presents a numerical modeling technique for dynamically modeling a human hand. We use a strand-based method of modeling the muscles. Our technique represents a compromise between capturing the full dynamics of the tissue mechanics and the need for computationally efficient representations for control design and multiple simulations appropriate for statistical planning tools of the hand. We show how to derive a strand-based model in a variational integrator context. Variational integrators are particularly well-suited to resolving closed-kinematic chains, making them appropriate for hand modeling. We demonstrate the technique first with a detailed exposition of modeling an index finger, and then extend the model to a full hand with 19 rigid bodies and 23 muscle strands. We end with a discussion of future work, including the need for impact handling, surface friction representations, and system identification.
This paper overviews a multidisciplinary research effort on the understanding of human locomotion. It addresses the computational principles of locomotion neuroscience via the geometric control of nonholonomic systems...
ISBN:
(纸本)9783642147432
This paper overviews a multidisciplinary research effort on the understanding of human locomotion. It addresses the computational principles of locomotion neuroscience via the geometric control of nonholonomic systems. We argue that a human locomotion model can be derived from a top-down approach, by exclusively looking at the shape of locomotor trajectories and by ignoring all the body biomechanical motor controls generating the motions.
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