ICRA 2015 论文列表

Walking with 5-link model has been achieved by HZD (Hybrid Zero Dynamics) controller based on virtual constraints. These holonomic constraints are obtained by optimizing a set of virtual relations (e.g., Beziér polynomial) between system states which mostly do not have physical interpretations. In this paper, the virtual constraints are designed using human walking experiment data. Inspiring from human locomotion, different polynomials are extracted to mimic human joint angles patterns during walking. The virtual leg angle is the increasing variable which synchronize the joints angles and defines the virtual constraints. Simulation results show that stable locomotion with leg and upper-body behavior similar to human experiment data is achieved for a wide range of speeds and body configuration parameters. VPP (Virtual Pivot Point) concept, a significant balancing feature found in human/animal locomotion, is investigated for different gait speeds as a performance index to compare the kinetic behavior of the simulated and human walking. Hence, we present human-like posture control as an outcome of motion control achieved by HZD with human inspired virtual constraints.

For an underactuated biped on a constant-slope terrain, the hybrid zero dynamics (HZD) controller framework provides exponentially stable walking motions. In this paper, we quantify the stability of such a control system on rough terrain by estimating the expected number of steps before failure. In addition, we show how to switch between multiple HZD controllers (optionally using terrain look-ahead) to increase the stability dramatically, e.g., 10 thousand steps compared to 10. To do this robustly, we make use of the new meshing method proposed in this paper.

This paper proposes an actuation mechanism of robotic arms using rotational counterweights. The robotic arm is driven by gravitational and inertia forces of the counterweight mounted on links. The joints of the robotic arms are able to rotate freely though this robot does not have any actuators on joints. Thereby, the robot has high joint flexibility. Additionally, the counterweight system reduces the moment applied to base link caused by gravity effect since the posture of the arm keeps a gravitational equilibrium state statically. Therefore, it is expected that the robotic arm is utilized in narrow and high altitude environment or on small vehicles. This paper also proposes a control method of the new robotic arm. The control method realizes precise position control of joint angle. The usefulness of the method is demonstrated by numerical simulations and experiments using a prototype.

In this paper a novel aerial manipulation system is proposed. The mechanical structure of the system, the number of thrusters and their geometry will be derived from technical optimization problems. The aforementioned problems are defined by taking into consideration the desired actuation forces and torques applied to the end-effector of the system. The framework of the proposed system is designed in a CAD Package in order to evaluate the system parameter values. Following this, the kinematic and dynamic models are developed and an adaptive backstepping controller is designed aiming to control the exact position and orientation of the end-effector in the Cartesian space. Finally, the performance of the system is demonstrated through a simulation study, where a manipulation task scenario is investigated.

Powered exoskeletons can provide motion enhancement for both healthy and physically challenged people. Upper limb exoskeletons are required to have multiple degrees-of-freedom and can still produce sufficient force to augment the upper limb motion. The design using serial mechanisms usually results in a complicated and bulky exoskeleton that prevents itself from being wearable. This paper presents a new exoskeleton design aimed to achieve compactness and wearability. We consider a shoulder exoskeleton that consists of a parallel spherical mechanism with two slider crank mechanisms. The actuators can be placed on a stationary platform and attached closely to human body. Thus a better inertia property can be obtained while maintaining lightweight. Through the use of a gravity-balancing mechanism, the required actuator power becomes smaller and with better efficiency. A static model is developed to analyze and optimize the exoskeleton. Through illustrations of a prototype, the exoskeleton is shown to be wearable and can provide adequate motion enhancement of a human's upper limb.

Artificial tactile sensing is still underdeveloped, especially in sensing shear and slip on a contact surface. For a robot hand to manually explore the environment or perform a manipulation task such as grasping, sensing of shear forces and detecting incipient slip is important. In this paper, we introduce a method of sensing the normal, shear and torsional load on the contact surface with a GelSight tactile sensor [1]. In addition, we demonstrate the detection of incipient slip. The method consists of inferring the state of the contact interface based on analysis of the sequence of images of GelSights elastomer medium, whose deformation under the external load indicates the conditions of contact. Results with a robot gripper like experimental setup show that the method is effective in detecting interactions with an object during stable grasp as well as at incipient slip. The method is also applicable to other optical based tactile sensors.

Laser range scanners are commonly used in mobile robotics to enable a robot to sense the spatial configuration of its environment. In addition to the range measurements, most scanners provide remission values, representing the intensity of the returned light pulse. These values add a visual component to the measurement and can be used to improve reasoning on the data. Unfortunately, a remission value does not directly tell us how bright a measured surface is in the infrared spectrum. Rather, it varies with respect to the incidence angle and the range at which it was measured. In addition, multiple scanners typically do not agree upon the values of a certain surface. In this paper, we present a calibration method for remission values of multiple laser scanners considering dependencies in range, incidence angle of the measured surface, and the respective scanner unit. Our system learns the calibration parameters based on a set of registered point clouds. It uses a graph optimization scheme to minimize the error between different measurements, so that all involved scanners yield consistent reflection values, independent of the perspective from which the corresponding surface is observed.

This paper reformulates an optimization algorithm previously presented in continuous-time to one using structured integration and structured linearization methods from discrete mechanics. The objective is to synthesize trajectories for dynamic robotic systems that improve the estimation of model parameters by using a metric on Fisher information in a nonlinear projection-based trajectory optimization algorithm. A simulation of a robot with a suspended double pendulum is used as an example system to illustrate the algorithm. Results from the simulation show that the change to a discrete mechanics formulation reduces the computation time by a factor of 19 when compared to the continuous algorithm while maintaining the same two orders of magnitude improvement in the Fisher information from the continuous-time formulation. Through the Cramer-Rao bound, the improvement in the Fisher information results in a maximum expected error reduction of the parameter estimates by up to a factor of 102.

This paper addresses a novel computation method of the Maximal Output Admissible (MOA) set for a limit cycle controller and its application to motion transition. By approximately calculating the MOA set via sample point cloud, we can obtain an analytic form of the MOA set even on a nonlinear system. Formulation provided in this paper can be applicable to various types of controllers. Using the MOA set, we demonstrate a motion transition from a standing posture to steady walking. Switching two types of controllers based on the MOA set realizes motion transition with the COP constraint satisfied. The validity of proposed method is verified with a simulation.

Large scale, long-term, distributed mapping is a core challenge to modern field robotics. Using the sensory output of multiple robots and fusing it in an efficient way enables the creation of globally accurate and consistent metric maps. To combine data from multiple agents into a global map, most existing approaches use a central entity that collects and manages the information from all agents. Often, the raw sensor data of one robot needs to be made available to processing algorithms on other agents due to the lack of computational resources on that robot. Unfortunately, network latency and low bandwidth in the field limit the generality of such an approach and make multi-robot map building a tedious task. In this paper, we present a distributed and decentralized back-end for concurrent and consistent robotic mapping. We propose a set of novel approaches that reduce the bandwidth usage and increase the effectiveness of inter-robot communication for distributed mapping. Instead of locking access to the map during operations, we define a version control system which allows concurrent and consistent access to the map data. Updates to the map are then shared asynchronously with agents which previously registered notifications. A technique for data lookup is provided by state-of-the-art algorithms from distributed computing. We validate our approach on real-world datasets and demonstrate the effectiveness of the proposed algorithms.

We consider the manipulation planning problem of an extensible elastic rod in collision-free or contact space. We assume the rod can be handled by grippers either at both or at only one of its extremities and during the manipulation, the grasped end may change. We show that the use of both quasi-static and dynamic models can be coupled efficiently with sampling-based methods. By sampling directly on the submanifold of static equilibrium and contact-free configurations, we can take advantage of the dynamic model to improve the exploration in the state space. We show the necessity of considering contacts for this type of problems with several simulation experiments on various scenarios.

In our previous work [1], the potential benefits of Magneto-Rheological Fluid based actuators to the field of haptics were studied. Our results showed that the superior mechanical attributes of such actuators contribute to improvement of stability and transparency in haptic devices. To this end, a novel design of a small-scale MRF-based clutch, was proposed in [1]. This paper reports on the development and validation of the proposed MRF-based clutch. In addition, a closed-loop torque control strategy is presented. The feedback signal used in this control scheme comes from the magnetic field measurement and is used to compensate for the nonlinear behavior using an estimated model, based on Artificial Neural Networks (ANNs). Such a control strategy eliminates the need for torque sensors for providing feedback signals. The performance of the developed design and the effectiveness of the proposed modeling and control techniques are experimentally validated. The results clearly demonstrate that the clutch shows great potential for use in a multiple degrees-of-freedom (DOF) haptic interface for a class of medical applications.

This paper presents the design, kinematics, fabrication, and magnetic manipulation of a milli-gripper for medical applications. The design employs a permanent magnet for two purposes. It actuates the compliant gripper and allows for maneuverability of the milli-gripper in an externally applied magnetic field generated by an electromagnetic manipulation system. The modular milli-gripper can be manipulated directly or attached to the distal tip of a magnetically steered catheter. Experiments show successful actuation of the gripper and guidance of the device with the integrated gripper in both the tethered and untethered configuration.

This paper describes a magnetic resonance imaging (MRI) compatibility assessment of our lower-extremity motion simulator called LoMS which provides gait-like motion for a wearer within his/her lying posture during functional MRI (fMRI) imaging. We confirmed that the existence and the movement of LoMS do not decrease the fMRI image quality when the distance between LoMS and the head coil of MRI is practical distance (more than 400 mm). We also confirmed that LoMS can operate properly in MRI environment, which is to measure own joint angle avoiding the noise from fMRI imaging. Then we show that the brain activity related to gait motion can be imaged during gait-like motion.

Real-world autonomous driving in city traffic must cope with dynamic environments including other agents with uncertain intentions. This poses a challenging decision-making problem, e.g., deciding when to perform a passing maneuver or how to safely merge into traffic. Previous work in the literature has typically approached the problem using ad-hoc solutions that do not consider the possible future states of other agents, and thus have difficulty scaling to complex traffic scenarios where the actions of participating agents are tightly conditioned on one another. In this paper we present multipolicy decision-making (MPDM), a decision-making algorithm that exploits knowledge from the autonomous driving domain to make decisions online for an autonomous vehicle navigating in traffic. By assuming the controlled vehicle and other traffic participants execute a policy from a set of plausible closed-loop policies at every timestep, the algorithm selects the best available policy for the controlled vehicle to execute. We perform policy election using forward simulation of both the controlled vehicle and other agents, efficiently sampling from the high-likelihood outcomes of their interactions. We then score the resulting outcomes using a user-defined cost function to accommodate different driving preferences, and select the policy with the highest score. We demonstrate the algorithm on a real-world autonomous vehicle performing passing maneuvers and in a simulated merging scenario.

We describe a new multi-resolution scan matching method that makes exhaustive (and thus local-minimum-proof) matching practical, even for large positional uncertainties. Unlike earlier multi-resolution methods, in which putative matches at low-resolutions can lead the matcher to an incorrect solution, our method generates exactly the same answer as a brute-force full-resolution method. We provide a proof of this. Novelly, our method allows decimation of both the look-up table and in the point cloud, yielding a 10x speedup versus contemporary correlative methods. When a robot closes a large-scale loop, it must often consider many loop-closure candidates. In this paper, we describe an approach for posing a scan matching query over these candidates jointly, finding the best match(es) between a particular pose and a set of candidate poses (“one-to-many”), or the best match between two sets of poses (“many-to-many”). This mode of operation finds the first loop closure as much as 45x faster than traditional “one-to-one” scan matching.

In this paper, we present a method to reconstruct the configurations of kinematic trees of rigid bodies not using measurements of relative angles (such as, e.g. rotary encoders at joints) but absolute posture sensors (such as IMUs) along with suitable filter algorithms. We argue that the relatively larger inaccuracies shown by absolute sensors can be compensated by suitable processing, such as a passive complementary filters exploiting the Mahony-Hamel formulation. The proposed method is applicable to systems where measurements of relative angles is not feasible or convenient, or where the joint kinematics are not lower pairs: for example, human body parts or soft robotic devices. In the paper, we make explicit reference to the reconstruction of posture of the compliant, underactuated Pisa/IIT SoftHand. Quantitative comparisons with ground truth data in grasping tests are used to validate the proposed method. The resulting hardware design is mechanically robust, cheap and can be easily adapted to robotic hands with different structures, as well as to sensorizing gloves for studying human grasping strategies.

A low-cost but accurate and backdrivable motor control system running on a DC motor and a harmonic drive gear is proposed. It compensates internal friction of the gear and the counter-electromotive torque by combining a model-based feed-forward method and a disturbance observer using a cheap torque sensor. A complementary use of those techniques lowers requirements to their performances i.e. precision, bandwidth, etc, while it is equipped with a flexible property against the external torque. A 2-DOF servo controller is also built upon the system in order to simultaneously achieve smooth responses and robust convergences to the reference.

In this work, we consider long-term topological place learning and present an approach that enables the robot to learn in an unsupervised, organized and incremental manner. The knowledge associated with the previously visited places is internally stored in the form of bubble descriptor semantic tree (BDST) using the previously proposed bubble space representation. The BDST is generated and maintained without any external supervision. It organizes the learned knowledge where the terminal nodes are viewed as corresponding to distinct places while its structure encodes their semantic hierarchy. In case the robot is not able to recognize a place with its current BDST, it learns it via updating the BDST incrementally based on the hierarchical single link clustering algorithm SLINK. The proposed approach is evaluated experimentally using combined benchmark datasets from indoor and outdoor settings with recognition rates comparable to those of state-of-the-art approaches while the robot is able to retain efficiently and use the knowledge associated with the learned places.