Anthropomorphic Robot Arm Research Articles

Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

This paper proposes a synthesis of a dynamic controller under constraints. It is based on model predictive control (MPC) approach and resolution of a convex optimisation problem with linear matrix inequalities (LMI). The controller guarantees the closed-loop stability for polytopic time-varying uncertain systems. Conditions are provided for the controller design based on the parameter dependent Lyapunov functions (PDLF). A new demonstration is developed based on the relaxation technique, to include a slack variables Gi. The new LMI's formulation offers an additional degree of freedom for the controller design. Input and output constraints are also taken into account during the design of the controller. This approach allows varying and adjusting the dynamic of system by taking into account input/output constraints.

Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

This paper proposes a synthesis of a dynamic controller under constraints. It is based on model predictive control (MPC) approach and resolution of a convex optimisation problem with linear matrix inequalities (LMI). The controller guarantees the closed-loop stability for polytopic time-varying uncertain systems. Conditions are provided for the controller design based on the parameter dependent Lyapunov functions (PDLF). A new demonstration is developed based on the relaxation technique, to include a slack variables Gi. The new LMI's formulation offers an additional degree of freedom for the controller design. Input and output constraints are also taken into account during the design of the controller. This approach allows varying and adjusting the dynamic of system by taking into account input/output constraints.

A Hybrid Joint/Cartesian DMP-Based Approach for Obstacle Avoidance of Anthropomorphic Assistive Robots

Anthropomorphic criteria are widely adopted for developing socially interactive robots since they can improve human capability to interpret and predict robot motion, with an impact on robot acceptability and human–robot interaction safety. Learning by demonstration approaches based on dynamic movement primitives are a suitable solution for planning the robot motion in human-like fashion and endow robots with generalization capabilities and robustness against perturbation. Objective of this work is to propose a new formulation of the learning by demonstration approach based on dynamic movement primitives (DMPs), called hybrid joint/Cartesian DMPs, for redundant robots with the twofold purpose of avoiding obstacles on the path and obtaining anthropomorphic motion in the joint as well as the task space. The proposed approach, suitable for assistive purposes, was experimentally validated on the anthropomorphic robot arm Kuka Light Weight Robot 4+. Trajectories recorded by an optoelectronic system (i.e. the BTS Smart D) from human demonstrators performing point-to-point reaching and pouring tasks were adopted to teach the robot how to move. A comparative analysis with current methods of learning by demonstration in the literature, i.e. Cartesian DMP and inverse kinematics, was performed. Performance indicators were introduced to (1) assess the robot accuracy of the motion performing, (2) evaluate the level of anthropomorphism of the computed motion, (3) measure the success rate in obstacle avoidance. Moreover, questionnaires were administered to ten human subjects who observed robot motion. The obtained results demonstrated that the proposed approach guarantees anthropomorphic motion of the robot both in the joint and in the task spaces ensuring obstacle avoidance along the robot kinematic chain. In particular, the anthropomorphic robot, while operating with the proposed method, exhibits behaviours in the joint space that are similar to the one recorded during the demonstration and has a level of anthropomorphism rather higher than the one obtained with Cartesian DMP and inverse kinematics (a configuration of the robot joints within the physiological limits is always ensured and the maximum convex hull created between the human and the robot arm is 0.0085 m\(^3\)). In presence of obstacles, an acceptable level of the Cartesian accuracy (\(Position~err<0.005\) m and \(Orientation~err<0.02\) rad) is achieved. The obstacles are avoided with a 100% of success. The questionnaire results showed that the users feel more comfortable and less nervous to interact with a robot that moves in human-like manner.

Deep Neural Network Approach in Human-Like Redundancy Optimization for Anthropomorphic Manipulators

Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular solutions. To fulfill this requirement, a reconstruction method called swivel motion was adopted to achieve human-like imitation. This approach aims at modeling the regression relationship between robot pose and swivel motion angle. Then it reaches the human-like swivel motion using its redundant degrees of the manipulator. This characteristic holds for most of the redundant anthropomorphic robots. Although artificial neural network (ANN) based approaches show moderate robustness, the predictive performance is limited. In this paper, we propose a novel deep convolutional neural network (DCNN) structure for reconstruction enhancement and reducing online prediction time. Finally, we utilized the trained DCNN model for managing redundancy control a 7 DoFs anthropomorphic robot arm (LWR4+, KUKA, Germany) for validation. A demonstration is presented to show the human-like behavior on the anthropomorphic manipulator. The proposed approach can also be applied to control other anthropomorphic robot manipulators in industry area or biomedical engineering.

Online human-like redundancy optimization for tele-operated anthropomorphic manipulators

Robot human-like behavior can enhance the performance of human–robot cooperation with prominently improved natural interaction. This also holds for redundant robots with an anthropomorphic kinematics. In this article, we translated human ability of managing redundancy to control a seven degrees of freedom anthropomorphic robot arm (LWR4+, KUKA, Germany) during tele-operated tasks. We implemented a nonlinear regression method—based on neural networks—between the human arm elbow swivel angle and the hand target pose to achieve an anthropomorphic arm posture during tele-operation tasks. The method was assessed in simulation and experiments were performed with virtual reality tracking tasks in a lab environment. The results showed that the robot achieves a human-like arm posture during tele-operation, and the subjects prefer to work with the biologically inspired robot. The proposed method can be applied in control of anthropomorphic robot manipulators for tele-operated collaborative tasks, such as in factories or in operating rooms.

Gravity-dependent change in the \u2018light-from-above\u2019 prior

In environments where orientation is ambiguous, the visual system uses prior knowledge about lighting coming from above to recognize objects, determine which way is up, and reorient the body. Here we investigated the extent with which assumed light from above preferences are affected by body orientation and the orientation of the retina relative to gravity. We tested the ability to extract shape-from-shading with seven human male observers positioned in multiple orientations relative to gravity using a modified KUKA anthropomorphic robot arm. Observers made convex-concave judgments of a central monocularly viewed stimulus with orientations of a shading gradient consistent with being lit from one of 24 simulated illumination directions. By positioning observers in different roll-tilt orientations relative to gravity and when supine, we were able to monitor change in the light-from-above prior (the orientation at which a shaded disk appears maximally convex). The results confirm previous findings that the light-from-above prior changes with body orientation relative to gravity. Interestingly, the results varied also with retinal orientation as well as an additional component that was approximately twice the frequency of retinal orientation. We use a modelling approach to show that the data are well predicted by summing retinal orientation with cross-multiplied utricle and saccule signals of the vestibular system, yielding gravity-dependent biases in the ability to extract shape-from-shading. We conclude that priors such as light coming from above appear to be constantly updated by neural processes that monitor self-orientation to achieve optimal object recognition over moderate deviations from upright posture at the cost of poor recognition when extremely tilted relative to gravity.

Anthropomorphic musculoskeletal 10 degrees-of-freedom robot arm driven by pneumatic artificial muscles

ABSTRACTThis paper describes the construction and design decisions of an anthropomorphic musculoskeletal robot arm actuated by pneumatic artificial muscles. This robot was designed to allow human-inspired compliant movements without the need to replicate the human body-structure in detail. This resulted in an mechanically simple design while preserving the motoric characteristics of a human. Besides the constructional details of the robot we will present two experiments to show the robots abilities regarding to its dexterity and compliance.

A teleoperated control approach for anthropomorphic manipulator using magneto-inertial sensors.

In this paper we propose and validate a teleoperated control approach for an anthropomorphic redundant robotic manipulator, using magneto-inertial sensors (IMUs). The proposed method allows mapping the motion of the human arm (used as the master) on the robot end-effector (the slave). We record arm movements using IMU sensors, and calculate human forward kinematics to be mapped on robot movements. In order to solve robot kinematic redundancy, we implemented different algorithms for inverse kinematics that allows imposing anthropomorphism criteria on robot movements. The main objective is to let the user to control the robotic platform in an easy and intuitive manner by providing the control input freely moving his/her own arm and exploiting redundancy and anthropomorphism criteria in order to achieve human-like behaviour on the robot arm. Therefore, three inverse kinematics algorithms are implemented: Damped Least Squares (DLS), Elastic Potential (EP) and Augmented Jacobian (AJ). In order to evaluate the performance of the algorithms, four healthy subjects have been asked to control the motion of an anthropomorphic robot arm (i.e. the Kuka Light Weight Robot 4+) through four magneto-inertial sensors (i.e. Xsens Wireless Motion Tracking sensors - MTw) positioned on their arm. Anthropomorphism indices and position and orientation errors between the human hand pose and the robot end-effector pose were evaluated to assess the performance of our approach.

Development of a multi-DoF transhumeral robotic arm prosthesis

An anthropomorphic transhumeral robotic arm prosthesis is proposed in this study. It is capable of generating fifteen degrees-of-freedom, seven active and eight passive. In order to realize wrist motions, a parallel manipulator-based mechanism is proposed. It simulates the human anatomical structure and generates motions in two axes. The hand-of-arm prosthesis consists of under-actuated fingers with intrinsic actuation. The finger mechanism is capable of generating three degrees of freedom, and it exhibits the capability of adjusting the joint angles passively according to the geometry of the grasping object. Additionally, a parameter to evaluate finger mechanisms is introduced, and it measures the adoptability of a finger mechanism. In order to verify the mechanism's efficacy in terms of motion generation, motion simulation and kinematic analysis were carried out. Results demonstrated that the mechanisms are capable of generating the required motions.

Self-Organized Behavior Generation for Musculoskeletal Robots.

With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object's identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors “waiting” to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics.

A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating.

Reaching for objects and grasping them is a fundamental skill for any autonomous robot that interacts with its environment. Although this skill seems trivial to adults, who effortlessly pick up even objects they have never seen before, it is hard for other animals, for human infants, and for most autonomous robots. Any time during movement preparation and execution, human reaching movement are updated if the visual scene changes (with a delay of about 100 ms). The capability for online updating highlights how tightly perception, movement planning, and movement generation are integrated in humans. Here, we report on an effort to reproduce this tight integration in a neural dynamic process model of reaching and grasping that covers the complete path from visual perception to movement generation within a unified modeling framework, Dynamic Field Theory. All requisite processes are realized as time-continuous dynamical systems that model the evolution in time of neural population activation. Population level neural processes bring about the attentional selection of objects, the estimation of object shape and pose, and the mapping of pose parameters to suitable movement parameters. Once a target object has been selected, its pose parameters couple into the neural dynamics of movement generation so that changes of pose are propagated through the architecture to update the performed movement online. Implementing the neural architecture on an anthropomorphic robot arm equipped with a Kinect sensor, we evaluate the model by grasping wooden objects. Their size, shape, and pose are estimated from a neural model of scene perception that is based on feature fields. The sequential organization of a reach and grasp act emerges from a sequence of dynamic instabilities within a neural dynamics of behavioral organization, that effectively switches the neural controllers from one phase of the action to the next. Trajectory formation itself is driven by a dynamical systems version of the potential field approach. We highlight the emergent capacity for online updating by showing that a shift or rotation of the object during the reaching phase leads to the online adaptation of the movement plan and successful completion of the grasp.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms.

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

Wireless Brain-Robot Interface: User Perception and Performance Assessment of Spinal Cord Injury Patients

Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Multi-DoF robotics control is possible by patients through commercial wireless BCI. Multiple sessions and tailored BCI algorithms are needed to improve performance.

Control of Redundant Kinematic Degrees of Freedom in a Closed-Loop Brain-Machine Interface.

Brain-machine interface (BMI) systems use signals acquired from the brain to directly control the movement of an actuator, such as a computer cursor or a robotic arm, with the goal of restoring motor function lost due to injury or disease of the nervous system. In BMIs with kinematically redundant actuators, the combination of the task goals and the system under neural control can allow for many equally optimal task solutions. The extent to which kinematically redundant degrees of freedom (DOFs) in a BMI system may be under direct neural control is unknown. To address this question, a Kalman filter was used to decode single- and multi-unit cortical neural activity of two macaque monkeys into the joint velocities of a virtual four-link kinematic chain. Subjects completed movements of the chain's endpoint to instructed target locations within a two-dimensional plane. This system was kinematically redundant for an endpoint movement task, as four DOFs were used to manipulate the 2-D endpoint position. Both subjects successfully performed the task and improved with practice by producing faster endpoint velocity control signals. Kinematic redundancy allowed null movements whereby the individual links of the chain could move in a way that cancels out and does not result in endpoint movement. As the subjects became more proficient at controlling the chain, the amount of null movement also increased. Task performance suffered when the links of the kinematic chain were hidden and only the endpoint was visible. Furthermore, all four DOFs of the joint-velocity control space exhibited task-relevant modulation. The relative usage of each DOF depended on the configuration of the chain, and trials in which the less-prominent DOFs were utilized also had better task performance. Overall, these results indicate that the subjects incorporated the redundant components of the control space into their control strategy. Future BMI systems with kinematic redundancy, such as exoskeletal systems or anthropomorphic robotic arms, may benefit from allowing neural control over redundant configuration dimensions as well as the end-effector.

Total mesorectal excision using a soft and flexible robotic arm: a feasibility study in cadaver models.

Sponsored by the European Commission, the FP7 STIFF-FLOP project aimed at developing a STIFFness controllable Flexible and Learn-able manipulator for surgical operations, in order to overcome the current limitations of rigid-link robotic technology. Herein, we describe the first cadaveric series of total mesorectal excision (TME) using a soft and flexible robotic arm for optic vision in a cadaver model. TME assisted by the STIFF-FLOP robotic optics was successfully performed in two embalmed male human cadavers. The soft and flexible optic prototype consisted of two modules, each measuring 60mm in length and 14.3mm in maximum outer diameter. The robot was attached to a rigid shaft connected to an anthropomorphic manipulator robot arm with six degrees of freedom. The controller device was equipped with two joysticks. The cadavers (BMI 25 and 28kg/m2) were prepared according to the Thiel embalming method. The procedure was performed using three standard laparoscopic instruments for traction and dissection, with the aid of a 30° rigid optics in the rear for documentation. Following mobilization of the left colonic flexure and division of the inferior mesenteric vessels, TME was completed down to the pelvic floor. The STIFF-FLOP robotic optic arm seemed to acquire superior angles of vision of the surgical field in the pelvis, resulting in an intact mesorectum in both cases. Completion times of the procedures were 165 and 145min, respectively. No intraoperative complications occurred. No technical failures were registered. The STIFF-FLOP soft and flexible robotic optic arm proved effective in assisting a laparoscopic TME in human cadavers, with a superior field of vision compared to the standard laparoscopic vision, especially low in the pelvis. The introduction of soft and flexible robotic devices may aid in overcoming the technical challenges of difficult laparoscopic procedures based on standard rigid instruments.

Path Planning and Co-Simulation Control of 8 DOF Anthropomorphic Robotic Arm

Path Planning and Co-Simulation Control of 8 DOF Anthropomorphic Robotic Arm

Closed form inverse kinematics solution for a redundant anthropomorphic robot arm

An inverse kinematics solution of a redundant 7R serial chain that mimics the human arm is presented. Such manipulators are composed of two spherical wrists with one revolute joint in between. In the case of non-redundant manipulators the inverse kinematics yields a discrete set of solutions for the joint axes to reach a given end effector position and orientation. For redundant arms, however, the solution consists of a one parameter set. In contrast to other solutions to this problem herein a closed form solution is given without the need of specifying the design, task nor the redundant parameter in the solution process. That gives the possibility to use the degree of freedom provided by the additional joint for different applications, such as avoiding singularities, joint limits and collisions or to apply an optimization algorithm to achieve, for example, minimal joint velocities or joint movements.

Long-term stability of sensitivity to intracortical microstimulation of somatosensory cortex

Objective. The dexterous manipulation of objects depends heavily on somatosensory signals from the limb. The development of anthropomorphic robotic arms and of algorithms to decode intended movements from neuronal signals has stimulated the need to restore somatosensation for use in upper-limb neuroprostheses. Without touch and proprioception, patients have difficulty controlling prosthetic limbs to a level that justifies the required invasive surgery. Intracortical microstimulation (ICMS) through chronically implanted electrode arrays has the potential to provide rich and intuitive sensory feedback. This approach to sensory restoration requires, however, that the evoked sensations remain stable over time. Approach. To investigate the stability of ICMS-evoked sensations, we measured the ability of non-human primates to detect ICMS over experimental sessions that spanned years. Main results. We found that the performance of the animals remained highly stable over time, even when they were tested with electrodes that had experienced extensive stimulation. Significance. Given the stability of the sensations that it evokes, ICMS may thus be a viable approach for sensory restoration.

On Configuration Trajectory Formation in Spatiotemporal Profile for Reproducing Human Hand Reaching Movement.

Most functional reaching activities in daily living generally require a hand to reach the functional position in appropriate orientation with invariant spatiotemporal profile. Effectively reproducing such spatiotemporal feature of hand configuration trajectory in real time is essential to understand the human motor control and plan human-like motion on anthropomorphic robotic arm. However, there are no novel computational models in literature toward reproducing hand configuration-to-configuration movement in spatiotemporal profile. In response to the problem, this paper presents a computational framework for hand configuration trajectory formation based on hierarchical principle of human motor control. The composite potential field is constructed on special Euclidean Group to induce time-varying configuration toward target. The dynamic behavior of hand is described by a second-order kinematic model to produce the external representation of high-level motor control. The multivariate regression relation between intrinsic and extrinsic coordinates of arm, is statistically analyzed for determining the arm orientation in real time, which produces the external representation of low-level motor control. The proposed method is demonstrated in an anthropomorphic arm by performing several highly curved self-reaching movements. The generated configuration trajectories are compared with actual human movement in spatiotemporal profile to validate the proposed method.

Detection and Tele-replication of Human Hand Motions by a Robotic Hand

This paper presents a tele-operated robotic hand controlled by replication of human hand motions and is focused on the description of technical solutions for detection and tele-replication of movements, in order to control a robotic hand. The purpose of such research is justified by the need of high precision human controlled operations in special environments. The system is based on a flex sensors set with processing units and has as effectors a robotic arm and an anthropomorphic hand. The current article displays the modality of achieving an anthropomorphic robotic arm capable of efficiently handling objects of different sizes. In order to implement and test the technical and computing solutions, the authors have used a commercial product as experimental platform and improved it both in its mechanical structure and in its command and control system. For implementing the motion algorithms of the robotic arm, a method was developed for decoding arm movements performed by a human operator. To this end, bending sensors placed at the human operator’s joints (shoulder, elbow, wrist and fingers) were used. Signals collected from the sensors during the realization of these different movements by the human operator were decoded, processed and implemented in the drive system corresponding to the anthropomorphic robotic arm. In this regard, all sets of complex movements by the human arm operator were duplicated and implemented in the anthropomorphic robotic arm. The results obtained in handling various objects by means of using the anthropomorphic robotic arm have certified the effectiveness of this method.