ICRA 2015 论文列表

This work describes how to use reactionless motion control with a free-floating space robot, suggesting thereby some tasks of practical importance. We show that the reactionless motion capability is directly affected by the kinematic structure of the manipulator, depending thereby upon the existence of kinematic redundancy, a typical lower/upper arm subchain and joint offsets. We investigate a seven-DoF redundant manipulator comprising these features and show that approximate reactionless motions can be obtained with the elbow joint only and/or the wrist joints. Using these reactionless motions, we propose three practical maneuvers for eye-in-hand type inspection, arm deploying/stowing and point-to-point motions with partial reactionless motion. Feasibility is verified via numerical simulations.

This paper presents a first attempt at a unified kinematics analysis of all serial and parallel solvable robots, that is, robots whose position analysis can be carried out without relying on numerical methods. The efforts herein are focused on finding a unified formulation for all quartically-solvable robots, as all other solvable robots can be seen as particular cases of them. The first part is centered on the quest for the most general quartically-solvable parallel and serial robots. As a result, representatives of both classes are selected. Then, using Distance Geometry, it is shown how solving the forward kinematics of the parallel representative is equivalent to solve the inverse kinematics of the serial representative, thus providing a unified formulation. Finally, it is shown that the position and singularity analysis of these robots reduces to the analysis of the relative position of two coplanar ellipses.

We present a (generalizable) method aimed to simultaneously transfer positional and force requirements encoded in a physical human skill (wood planing) from a human instructor to a robotic arm through kinesthetic teaching. We achieve our goal through a novel use of a common sensory configuration, constituted by a force/torque sensor mounted between the tool and the flange of a robotic arm. The robotic arm is endowed with integrated torque sensors at each joint. The mathematical model used to capture the general dynamic of the interaction between the human user and the wood surface is based on Dynamic Movement Primitives. During reenactment of the task, the system can imitate and generalize the demonstrated spatial requirements, as well as their associated force profiles. Therefore, the robotic arm acquires the capacity to reproduce the dynamic profile for in-contact tasks requiring an articulated coordination in the distribution of forces. For example, the capacity to effectively operate the plane on a wood plank over multiple strokes, according to the demonstration of the human instructor.

We address the problem of detecting embeddability of modular robots: namely, to decide automatically whether a given modular robot design can simulate the functionality of a seemingly different design. To that end, we introduce a novel graph representation for modular robots and formalize the notion of embedding through topological and kinematic conditions. Based on that, we develop an algorithm that decides embeddability when the two involved designs have tree topologies. Our algorithm performs two passes and involves dynamic programming and maximum cardinality matching. We demonstrate our approach on real modular robots and show that we can detect embeddability of complex designs efficiently.

In this paper we present a novel co-operative control policy purely for the transportation of large objects in underwater environments using two free floating vehicles, each one endowed with a 7 d.o.f redundant manipulator. Due to the presence of harsh conditions in underwater scenarios, it is extremely important to realize algorithms that depend on a minimal amount of explicit information exchanged by the agent, or without any exchange of information at all. To achieve this goal the control policy proposed in the paper only requires the exchange of six numbers at each time instant, while however exhibiting extremely good performances, inspite of the restraints on the information exchange.

Ocean monitoring using underwater sensor networks faces communication challenges in retrieving data, communicating large amounts of data between nodes, and covering increasing spatial regions while remaining connected. With underwater sensor networks that are capable of surfacing, unmanned aerial vehicles (UAVs) provide a solution to this by providing radio-based data muling services, but, as this area is still unexplored, the utility of this solution is unclear. In this paper, we examine the theoretical expectations, perform several field experiments, and analyze the communication success rates of 802.15.4 radios near the water surface both communicating between surface nodes as well as between a node and the UAV. These indicate that on the water surface internode radio communication is poor, but node to UAV communication can provide both reasonable ranges and success rates. We additionally measure and analyze the energy aspects of the systems, determining the impacts of parameters such as network size and distance between nodes on the UAV energy. Finally, we consolidate the information into an algorithm outlining how to configure and design hybrid UAV and underwater sensor network systems.

In this paper, we present an omnidirectional-vision-based localization and mapping system which can detect whether a robotic mower is contained in a permitted area. We exploit a robot-centric mapping framework that exploits a differential equation of motion of the landmarks, which are referenced with respect to the robot body frame. The estimator in our system generates a 3D point-based map with landmarks. Concurrently, the estimator defines a boundary of the mowing area with the estimated trajectory of the mower. The estimated boundary and the landmark map are provided for the estimation of the mowing location and for the containment detection. We validate the effectiveness of our system through numerical simulations and present the results of the outdoor experiment that we conducted with our robotic mower.

The main advantage of multi-legged robots is the ability to traverse uneven terrain and to overcome obstacles that would impede the movement of a wheeled robot. Compliant joint drives can further improve the performance of legged robot systems by considerably reducing the problems associated with uneven or slippery footholds. Small changes of the foot position in uneven terrain are compensated by the passive joint compliance without the need for fast contact detection and controller responses while the mechanical tension is reduced. On rougher terrain with bigger elevations/depressions of the surface structure, however, a reliable ground contact detection and stance height regulation for each leg is required to distribute the weight of the robot evenly among the legs on ground during stance phase. This work presents a concept that allows the inherently compliant hexapod robot HECTOR to walk on uneven terrain and to overcome moderate obstacles by means of a decentralized walking controller. An important prerequisite is the availability of inherently compliant joint drives with an increased power/weight-ratio.

We analyze the observability of 3-D pose from the fusion of visual and inertial sensors. Because the model contains unknown parameters, such as sensor biases, the problem is usually cast as a mixed filtering/identification, with the resulting observability analysis providing necessary conditions for convergence to a unique point estimate. Most models treat sensor bias rates as “noise,” independent of other states, including biases themselves, an assumption that is patently violated in practice. We show that, when this assumption is lifted, the resulting model is not observable, and therefore existing analyses cannot be used to conclude that the set of states that are indistinguishable from the measurements is a singleton. In other words, the resulting model is not observable. We therefore re-cast the analysis as one of sensitivity: Rather than attempting to prove that the set of indistinguishable trajectories is a singleton, we derive bounds on its volume, as a function of characteristics of the sensor and other sufficient excitation conditions. This provides an explicit characterization of the indistinguishable set that can be used for analysis and validation purposes.

In this paper, we consider the problem of controlling a multi-robot team in order to track a mobile target with unknown dynamics. Our contribution in this context is a non-linear observability analysis of the target tracking network, which yields insights into the topological and actuation conditions necessary for the underlying state estimation problem. We demonstrate a metric of observability which allows us to determine robot inputs that maximize observability to improve team localization. Combining the observability metric with topological control then yields a robust target tracking solution. We close the paper with a simulation example which demonstrates the ability of our solution to cope with scenarios in which relative measurements poorly distinguish the target.

This paper presents a new control scheme for dexterous manipulation of an object by a multifingered hand. The effects of the uncertainties on the contact orientation and location are investigated and taken into account in the control design, allowing to manipulate the object robustly. The control law has three main objectives: (i) ensuring the motion control of the object, (ii) satisfying the constraints of the manipulation system, and (iii) being robust to the uncertainties on the contact point. The proposed control is based on a state feedback architecture with robust pole placement by an LMI approach. The controller is designed offline and can be related to an object-level impedance controller. The constraints of the manipulation system, e.g. the friction constraints, are taken into account with an additional control action, based on an online LMI evaluation. Simulation results are presented and demonstrate that the proposed control law ensures the three main objectives.

A class of robotic systems that is attracting increased scientific attention is Large-Scale Actuator Networks (LSANs). A potential application of actuator networks is distributed manipulation, a technique that has significant utility within the field of industrial automation. A reactive elastic surface that autonomously morphs its shape to transport an arbitrary number of objects to a target location is presented to explore this class of systems. A collection of actuators within a network are used to overcome the limitations of individual actuator elements, and results in a system that has multiple degrees-of-freedom. Experimental results and simulation data are presented to illustrate the functionality and scalability of the platform.

In this paper, we introduce a dictionary learning framework using RGB-D covariance descriptors on point cloud data for performing object classification. Dictionary learning in combination with RGB-D covariance descriptors provides a compact and flexible description of point cloud data. Furthermore, the proposed framework is ideal for updating and sharing dictionaries among robots in a decentralized or cloud network. This work demonstrates the increased performance of 3D object classification utilizing covariance descriptors and dictionary learning over previous results with experiments performed on a publicly available RGB-D database.

In planning for deliberation or navigation in real-world robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its predictions about the world. In this paper, we present a novel approach to specifically represent changes that stem from periodic events in the environment (e.g. a door being opened or closed), which impact on the success probability of planned actions. We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes in a long-term data obtained in two real-life offices better than a static model. We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans. The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework.

We consider low-noise angular acceleration estimation for multi-axis robotic manipulators. The proposed model uses pairwise coupled inertial measurements across a section of the kinematic chain, which is reduced to a single rigid body. Experimental validation is built upon compact low-power micro-electro-mechanical (MEMS) components, installed on a full-scale heavy-duty mobile manipulator. While the model itself has a built-in mechanism for common-mode disturbance rejection, an adaptive transversal filter is devised for a further improvement. The results indicate a 40–80 fold suppression of high-frequency perturbations with respect to a baseline motion derivative, the discrete difference. As inertial sensors require no mechanical contact to rotating axes and the number of parameters is kept low, the model is easily applicable to motion control feedback of typical heavy-duty manipulators.

We present a randomized kinodynamic planner that solves rearrangement planning problems. We embed a physics model into the planner to allow reasoning about interaction with objects in the environment. By carefully selecting this model, we are able to reduce our state and action space, gaining tractability in the search. The result is a planner capable of generating trajectories for full arm manipulation and simultaneous object interaction. We demonstrate the ability to solve more rearrangement by pushing tasks than existing primitive based solutions. Finally, we show the plans we generate are feasible for execution on a real robot.

This work deals with a comprehensive version of the tethered aerial vehicle problem including all the three possible link cases: cable, strut, and bar. We prove the dynamic feedback linearizability and differential flatness of the system with respect to the elevation of the vehicle and the stress applied to the link. Moreover we prove the observability of the system using only on-board inertial sensors (i.e., only a gyroscope plus an accelerometer). We design a globally convergent nonlinear controller based on the concurrent use of a dynamic feedback linearization control and a state estimator based on a nonlinear state/output transformation and a high gain observer scheme. The controller/observer algorithm is thus able to globally control elevation and stress (both tension and compression) along independent time-varying trajectories only resorting to inertial measurements. The stability of the controlled system is theoretically proven and its behavior is shown by means of extensive dynamical simulations.

We present an approach to high-level control for bipedal walking exemplified with a 2D point-mass inextensible-legs inverted-pendulum model. Balance control authority here is only from step position and trailing-leg push-off, both of which are bounded to reflect actuator limits. The controller is defined implicitly as the solution of an optimization problem. The optimization robustly avoids falling for given bounded disturbances and errors and, given that, minimizes the number of steps to reach a given target speed. The optimization can be computed in advance and stored for interpolated real-time use online. The general form of the resulting optimized controller suggests a few simple principles for regulating walking speed: 1) The robot should take bigger steps when speeding up and should also take bigger steps when slowing down 2) push-off is useful for regulating small changes in speed, but it is fully saturated or inactive for larger changes in speed. While the numerically optimized model is simple, the approach should be applicable to, and we plan to use it for, control of bipedal robots in 3D with many degrees of freedom.

Reachability is an important dynamic property of Petri nets. Its determination given an unbounded net and initial marking has remained an open problem since 1960s. Due to its extreme difficulty, a great deal of research has been focused on some subclasses of unbounded nets. For arbitrary ones, this work intends to propose a new reachability tree (NRT), which consists of only but all reachable markings from its initial marking. Furthermore, an NRT-based method to decide deadlocks of unbounded nets is presented. An example is provided to show the new results.