Tadej Petrič’s research while affiliated with Jožef Stefan Institute and other places

To achieve higher positioning accuracy, it is common practice to calibrate the robot. An essential part of the calibration is the estimation of the kinematic parameters. Due to various nonlinear influences on the end-effector position accuracy, such as joint and link flexibility, standard methods of identifying kinematic parameters do not always give a satisfactory result. In this paper, we propose a strategy that considers deflection-dependent errors to improve the overall positioning accuracy of the robot. As joint/link deflections mainly depend on gravity, we include the compensation of gravity-induced errors in the estimation procedure. In the first step of the proposed strategy, we compute the joint position errors caused by gravity. In the next step, we apply an existing optimization method to estimate the kinematic parameters. We propose to use an optimization based on random configurations. Such an approach allows good calibration even when we want to calibrate a robot in a bounded workspace. Since calibration is generally time consuming, we investigated how the number of measured configurations influences the calibration. To evaluate the proposed method, we used a simulation of the collaborative robot Franka Emika Panda in MuJoCo.Keywordsserial manipulatorscalibrationerror compensation

Collaborative robotics is one of the fastest developing but at the same time one of the most challenging fields in robotics. The reason for the latter is that a good robot collaborator should precisely understand the intentions of the human coworker. In this paper, human-robot collaboration is addressed by the application of a dynamical phase state system guided by stable heteroclinic channel networks to the motion of a humanoid robot. With this approach, a person can control the underlying dynamical system by applying forces to the grippers of the humanoid robot and this way guide the robot. The robot motions presented in this paper are lifting up the forearms and squatting, but the dynamical model can be further extended to incorporate an arbitrary amount of motions. The presented approach, therefore, provides a suitable way for the realization of efficient human-robot collaboration and should be further explored and developed.KeywordsHuman–robot interactionCollaborative roboticsPhase state systemStable heteroclinic channel networks

Purpose of Review The ability to autonomously manipulate the physical world is the key capability needed to fulfill the potential of cognitive robots. Humanoid robots, which offer very rich sensorimotor capabilities, have made giant leaps in their manipulation capabilities in recent years. Due to their similarity to humans, the progress can be partially attributed to the learning by demonstration paradigm. Supplemented by the autonomous learning methods to refine the demonstrated manipulation actions, humanoid robots can effectively learn new manipulation skills. In this paper we present continuous effort by our research group to advance the manipulation capabilities of humanoid robots and bring them to autonomously act in an unstructured world. Recent Findings The paper details progress in the area of humanoid robot learning, ranging from trajectory imitation, motion adaptation in order to maintain feasibility and stability, and learning of dynamics to statistical generalization of actions, autonomous learning, and end-to-end vision-to-action learning that exploits deep neural networks. Summary With the focus on manipulation, the presented research provides the means to overcome the complexity behind the problem of engineering manipulation skills on robots, especially humanoid robots where programming by demonstration is most effective.

Quasi-passive exoskeletons have emerged as a solution that avoids the high energy requirements that negatively affect the efficiency of exoskeletons. These exoskeletons do not deliver positive mechanical work to the joint, but accumulate and deliver energy in a viscoelastic element that is actively placed or removed parallel to the user's muscles. %One of the crucial aspects of the exoskeleton is the power-to-weight ratio of the actuator, as this inescapably affects the weight and thus the portability of the entire system. Previous research has investigated different strategies, mostly based on energy harvesting with compliant elements for mechanical energy storage and locking mechanisms to turn the elements on and off. This paper seeks to address the problem of bulky actuators in quasi-passive exoskeletons by experimentally evaluating the proposed quasi-passive mechanism consisting of a pneumatic cylinder, acting as an elastic element, and a solenoid valve replacing a mechanical clutch. The main advantage of the proposed mechanism is that the elastic element can be turned on and off at any time and position, with a high switching frequency, which improves the possibility to harvest the energy. Our aim was to investigate whether, with the proposed actuation, it is possible to achieve the force that has previously been shown to have a positive effect on ankle plantar flexion. Furthermore, we analyzed how the timely activation of the solenoid valve affects the force characteristics. We built the testbed equipped with sensors that allow measurements of torque, pressure, and angular deflection. The obtained measurements are in line with the theoretical model, where the force achieved is within the required range. In addition, it was shown that the time for the actuator to switch off from the peak force is about 100 ms, without major air leaks and energy bursts. The presented results highlight that the actuation of this type is a good candidate for designing a lightweight high-performance quasi-passive exoskeleton.

One of the major causes of disability and sick day leaves is lower back pain. Hence it can result in a decreased life quality and lower industrial productivity. One of the possible solutions to lower back pain could be the use of exoskeletons, which would reduce the spinal loading. One of such solutions is a quasi-passive spinal exoskeleton that engages and disengages the passive support depending on the movements performed by the user. This enables the spinal support for the user when lifting a heavy load and for all other tasks, the user motion is unobstructed. To achieve autonomous clutch activation, the main challenge is to properly classify the beginning of each motion. In this paper, we proposed a novel control method that uses Gaussian Mixture Models (GMM) for movement classifiers and a network of Stable Heteroclinic Channels (SHC) for designing a phase-state-machine. Integrating GMM into the SHC network enables a fast and reliable control of the clutch mechanism of the quasi-passive spinal exoskeleton. The control system capabilities were demonstrated in an experiment with a male subject wearing the quasi-passive exoskeleton while executing three different movements representative for an industrial working environment: walking, standing, and lifting.

Compliant robots with constant joint stiffness (Serial Elastic Actuators - SEA), on the contrary to ones with variable joint stiffness (Variable Stiffness Actuators – VSA), have limited capabilities for modulating robot mechanical impedance in the interaction task. However, in the case of kinematic redundancy in specific tasks, robots can exploit the null space to adjust End-Effector (EE) Cartesian stiffness. Thus, prior knowledge of the task path or the operational workspace can be used to pre-compute joint stiffness that can enable maximal ratio between maximal and minimal stiffness of the robot’s EE during the task execution, and therefore shape achievable EE stiffness to best fit the task execution. In that light, this paper elaborates on the preselection of joint stiffnesses which influences the achievable robot’s Cartesian stiffness in a specific task. Besides optimizing the available operational EE stiffness, by pre-computed joint stiffness values, the robot will be able to adapt better to specific tasks and provide a better framework for safe and efficient physical human-robot interaction. The paper presents an approach to the selection of predefined joint stiffness values of the 7-DOFs KUKA LWR, where joint stiffness is achieved/emulated with torque feedback. In the simulation experiments, the approach is depicted in the preselection of two joint stiffness values within the prescribed range, while other joint stiffness is set constant.

Several approaches exist for learning and control of robot behaviors in physical human-robot interaction (PHRI) scenarios. One of these is the approach based on virtual guides which actively helps to guide the user. Such a system enables guiding users towards preferred movement directions or prevents them to enter into a prohibited zone. Despite being shown that such a framework works well in physical contact with humans, the efficient interaction with the environment is still limited. Within the virtual guide framework, the environment is considered as a physical guide, for example, a table is a plane that prevents the robot to penetrate through. To mitigate these limits we introduce and evaluate the means of autonomous path adaptation through interaction with physical guides, which essentially means merging virtual and physical guides. The virtual guide framework was extended by introducing an algorithm which partially modifies the virtual guides online. The path updates are now based on the interactive force measurements and essentially improves the virtual guides to match them with the actual physical guides.

In this paper, we consider the problem of generating smooth Cartesian paths for robots passing through a sequence of waypoints. For interpolation between waypoints we propose to use radial basis functions (RBF). First, we describe RBF based on Gaussian kernel functions and how the weights are calculated. The path generation considers also boundary conditions for velocity and accelerations. Then we present how RBF parameters influence the shape of the generated path. The proposed RBF method is compared with paths generated by a spline and linear interpolation. The results demonstrate the advantages of the proposed method, which is offering a good alternative to generate smooth Cartesian paths.

Research and development of active and passive exoskeletons for preventing work related injuries has steadily increased in the last decade. Recently, new types of quasi-passive designs have been emerging. These exoskeletons use passive viscoelastic elements, such as springs and dampers, to provide support to the user, while using small actuators only to change the level of support or to disengage the passive elements. Control of such devices is still largely unexplored, especially the algorithms that predict the movement of the user, to take maximum advantage of the passive viscoelastic elements. To address this issue, we developed a new control scheme consisting of Gaussian mixture models (GMM) in combination with a state machine controller to identify and classify the movement of the user as early as possible and thus provide a timely control output for the quasi-passive spinal exoskeleton. In a leave-one-out cross-validation procedure, the overall accuracy for providing support to the user was 86.72±0.86% (mean ± s.d.) with a sensitivity and specificity of 97.46±2.09% and 83.15±0.85% respectively. The results of this study indicate that our approach is a promising tool for the control of quasi-passive spinal exoskeletons.