IROS 2015 Papers

This paper presents a novel manipulation trajectory generating algorithm that constructs trajectories from learned motion harmonics and user defined constraints. The algorithm uses functional eigenanalysis to learn motion harmonics from demonstrated motions and then use the motion harmonics to compute the optimal trajectory that resembles the demonstrated motions and also satisfies the constraints. The algorithm has been tested on five real human motion data sets to obtain motion harmonics and then generate motions of each task for a NAO robot. The generated trajectories were compared with the trajectories generated using linear segment with parabolic blend approach and with the Open Motion Planning Library. The approach can also work with motion planners.

There is a possibility of remaining indication of microorganisms on Martian subsurface and therefore, a robotic arm mounted on an exploration robot is required to dig to a certain depth and collect appropriate sample to be analyzed. However, the environment on the surface of Mars is harsh and most of all, limited power is available from the solar panel. In this paper, FEMI (Force-and-Energy Manipulability Index) is proposed to evaluate a feasible arm configuration for low energy consumption in its soil sampling operation. FEMI is calculated by the combination of an energy manipulability of an arm and an external force generated at an end effector of the arm. The FEMI derives the most feasible configuration on the joint angles of the arm for each sampling point. The usefulness of the FEMI is confirmed through a numerical simulation of a robotic arm. The simulation also presents that the FEMI can be used to obtain mechanical parameters of the arm such as link length and motor power, that are optimally-designed for a certain mission.

Control of robot locomotion profits from the use of pre-planned trajectories. This paper presents a way to generalize globally optimal and dynamically consistent trajectories for cyclic bipedal walking. A small task-space consisting of stride-length and step time is mapped to spline parameters which fully define the optimal joint space motion. The paper presents the impact of different machine learning algorithms for velocity and torque optimal trajectories with respect to optimality and feasibility. To demonstrate the usefulness of the trajectories, a control approach is presented that allows general walking including transitions between points in the task-space.

The magnetic field that exists in an indoor environment includes rich magnetic fluctuations because buildings contain many magnetized materials (e.g., steel frames). These fluctuations can be used as landmarks, the use of which requires the creation of a magnetic map representing the distribution of the magnetic field. It is, however, difficult to build a large-scale magnetic map because of the narrow measurement range of a magnetic sensor. This paper proposes an efficient method for collecting magnetic data using a mobile robot and a method for building a magnetic map using Gaussian processes. The use of these methods make it possible to build a large-scale magnetic map efficiently. Moreover, this paper presents a particle filter-based localization method based on the magnetic map. The presented system enables a robot to identify its own position in large-scale buildings. Experiments are used to demonstrate the performance and usefulness of the presented system.

This work presents a method for deriving a gain scheduled balance controller to stabilize the gate of a three legged under-actuated robotic platform called THALeR (Tri-Pedal Hyper Altitudinal Legged Robot). The scheduler adapts the controller gains in real time based upon the system's instantaneous potential energy in order to create a smooth, stable gait. The final controller is simulated with white noise and impulse perturbations to show robustness.

Gait analysis provides insightful information about people condition, progress of rehabilitation treatments and fall risk. This analysis is frequently performed in very specific conditions, because it often implies the use of treadmills, cameras and/or wearable sensors. Alternatively, gait can be analyzed using a smart rollator, equipped with a basic set of sensors. This work proposes a methodology to obtain relevant gait parameters using a simple, cheap source of information: wheel odometry and handlebar force sensors. The main advantages of this approach are that users are not bothered with extra equipment. Besides, monitoring can be performed anywhere, even during everyday conditions and for extended periods of time. We have tested our system with a set of rehabilitation patients with different disabilities. Preliminary results have successfully proven that extracted parameters are coherent with reported effects of their specific condition.

The parameterisation of rotations in three dimensional Euclidean space is an area of applied mathematics that has long been studied, dating back to the original works of Euler in the 18th century. As such, many ways of parameterising a rotation have been developed over the years. Motivated by the task of representing the orientation of a balancing body, the fused angles parameterisation is developed and introduced in this paper. This novel representation is carefully defined both mathematically and geometrically, and thoroughly investigated in terms of the properties it possesses, and how it relates to other existing representations. A second intermediate representation, tilt angles, is also introduced as a natural consequence thereof.

This paper presents a robust 3D position tracking and map generation algorithm that combines data from a Laser Rangefinder and a RGB-D camera. Our algorithm is capable of running in real time on resource constrained robots. The novelty of this paper is the fusing of 2D-ICP from a Laser Rangefinder directly into a 3D position tracking, map building and SLAM system. Our approach takes advantage of the wide field of view and precision of laser scans to significantly improve the efficiency and accuracy of 3D alignment. We introduce a regularisation term to restrict movement in degrees of freedom that are not constrained by surface geometry. With efficiency in mind, we mirror a 2D local map based Graph SLAM approach in 3D to reliably detect loops if the robot comes near to an area it has previously visited. The mesh of each local map is warped after a graph of local maps is optimised to maintain a globally fused map. We provide experimental results to show that our algorithm is able to generate dense, globally consistent, mesh-based maps on resource constrained robots.

This paper shows how to carry out batch continuous-time trajectory estimation for bodies translating and rotating in three-dimensional (3D) space, using a very efficient form of Gaussian-process (GP) regression. The method is fast, singularity-free, uses a physically motivated prior (the mean is constant body-centric velocity), and permits trajectory queries at arbitrary times through GP interpolation. Landmark estimation can be folded in to allow for simultaneous trajectory estimation and mapping (STEAM), a variant of SLAM.

This paper aims at developing a unified and adaptive foot placement control for legged robots. The locomotion control of legged robots can be classified into three parts as body height control, body attitude control, and forward velocity control. In our study, the body attitude is controlled at stance phase by the hip actuator, and the height is controlled by the motion of the stance leg. In this case, the foot placement has a nearly linear correlation with forward velocity. Hereby, a generic foot placement controller is developed to control the forward velocity based on the online linear regression analysis of their coupled correlation. Our proposed algorithm is capable of adjusting the control parameters automatically, and is featured by good adaptability and higher control accuracy that outperforms the empirical tuning. The very same controller is able to produce stable hopping with accurate forward velocity tracking even with unknown mass offset, as well as stable bipedal running and walking with accurate velocity tracking.

In this paper we present a new friction model for industrial robot manipulators that takes into account temperature effects. In particular, after having shown that friction might change very significantly during robot operations, two solutions based on a polynomial description of the joint friction are proposed and compared. In both cases the models proposed do not need a measurement of the joint temperature, but just of the environmental temperature, so as to be easily applied in industry. Experimental results demonstrate the effectiveness of the applied methodology.

For a parallel manipulator with a kinematic loop and a highly flexible link, a comprehensive control approach including friction compensation for the drive train, gain scheduling and active oscillation damping is considered. Friction compensation based on the Stribeck as well as the LuGre models and gain scheduling are used to improve the position control. A curvature controller, which is based on a nonlinear flexible multibody system and on an accurate position control, actively damps the oscillations of the parallel manipulator. First experimental results are presented to confirm the utility of the implemented concepts.

The rolling motion of a soft robotic fingertip is in this paper explicitly included in the control objectives together with the force/position regulation targets. A model based control law is proposed to linearize and decouple the system with respect to the force/position and sliding dynamics based on an appropriately defined task Jacobian. The controller is validated by simulations including rolling on a stationary surface and graspless manipulation of a flat object.

Mobile robot contact sensing can be useful for navigation and manipulation with small robots. In this work, we present a low-cost, 18.7 gram force-torque sensor for a 10 cm hexapedal millirobot. A planar array of photointerrupters, rigidly attached to the structure of the robot, is used to measure the six-axis movement of a shell attached to the robot with springs. The sensors measure the intensity of infrared light reflected off of a surface on the underside of the shell, which has been specially designed to enable the resolution of the forces and moments in the x-, y-, and z-axes applied to the shell. The sensor has a force sensitivity of 17 mN and torque sensitivity of 0.72 mN-m for a sampling rate of 100 Hz. The sensor can resolve a force equivalent to 2.9% of the combined robot and sensor weight of 581 mN.

Coil springs are nowadays widely used in robotic applications, in particular in Series Elastic Actuators. The measurement of spring force, either via load cells or via position sensors, conventionally requires additional sensor hardware to be part of or parallel to the kinematic chain. In order to simplify measurement of spring deflection, we exploit the fact that helical spring are in fact solenoid coils, and as such exhibit inductance properties that change strongly with length. We investigate theoretical models for this effect, and we experimentally evaluate the accuracy of such models in predicting spring length from inductance, with and without additional calibrating measurements. Our preliminary results show that a sensing precision as low as 2% can be achieved, indicating that the principle could be suitable for force sensing of compliant actuators.

Surgeons can benefit from the cooperation with a robotic assistant during the repetitive execution of precise targeting tasks on soft tissues, such as during brain cortex stimulation procedures in open-skull neurosurgery. Position-based force-to-motion control schemes may not be suitable solution to provide the manipulator with the high compliance desirable during guidance along wide trajectories. A new torque controller with non-linear force feedback (FFE) is presented to provide augmented haptic perception to the operator, during the instrument's placement on the tissue. The FFE controller was experimentally validated with a pool of non-expert users using brain-mimicking gelatin phantoms (8%-16% concentration). Besides providing hand tremor rejection for a stable holding of the tool, the FFE controller was proved to allow for a safer tissue contact with respect to both robotic assistance without force feedback and freehand executions (50% and 75% reduction of the indentation depth, respectively). Future work will address the evaluation of the safety features of the FFE controller with expert surgeons on a realistic brain phantom, also accounting for unpredictable tissue motions as during seizures due to cortex stimulation.

It is well known that tactile information can be used to enhance the quality of grasping. Therefore, new technological solutions for sensing in grasping are needed. This paper presents an optical based fingertip sensor that measures both interaction forces and proximity between fingertip and environment. The combination of multiple sensing modalities in the tip of a finger can significantly improve grasping and manipulation capabilities. In this work we present the design and the required calibration of individual sensing elements, and of the integrated fingertip sensor developed for a 3-fingered metamorphic robotic hand. Emulated grasping experiments, using a pinch grip, were performed to illustrate the concept and validate the performance of the developed sensing system. As a result, it was possible to determine the sensor position with respect to an object during approach, contact and grasp.

In this paper we exploit Iterative Learning Controllers (ILC) schemes in force adaptation tasks. We propose to encode the control signal with Radial Basis Functions (RBF), which enhances the robustness of the ILC scheme and allows to vary the execution speed of the learned motion. For that a novel control scheme is proposed, which updates the feedforward compensation signals based on current iteration cycle signals in contrast to the standard ILC, which uses signals from the previous iteration cycle. This reduces the computational burden and enhances the adaptation speed. Stability of the proposed control law is analysed and discussed. The proposed approach is evaluated in simulation and on a Kuka Light Weight Robot Arm where the task was to perform force-based surface following with both discrete and periodic movements.

Robotic manipulation of deformable objects remains a challenging task. One such task is folding a garment autonomously. Given start and end folding positions, what is an optimal trajectory to move the robotic arm to fold a garment? Certain trajectories will cause the garment to move, creating wrinkles, and gaps, other trajectories will fail altogether. We present a novel solution to find an optimal trajectory that avoids such problematic scenarios. The trajectory is optimized by minimizing a quadratic objective function in an off-line simulator, which includes material properties of the garment and frictional force on the table. The function measures the dissimilarity between a user folded shape and the folded garment in simulation, which is then used as an error measurement to create an optimal trajectory. We demonstrate that our two-arm robot can follow the optimized trajectories, achieving accurate and efficient manipulations of deformable objects.