ICRA 2015 论文列表

This paper deals with the dynamics and motion planning for a spherical rolling robot with a pendulum actuated by two motors. First a dynamic model for the rolling robot is established. In general, not all feasible kinematic trajectories of the rolling carrier are dynamically realizable. A notable exception is when the contact trajectories on the sphere and on the plane are geodesic lines. Based on this consideration, a motion planning strategy for complete reconfiguration of the rolling robot is proposed. The strategy consists of two trivial movements and a non-trivial maneuver that is based on tracing multiple spherical triangles. To compute the sizes and the number of triangles, a reachability diagram is constructed. To define the control torques realizing the rest-to-rest motion along the geodesic lines, two possible approaches are suggested. A computational algorithm, implementing the motion planning strategy, is developed and verified under simulation.

This paper describes the design and control architectures for a novel active thoracolumbosacral orthosis targeted at correction of abnormal postures and treatment of the human spine, often seen in adolescent idiopathic or neuromuscular scoliosis. Our novel device is motivated by the current limitations of the rigid braces used for this purpose which do not adapt to changes in the skeletal system in response to treatment. In addition, the dynamic brace can open possibilities for new treatment methods which currently do not exist. Previous brace designs were not capable of providing dynamic controlled forces. Our design utilizes two Stewart-Gough platforms in series, each controlled independently, either in position or force modes. The design can provide controlled forces/torques on different regions of spine to modify the posture. Additionally, it can control the motion of different regions of the spine through independent position control of each platform using six parallel actuators. Both control methods were validated in benchtop tests. A range of motion study was also performed with a healthy subject wearing the device while the system was controlled in transparent mode.

This idea introduces a tilting angle amplification system for the cable planar parallel robots. The end-effector rotation capability is increased by adding a serial linkage to the parallel platform. The hybrid serial-parallel robot has the features of both of the parallel manipulator and the serial manipulator. The manipulator is modeled in terms of full dynamics using Newton-Euler and Lagrange methods based analytical approaches.

Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the configuration space has several ‘narrow passages’. Search-based planners on the other hand typically provide good solution qualities and are not affected by narrow passages. However, in the absence of a good heuristic or when there are deep local minima in the heuristic, they suffer from the curse of dimensionality. In this work, our primary contribution is a method for dynamically generating heuristics, in addition to the original heuristic(s) used, to guide the search out of local minima. With the ability to escape local minima easily, the effect of dimensionality becomes less pronounced. On the theoretical side, we provide guarantees on completeness and bounds on suboptimality of the solution found. We compare our proposed method with the recently published Multi-Heuristic A* search, and the popular RRT-Connect in a full-body mobile manipulation domain for the PR2 robot, and show its benefits over these approaches.

Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic-Virtual-Metrology (AVM) based Original Intelligent Sampling Decision (Original ISD) scheme had been previously developed for reducing the sampling rate and sustaining the VM accuracy. However, the desired sampling rate of the Original ISD scheme is fixed and set manually. Hence, whenever the VM accuracy gets worse, it cannot dynamically increase the desired sampling rate in the Original ISD scheme. As a consequence, it would take more time to collect enough samples for improving the VM accuracy. Moreover, when the VM accuracy performs well all the time, it cannot automatically decrease the desired sampling rate in Original ISD, which may result in unnecessary waste. Accordingly, this paper proposes a preliminary study of a Dynamic ISD scheme to dynamically and automatically modify the sampling rate online and in real time. The Dynamic ISD scheme can monitor the VM accuracy on line as well as update the VM models in real time for maintaining the VM accuracy when the VM accuracy becomes poor. Also, the Dynamic ISD scheme can automatically reduce the sampling rate while the VM accuracy performs well.

While iterative optimization techniques for Simultaneous Localization and Mapping (SLAM) are now very efficient and widely used, none of them can guarantee global convergence to the maximum likelihood estimate. Local convergence usually implies artifacts in map reconstruction and large localization errors, hence it is very undesirable for applications in which accuracy and safety are of paramount importance. We provide a technique to verify if a given 2D SLAM solution is globally optimal. The insight is that, while computing the optimal solution is hard in general, duality theory provides tools to compute tight bounds on the optimal cost, via convex programming. These bounds can be used to evaluate the quality of a SLAM solution, hence providing a “sanity check” for state-of-the-art incremental and batch solvers. Experimental results show that our technique successfully identifies wrong estimates (i.e., local minima) in large-scale SLAM scenarios. This work, together with [1], represents a step towards the objective of having SLAM techniques with guaranteed performance, that can be used in safety-critical applications.

This paper presents general modal dual Quaternion (DQ) kinematics for multisection continuum arms. DQ's produce higher accuracy than homogeneous transformation matrices (HTM) when transformed to modal shape functions (MSF) of similar order and are numerically stable. Thus, the model is compact, more accurate and computationally efficient than the modal kinematics proposed by the author based on HTM's. Also, DQ kinematics does not suffer from singularity related limitations of Euler angle based inverse orientation kinematics. Recursive schemes for deriving DQ's and DQ Jacobians are also presented and can be extended arbitrarily. Both modal HTM and modal DQ kinematics are then applied to solve illustrative spatial inverse position and orientation tracking problems. Based on the results, this paper quantitatively compares both methods and highlights the advantages of modal DQ kinematics. The proposed DQ kinematics are easily extensible to variable length multisection continuum arm with general actuator configurations.

This paper presents a development of a new method dedicated to the modeling and control of Continuum Robots, based on the Finite Element Method (FEM) using quasi-static assumption. The modeling relies on a discretization of the continuum robots using 6 DoFs Frames along the structure of the robot that is compatible with the modeling of a sequence of rigid vertebras. When the robot's structure relies on rods with constant sections, internal forces are computed with beam elements, placed between two adjacent frames, that applies forces and torques. In the opposite, when the robot is composed of a complex shape deformable backbone separated by the rigid vertebras, a domain decomposition strategy is used to obtain an equivalent stiffness between two vertebras using volumetric FEM. In both cases, for solving the whole robot model and inverting it in real-time, the numerical method takes advantage of the serial nature of continuum robots, using a Block-Tri-Diagonal solver. The factor of improvement in the computation time reaches several order of magnitude compared to a classical FEM model, while keeping a good precision. The method has also been implemented and tested on a real pneumatic CBHA trunk designed by Festo Robotics and some complementarity examples have been generated numerically.

We investigate the task-optimality of legged limit cycles and present numerical evidence supporting a simple general locomotion-planning template. Limit cycles have been foundational to the control and analysis of legged systems, but as robots move toward completing real-world tasks, are limit cycles practical in the long run? We address this question both figuratively and literally by solving for optimal strategies for long-horizon tasks spanning as many as 20 running steps. These scenarios were designed to embody practical locomotion tasks, such as evading a pursuer, and were formulated with minimal constraints (complete the task, minimize energy cost, and don't fall). By leveraging large-scale constrained optimization techniques, we numerically solve the trajectory for a reduced-order running model to optimally complete each scenario. We find, in the tested scenarios in flat terrain, that near-limit-cycle behaviors emerge after a transient period of acceleration and deceleration, suggesting limit cycles may be a useful, near-optimal planning target. On rough terrain, enforcing a limit cycle on every step only degrades gait economy by 2-5% compared to optimal 20-step look-ahead planning. When perturbing the scenario with a single “bump” in the road, the model converged in a manner giving the appearance of an exponentially stable orbit, despite not explicitly enforcing exponential stability. Further, we show that the transient periods of acceleration and deceleration may be near-optimally approximated by planning with a simple “sliding mass” template. These results support the notion that limit cycles can be useful approximations of task-optimal behavior, and thus are useful near-term targets for long-term planning.

We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.

Simultaneous localization and mapping (SLAM) algorithms allow a single robot to reduce the effects of drifting sensor biases while exploring unknown, GPS-denied environments. To reduce exploration time, a team of robots can build smaller maps in parallel and perform map fusion. Most map fusion techniques require a known relative transformation between coordinate frames. Other techniques rely on inter-robot detections to estimate an initial transformation. However, large environments with sparse robot coverage may necessitate alternative techniques when robots are in communication, but not, sensor range. In this paper, we address the map fusion problem with unknown relative transformations between robot pairs. We use the probabilistic hypothesis density (PHD) SLAM algorithm to track features within a static, simulated environment and propose two techniques for distributed map matching: a RANSAC based congruent triangle matching algorithm and an earth mover's distance (EMD) based assignment algorithm.

This paper presents four distributed motion controllers to enable a group of robots to collectively transport an object towards a guide robot. These controllers include: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path. Three of these controllers require an estimate of the centroid of the object, to use as the axis of rotation. Assuming the object is surrounded by manipulator robots, we approximate the centroid of the object by measuring the centroid of the manipulating robots. Our algorithms and controllers are fully distributed and robust to changes in network topology, robot population, and sensor error. We tested all of the algorithms in real-world environments with 9 robots, and show that the error of the centroid estimation is low, and that all four controllers produce reliable motion of the object.

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to quadratic mirrors involved. Conventional visual features developed based on the perspective model are hard to achieve a satisfactory performance when directly applied to the distorted omnidirectional image. This paper presents a parameterized neighborhood model to efficiently calculate the adaptive neighborhood of an object based on the measurable radial distance in image plane. On the basis of the parameterized neighborhood model, a distortion invariant joint-feature framework implemented with contour-color fragment mixture model of Gaussian is proposed for visual tracking in catadioptric omnidirectional camera system. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into feature clustering. A weight contribution mechanism is presented to flexibly weight the fragments based on their responses, which makes the system robustly guided by limited visible fragments even when serious partial occlusion happens. The experiments validate the performance of the proposed algorithm.

The distance metric is a key component in RRT-based motion planning that deeply affects coverage of the state space, path quality and planning time. With the goal to speed up planning time, we introduce a learning approach to approximate the distance metric for RRT-based planners. By exploiting a novel steer function which solves the two-point boundary value problem for wheeled mobile robots, we train a simple nonlinear parametric model with constant-time inference that is shown to predict distances accurately in terms of regression and ranking performance. In an extensive analysis we compare our approach to an Euclidean distance baseline, consider four alternative regression models and study the impact of domain-specific feature expansion. The learning approach is shown to be faster in planning time by several factors at negligible loss of path quality.

Distance Bound Smoothing (DBS) is a basic operation originally developed in Computational Chemistry to determine point configurations that are within certain pairwise ranges of distances. This operation consist in the iterative application of filtering processes that reduce the given ranges using triangular and tetrangular inequalities. Standard DBS has a limited range of applications because it does not take into account constraints on the orientations of simplices (triangles or tetrahedra, depending on the dimension of the problem). This paper discusses an extension of DBS that permits incorporating these constraints. This paves the way for the application of DBS techniques to a broad range of problems in Robotics.

Automatic discovery of topical objects from a set of image collections provides more strong cognitive capability of robot to understand the unstructured environment. In this paper, we propose a novel framework based on dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method admits a great advantage that the interested objects can be more reliably segmented. A dictionary learning method is proposed to discover the topical objects. Such an optimization model exploits the observation that any image only includes a few topical objects and therefore sparsity is encouraged. Further, a globally convergent algorithm is developed to solve the dictionary learning problem and extensive experiments show that the proposed method outperforms the state-of-the-arts.

This paper introduces the phenomenon of directional efficiency, a property of spur gear transmissions in which the coefficient of torque dependent efficiency is function of the power flow direction; the efficiency in the step-up direction differs from that of step-down. Understanding the directional efficiency is important in robot design where the mechanical impedance of the transmission significantly affects the dynamics of physical interaction with the environment. This paper is the first to address `backdrivability' by investigating directional efficiency on a high gear ratio robotic actuator, industrial servo gearboxes and over a family of gears. The kinematic model shows that a single pair of gears operates at higher efficiency in step-down than in step-up and the effect is confirmed in experiment, albeit with discrepancy in the magnitude of difference. Experimental results show that step-down and step-up directional efficiency are 98% and 96% respectively for a PIC DESIGNS 6:1 gearbox over the range of tested velocities. Additionally the overall energy efficiencies of the Dynamixel robotic servo motor with a gear ratio of 248:1 are approximately 75% and 70% for step-down and step-up respectively over a range of tested velocities. For the Dynamixel servo actuator, modeling with directional efficiency coefficients reduces error in predicted output force to 2-4% compared to a 4-8% with a single efficiency coefficient and 30% for models without friction.

Classically Visual servoing considered the regulation in the image of a set of visual features (usually geometric features). Recently direct visual servoing scheme, such as photometric visual servoing, have been introduced in order to consider every pixel of the image as a primary source of information and thus avoid the extraction and the tracking of such geometric features. Previous works proposed methods to use directly the image intensities in the definition of the control law, by using for example mutual information. In this paper, we propose a method to extend these works by using a global descriptor, namely intensity histograms, on the whole or multiple sub-sets of the images in order to achieve control of a 6 degrees of freedom (DoF) robot. The results are then demonstrated through experimental validations.

This paper studies an optimization-based approach for solving optimal estimation and optimal control problems through a unified computational formulation. The goal is to perform trajectory estimation over extended past horizons and model-predictive control over future horizons by enforcing the same dynamics, control, and sensing constraints in both problems, and thus solving both problems with identical computational tools. Through such systematic estimation-control formulation we aim to improve the performance of autonomous systems such as agile robotic vehicles. This work focuses on sequential sweep trajectory optimization methods, and more specifically extends the method known as differential dynamic programming to the parameter-dependent setting in order to enable the solutions to general estimation and control problems.