Pietro Falco

On the Stability of Closed-Loop Inverse Kinematics Algorithms for Redundant Robots

The purpose of this paper is to provide a convergence analysis of classical inverse kinematics algorithms for redundant robots, whose stability is usually proved only in the continuous-time domain, thus neglecting limits of the actual implementation in the discrete time, whereas the convergence analysis carried out in this paper in the discrete-time domain provides a method to find bounds on the gain of the closed-loop inverse kinematics algorithms in relation to the sampling time. It also provides an estimation of the region of attraction (without resorting to Lyapunov arguments), i.e., upper bounds on the initial task space error. Simulations on an 11-degree-of-freedom manipulator are performed to show how the found bounds on the gain are not too restrictive.

Integrated force/tactile sensing: The enabling technology for slipping detection and avoidance

This paper proposes an experimental study of slipping avoidance algorithms based on force/tactile perception data. The claim is that contact force measurements alone or tactile data alone are not sufficient for an effective slipping avoidance strategy in real world conditions. Integrated force/tactile sensors able to provide measurements of both the contact force vector and spatially distributed tactile maps are the key enabling technology for efficient slipping avoidance control algorithms that can actually work with real world objects under no restricting assumption on the contact geometry or with unknown physical properties of the objects. The paper proposes a new slipping avoidance control scheme, which usefully exploits an integrated force/tactile sensor mounted on the parallel gripper of a Kuka youBot. The results show how the strategy successfully allows the robot to safely manipulate real-world objects, both rigid and compliant, in various friction conditions of their surface, both stable and slippery.

Experimental Comparison of Sensor Fusion Algorithms for Attitude Estimation

Abstract Inertial Measurement Unit is commonly used in various applications especially as a low-cost system for localization and attitude estimation. Some applications are: real-time motion capture system, gait analysis for rehabilitation purposes, biomedical applications, advanced robotic applications such as mobile robot localization and Unmanned Aerial Vehicles (UAV) attitude estimation. In all the mentioned applications the accuracy and the fast response are the most important requirements, thus the research is focused on the design and the implementation of highly accurate hardware systems and fast sensor data fusion algorithms, named Attitude and Heading Reference System (AHRS), aimed at estimating the orientation of a rigid body with respect to a reference frame. A large number of different solutions can be found in the literature, and an experimental comparison of the most popular is presented in this work. In particular, the algorithm based on the gradient descent method and the algorithm based on a nonlinear complementary filter are compared to a standard Extended Kalman Filter (EKF) with the aim to show that a general method can easily compete with ad-hoc solutions and even outperform them in particular conditions. In order to validate the estimation accuracy a Kuka robot is used to compute the ground truth. Moreover, in order to estimate the computational burden, the algorithms are implemented on an ARM-Cortex M4-based evaluation board.

Online Segmentation and Classification of Manipulation Actions From the Observation of Kinetostatic Data

This paper presents an automated method for segmentation and classification of manipulation tasks. It introduces a method to build and update a dictionary of elementary actions, so as to express observed tasks as a sequence of items. Segmentation is carried out by splitting an observed manipulation task into submaneuvers. It is based on singular value decomposition of data that is gathered from the observation of humans. This observation consists of hand joint angles, the hand pose with respect to a world frame, and fingertip contact forces. The classification step introduces, from a large set of observed maneuvers, new entities called elementary actions that generalize the concept of segments, instances of elementary actions. This paper uses fingertip contact forces in the measured data. In grasping and manipulation tasks, the interaction between the hand and the object in the physical world is necessary to segment and interpret motion. A set of 120 maneuvers involving six tasks have been used to evaluate the methods with dependent measures including metrics of robustness, effectiveness, and repeatability. In such evaluations, the average value of the effectiveness metrics over all the maneuvers is 0.866. The interuser repeatability is equal to 0.8926, while the average repeatability is 0.911.

Data Fusion Based on Optical Technology for Observation of Human Manipulation

The adoption of human observation is becoming more and more frequent within imitation learning and programming by demonstration approaches (PbD) to robot programming. For robotic systems equipped with anthropomorphic hands, the observation phase is very challenging and no ultimate solution exists. This work proposes a novel mechatronic approach to the observation of human hand motion during manipulation tasks. The strategy is based on the combined use of an optical motion capture system and a low-cost data glove equipped with novel joint angle sensors, based on optoelectronic technology. The combination of the two information sources is obtained through a sensor fusion algorithm based on the extended Kalman filter (EKF) suitably modified to tackle the problem of marker occlusions, typical of optical motion capture systems. This approach requires a kinematic model of the human hand. Another key contribution of this work is a new method to calibrate this model.

Stability Analysis of a Hierarchical Architecture for Discrete-Time Sensor-Based Control of Robotic Systems

The stability of discrete time kinematic sensor-based control of robots is investigated in this paper. A hierarchical inner-loop/outer-loop control architecture common for a generic robotic system is considered. The inner loop is composed of a servo-level joint controller and higher level kinematic feedback is performed in the outer loop. Stability results derived in this paper are of interest in several applications including visual servoing problems, redundancy control, and coordination/synchronization problems. The stability of the overall system is investigated taking into account input/output delays and the inner loop dynamics. A necessary and sufficient condition that the gain of the outer feedback loop has to satisfy to ensure local stability is derived. Experiments on a Kuka K-R16 manipulator have been performed in order to validate the theoretical findings on a real robotic system and show their practical relevance.

Low-level flexible planning for mobile manipulators: a distributed perception approach

The paper proposes a method to improve flexibility of the motion planning process for mobile manipulators. The approach is based on the exploitation of perception data available only from simple proximity sensors distributed on the robot. Such data are used to correct pre-planned motions to cope with uncertainties and dynamic changes of the scene at execution time. The algorithm computes robot motion commands aimed at fulfilling the mission by combining two tasks at the same time, i.e. following the planned end-effector path and avoiding obstacles in the environment, by exploiting robot redundancy as well as handling priorities among tasks. Moreover, a technique to smoothly switch between the tasks is presented. To show the effectiveness of the method, four experimental case studies have been presented consisting in a place task executed by a mobile manipulator in an increasingly cluttered scene. Graphical Abstract

Observation and Execution

Assistive robotic systems in household or industrial production environments get more and more capable of performing also complex tasks which previously only humans were able to do. As robots are often equipped with two arms and hands, similar manipulations can be executed. The robust programming of such devices with a very large number of degrees of freedom (DOFs) compared with single industrial robot arms however is laborious if done joint-wise. Two major directions to overcome this problem have been previously proposed. The programming by demonstration (PbD) approach, where human arm and recently also hand motions are tracked, segmented and re-executed in an adaptive way on the robotic system and the high-level planning approach which tries to generate a task sequence on a logical level and attributes geometric information as necessary to generate artificial trajectories to solve the task. Here we propose to combine the best of both worlds. For the very complex motion generation for a robotic hand, a rather direct approach to assign manipulation actions from human demonstration to a human hand is taken. For the combination of different basic manipulation actions the task constraints are segmented from the demonstration action and used to generate a task oriented plan. This plan is validated against the robot kinematic and geometric constraints and then a geometric motion planner can generate the necessary robot motions to fulfill the task execution on the system.

Ensuring kinetostatic consistency in observation of human manipulation

The aim of this paper is to present a method to guarantee the kinetostatic consistency in observation of human manipulation, i.e. the consistency between the observed hand posture and the tactile information on the contact between the fingertips and the objects. The core idea of the proposed algorithm is to compare the fingertip contact information, obtained by tactile sensors, with the contact information computed in a virtual environment, that reproduces the real environment where the observation is carried out. In case the estimation of the joint angles and the relative pose between the hand and the object are not consistent, a correction of the hand posture is computed. For some tasks, collisions might occur between parts of the hand (e.g. palm) and the grasped object. To handle this problem, the corrected hand posture is computed by adopting a closed loop inverse kinematic (CLIK) approach that exploits the redundant Degrees of Freedom (DoFs) of the hand. The algorithm has been designed to work on-line. This feature is particularly important for Programming by Demonstration (PbD) applications, since it allows the trainer to actively adapt the demonstration to measurement noise and model errors. The effectiveness of the proposed method has been tested in five different tasks: grasping a cup, unscrewing a bottle, grasping a plate, grasping a ketchup bottle, and grasping a measuring cup.

Improvement of human hand motion observation by exploiting contact force measurements

The aim of this paper is to present a novel method to improve the observation of the human hand motion, exploiting the measurements of fingertip contact forces. The core idea of the proposed algorithm is to compare the fingertip contact information, obtained by commercial tactile sensors, with the contact information computed in a virtual environment, that reproduces the real environment in which the observation is carried out. In case the estimation of the joint angles and the relative pose between the hand and the object are accurate, the contact information in the virtual and in the real environment are consistent. On the other hand, when the two sources of information are not consistent, a correction of the hand posture is applied. The algorithm has been designed to work on-line. In general, this feature is particulary important for Programming by Demonstration (PbD) applications, since it allows the trainer to actively adapt the demonstration to measurement noise and model errors. The effectiveness of the proposed method has been tested in three different tasks: grasping a cup, unscrewing a bottle, grasping a plate.