IROS 2015 论文列表

Current robotic rehabilitation devices have a high cost-to-benefit ratio, which prevents their large scale adoption by the clinical rehabilitation community. This paper first presents H-Man, a low cost planar robot, as a quantitative assessment and training tool. This is followed by a preliminary study to investigate baseline performance measures for motor assessment during reaching tasks as a step toward replacing conventional ordinal scales with continuous quantitative scales. Thirteen healthy and one participant with upper limb motor impairment participated in the study and performed reaching tasks with their dominant and non-dominant hands in three directions. The results from healthy subjects indicate no significant difference between different directions for both limbs and also between corresponding directions of dominant and non-dominant limbs (p > 0.05, all cases). However, differences in measures can be observed for the impaired subject.

This paper explores the manipulation of a grasped object by pushing it against its environment. Relying on precise arm motions and detailed models of frictional contact, prehensile pushing enables dexterous manipulation with simple manipulators, such as those currently available in industrial settings, and those likely affordable by service and field robots.

Prediction of interaction force between human and contacting environment has always been an important issue for such as robot control and clinical treatment. Although force/torque sensors can provide direct and precise measurement results, in many cases it is inconvenient to attach these sensors on the environment surface or on human beings. The purpose of this paper is to propose a prediction method for interaction force between human and contacting environment, using only electromyographic (EMG) signals. The motions are touch motion and push motion of upper extremity. Seven muscles of the upper limb were selected to record EMG signals. The predict function were derived from two muscle-skeleton models implemented for touch motion and push motion, respectively. The Bayesian linear regression (BLR) algorithm was implemented for parameters calibration. In order to avoid complex model for dynamic movement, a neural network classifier was used to recognize the force exerting motions. The proposed method was applied in a remote interaction force evaluation experiment. The “Phantom Premium” haptic device is used to represent the predicted force in the remote site. The experimental results show that the proposed method can provide acceptable prediction results with root-mean-square (RMS) error below 2.20 N.

In this paper, a method to predict the precision of parallel microstructures made of carbon fibers, polyimide layers and piezoelectric materials is proposed. To this aim, we propose to combine the exponential representation of transformation matrix with intervals to bound the uncertainties in each flexure joint of the microstructures. Based on interval techniques and algorithms, the derived model permits to guarantee a bound on the precision value of the end effector. The method has been demonstrated with a numerical example of parallel microrobot devoted to orient the laser spot towards vocal fold during phonomicrosurgery. This example considers the uncertainties that occur in the compliant joints caused by the fabrication technique.

Although considering dynamics in the control of humanoid robots can improve tracking and compliance in agile tasks, it requires local and global states of the system, precise torque control and proper modeling. In this paper we discuss practical issues to implement inverse dynamics on a torque controlled robot. By modeling electrical actuators offline, inverting such model and estimating the friction on-line, a high bandwidth torque controller is implemented. In addition, a cascade of optimization problems to fuse all the sensory data coming from IMU, joint encoders and contact force sensors estimate the robot's global state robustly. Our estimation builds the kinematic chain of the legs from the center of pressure which is more robust in case of slight slippage, tilting or rolling of the feet. Thanks to precise and fast torque control, robust state estimation and optimization-based whole body inverse dynamics, the real robot can keep balance with very small stiffness and damping in Cartesian space. It can also recover from strong pushes and perform dexterous tasks. The highly compliant and stable performance is based on pure torque control, without any joint damping or position/velocity tracking.

This paper characterises the power consumption of electric rotorcraft and derives an endurance estimation model for such aircraft powered by LiPo batteries. Theoretical analysis is backed by experimental flight tests using a popular commercial quadrotor. Experiments on Commercial Off-The-Shelf Lithium-Polymer batteries, which are the technology dominating the multi-rotor Unmanned Aerial Vehicle market, are carried out to determine battery run-time and specific energy/capacity models, whilst investigating the battery variability. These battery models are combined with the rotorcraft power model to provide an endurance estimation model, accounting for both battery variability as well as the electric propulsion system effects, which is experimentally validated through flight tests.

This paper presents an estimator to obtain the posture of a moving skeleton, i.e. all relative orientations of the skeleton's bodies that are connected via a common joint. The estimator uses inertial sensor data only, the relative orientation around all axes is obtained without magnetometers. Instead, the accelerations of the skeleton's motion provide information about the otherwise missing degree of freedom. The method also yields the orientation of the skeleton as a whole except for the heading. In addition, a technique to decouple the estimation rate from the sensor sampling rate is introduced. The estimator both with and without rate decoupling is evaluated against ground truth data and yields relative orientations about 5 degrees off ground truth while the skeleton moves.

This paper proposes a position and stiffness bounding approach (PSBA) for improving “geometry transparency” in time-delayed teleoperations. The proposed method can rapidly update the local model location in master site to the contact location in the slave site. The proposed PSBA can therefore avoid instability problem due to both discrete force controller and sudden contact location changes (so called “model jump effect”). Effectiveness of the proposed approach is shown by 1-DOF simple contact experiment in 500ms round trip time delayed virtual environment.

Precise 3D mapping and 6DOF trajectory estimation using exteroceptive sensors are key problems in many fields. Real-time moving laser sensors gained popularity due to their precise depth measurements, high frame rate and large field of view. We propose an optimization framework for Simultaneous Localization And Mapping that properly models the acquisition process in a scanning-while-moving scenario. Each measurement is correctly reprojected in the map reference frame by considering a continuous time trajectory which is defined as the linear interpolation of a discrete set of control poses in SE3. The trajectory estimation is performed using the sensor readings only, i.e., no external motion measurement units are used. An efficient data structure that makes use of a hybrid sparse voxelized representation for large map management allows to perform global optimization over trajectories, resetting the accumulated drift when loops are detected. We experimentally show that such framework improves localization and mapping w.r.t. solutions that compensate the distortion effects without including them in the optimization step. Moreover, we show that the proposed map structure provides linear or constant time operations w.r.t. the map size in order to perform real time SLAM and it can handle very large maps.

Human pose estimation in realistic world conditions raises multiple challenges such as foreground extraction, background update and occlusion by scene objects. Most of existing approaches were demonstrated in controlled environments.

This paper describes the topology of general multi-arm prehensile manipulation. Reasonable assumptions are applied to reduce the number of manipulation modes, which results in an explicit graphical representation for multi-arm manipulation that is computationally manageable to store and search for solution paths. In this context, it is also possible to take advantage of preprocessing steps to significantly speed up online query resolution. The approach is evaluated in simulation for multiple arms showing it is possible to quickly compute multi-arm manipulation paths of high-quality on the fly.

Exchanging objects with humans through hand-overs is a key feature for any robot operating side by side with humans. The studies on the topic tackle various problems, such as the hand dexterity, the communication cues, the arms motions, the forces, the head motions, but most of them consider a handover as an independent action, decorrelated from the plan it is part of.

Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming; e.g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoot of these settings that has largely been overlooked is the problem of planning for serendipity - i.e. planning for stigmergic collaboration without explicit commitments on agents in co-habitation. In this paper we formalize this notion of planning for serendipity for the first time, and provide an Integer Programming based solution for this problem. Further, we illustrate the different modes of this planning technique on a typical Urban Search and Rescue scenario and show a real-life implementation of the ideas on the Nao Robot interacting with a human colleague.

In off-road applications, where mobile robots operate on rough environments, the physical properties of the terrain play a key role on their performance. An extreme example is posed by planetary rover missions to Mars, for which communication constraints and the inability of vision-based approaches to detect non-geometric hazards, e.g. sand traps hidden below thin duricrusts, can lead to permanent immobilisation, as experienced by NASA's Spirit rover. To prevent such events, this paper proposes a method to classify dry granular soils according to their physical properties by using an on-board sensor system for on-line analysis of sinkage, slippage and vibrations of the hybrid wheel-legs mounted on a highly mobile robot. As reflected by the experimental results, obtained using a single wheel-leg test bed, the novel approach produces an efficient and robust differentiation of soils with dissimilar physical properties. This output can enable autonomous avoidance of non-geometric hazards without endangering the mobility of the mission. Different classifier algorithms are trained, validated and compared in terms of classification accuracy and computational efficiency, revealing the advantages and disadvantages of each approach.

The advantages of using the entire photometric image information as visual feature are: it does not require any feature detections, matching or tracking process. To enlarge the convergence domain, we propose to accomplish visual servoing based on the analytical formulation of Gaussian mixtures to model the images. During the servoing, we consider the optimization of the Gaussian spreads allowing the camera to converge to a desired pose even from a far initial one. Simulation that overcomes the state-of-the-art and real experiments highlight the success of our approach.

We present a framework for parameter and state estimation of personalized human kinematic models from motion capture data. These models can be used to optimize a variety of human-robot collaboration scenarios for the comfort or ergonomics of an individual human collaborator. Our approach offers two main advantages over prior approaches from the literature and commercial software: the kinematic models are estimated for a specific individual without a priori assumptions on limb dimensions or range of motion, and our kinematic formalism explicitly encodes the natural kinematic constraints of the human body. The personalized models are tested in a human-robot collaborative manipulation experiment. We find that human subjects with a restricted range of motion rotate their torso significantly less during bimanual object handoffs if the robot uses a personalized kinematic model to plan the handoff configuration, as compared to previous approaches using generic human kinematic models.

Advanced countries are currently experiencing an aging society, and many people could benefit from a personal robot that can support a comfortable lifestyle. However, excessive and premature robot assistance may deteriorate the user's physical abilities and can accelerate their aging process. The authors have already proposed a personal robot that can follow the user, even on uneven road surfaces. Therefore, people utilizing this personal robot do not need to carry heavy baggage, even after shopping. It is reported that a few brisk walks per week can be advantageous to our health management and can improve our quality of life, so this means that the personal robot can encourage the user to maintain an active lifestyle. For this personal robot, close following is required to ensure the safety of the baggage and the personal robot. However, it is difficult for the conventional approach to avoid collisions when the user stops suddenly. In this paper, a control approach based on model predictive control is proposed to achieve two conflicting requirements: close following and no collision. The proposed approach predicts multiple future outcomes, in which the user either stops or accelerates in the next step, in order to realize an appropriate relative distance and to consider the upper and lower boundaries for posture stabilization. The effectiveness of the proposed approach can be verified in numerical simulations using a multibody dynamics model consisting of actual 3D representations.

This paper reports on the performance evaluation of Magneto-Rheological Fluid (MRF) based actuation systems when used in haptic interfaces. MRF-based actuators exhibit superior characteristics, which can significantly contribute to the transparency and stability of haptic devices. To validate this statement, a prototype two degrees-of-freedom (DoF) haptic interface is constructed. A distributed antagonistic configuration is used in order to develop the 2-DoF haptic interface based on small-scale MRF-based clutches for a class of medical applications. The developed device is compared with three conventional haptic interfaces and their stability is compared using the Virtual Wall experiment. Next, the prototype interface is incorporated in a master-slave teleoperation medical setup. Preliminary studies on the performance of the haptic interface show great potential of MRF-based actuators for integration in haptic devices for medical interventions that require safe, accurate, and stable force representation.

Perching on a vertical surface carries the risk of severe damage to the vehicle if the maneuver fails, especially if failure goes undetected. We present a detection method using an onboard 3-axis accelerometer to discriminate between perching success and failure. An analytical model was developed to calculate acceleration differences for success and failure and set decision times. Two distinct decision times were shown to be effective, corresponding to properly engaging the gripper and overloading the gripper's capabilities. According to a machine learning feature selection algorithm, the maximum Z axis acceleration of the quadrotor and the presence of near-zero readings are the most relevant features within these two time frames. Using these features, the detection algorithm discriminated between success and failure with a 91% accuracy at 40 ms, and 94% at 80 ms. Real-time detection and failure recovery experiments with a 20 g quadrotor verify the detection method. An improved approach that combines various decision times correctly identified success/failure for all 20 trials with an average total falling distance of 0.8m during recovery. We discuss the feasibility of extending our method to other quadrotor platforms.