Kinematic Redundancy Resolution for Baxter Robot

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.

Redundancy of degrees of freedom (DOFs) during natural human movements is a central problem of motor control research. This study tests a novel interpretation that during arm movements, the DOF redundancy is used to support a preferred, simplified joint control pattern that consists of rotating either the shoulder or elbow actively and the other (trailing) joint predominantly passively by interaction and gravitational torques. We previously revealed the preference for this control pattern during nonredundant horizontal arm movements. Here, we studied whether this preference persists during movements with redundant DOFs and the redundancy is used to enlarge the range of directions in which this control pattern can be utilized. A free-stroke drawing task was performed that involved production of series of horizontal center-out strokes in randomly selected directions. Two conditions were used, with the arm's joints unconstrained (U) and constrained (C) to the horizontal plane. In both conditions, directional preferences were revealed and the simplified control pattern was used in the preferred and not in nonpreferred directions. The directional preferences were weaker and the range of preferred directions was wider in the U condition, with higher percentage of strokes performed with the simplified control pattern. This advantage was related to the usage of additional DOFs. We discuss that the simplified pattern may represent a feedforward control strategy that reduces the challenge of joint coordination caused by signal-dependent noise during movement execution. The results suggest a possibility that the simplified pattern is used during the majority of natural, seemingly complex arm movements.

For use as five-finger myoelectric hands in daily living, robotic hands must be 1) lightweight and human sized as well as possess 2) human-like multiple degrees of freedom (DoF) and a high grip force. The compatibility of these requirements is a trade-off; ideal robotic hands have yet to sufficiently satisfy both these requirements. Herein a power allocation mechanism is proposed to improve the grip force without increasing the size or weight of robotic hands using redundant DoF during pinching motions. Additionally, this mechanism is applied to an actual five-finger myoelectric hand, which can make seven types of motions that are necessary for activities of daily living (ADL) and realizes about a 60% improvement in fingertip force, allowing three fingers to pinch objects exceeding 1 kg.

In comparison to classical serial mechanisms parallel kinematic machines (PKM) provide a higher accuracy, a higher stiffness, and higher dynamic properties. However, parallel robots suffer from the presence of singularities within their workspace (Gosselin & Angeles, 1990). In such configurations the moving platform gains at least one degree of freedom (DOF) and the actuation forces become (in theory) infinite. As a result, the kinematic structure can be damaged or even destroyed. Additionally, several performance indices, e.g. the achievable accuracy, are directly related to the singularity loci (Kotlarski, Abdellatif & Heimann, 2008). The closer the endeffector (EE) is ’located’ to a singularity the higher is the pose error resulting from the influence of active joint errors, e.g. from limited encoder resolution. In order to minimize the singularity loci of parallel mechanisms and to increase their performance, e.g. their achievable accuracy, redundancy can be used (Merlet, 1996). Two redundancy approaches are established for PKM, actuation redundancy and kinematic redundancy (Kock & Schumacher, 1998; Wang & Gosselin, 2004). Actuation redundancy can be realized whether by adding a kinematic chain to the mechanism or by actuating a passive joint. Amongst others, it reduces singular configurations and leads to internal preload that can be controlled in order to prevent backlash (Kock, 2001). However, the control of such mechanisms is a challenging task (Muller, 2005). Furthermore, an additional kinematic chain mostly reduces the total workspace. Therefore, kinematic redundancy is proposed realized by adding at least one actuated joint to one kinematic chain (Cha et al., 2007; Mohamed & Gosselin, 2005). It is well known that the singularity loci as well as the achievable accuracy are greatly affected by the geometrical parameters of a mechanism (Kotlarski, de Nijs, Abdellatif & Heimann, 2009; Merlet & Daney, 2005), and are therefore highly dependent on the mechanism’s actual configuration. In this chapter, as examples, kinematically redundant versions of the well known planar 3RRR and 3RPR mechanisms (Gosselin & Angeles, 1988; Zein et al., 2006) are considered. In each case, an additional prismatic actuator is added to an arbitrary base joint. The introduced mechanisms are denoted as 3(P)RRR and 3(P)RPR. Thanks to the additional prismatic actuator, the inverse displacement problem has an infinite number of solutions (Ebrahimi et al., 2007). Hence, reconfigurations of the mechanisms can be performed selectively in order to avoid singularities and to affect their performance directly (Kotlarski, Do Thanh, Abdellatif & Heimann, 2008). It is important to note that with respect to the work of Arakelian et al. (Arakelian et al., 2008), kinematic redundancy can be used to rather change the geometrical parameters of a mechanism than its basic structure. This can be done at the 19

Determining required joint angles to achieve a desired position in a manipulator’s arm is a complicated problem without simple analytical solutions. This paper researches several computational methods based on artificial intelligence (AI) for calculating the joint positions of the 6-DOF robotic arm. We can extrapolate relevance, for example, to the crucial role that robotic manipulator arms play in industrial and medical applications, where enhanced precision and movement efficiency may sharply boost performance and expand applicability. Here, we investigate the effectiveness of methods, such as the artificial immune system (AIS) and multi-layer perceptron-biogeography-based optimization (MLP-BBO). Those AI-driven methods have been applied to determine joint angles for reaching desired positions through simulations for the robotic arm. The results show that the AIS and MLP-BBO approach can handle the intrinsic complexities of the task, thus testifying to the practicability and dependability of these two methods in this application. From the findings in the study, it was indicated that AI-driven techniques can effectively answer the complex problem of the robotic manipulator arm in finding joint angles.

Demands have been increasing for skillful robots in many fields including atomic power generation, ocean bottom development, space craft, hospital aids, and so on. Such robots will essentially require high degrees of freedom. This paper discusses the advantages of a robot equipped with redundant degrees of freedom and outlines a method of positive use of such redundancy.

In this paper, a learning control algorithm for the motion and force of a direct-drive robot, which has a variable table and six degrees of freedom (d. o. f.), is proposed for a task in constrained work space. The five-d. o. f. relative motion of an end effector attached to the wrist of the robot arm and the internal force between the end effector and the table through an object are achieved by utilizing the redundancy to avoid interference with the object. A hybrid controller for an end effector and a position controller for a robot arm are used. Under the over-constrained conditions, which are imposed by the two tasks stated above, a learning control algorithm using neural networks (NN's)is employed to control an end effector with higher priority than the robot arm. The proposed control algorithm is applied to an experiment on the control of both the position of an end effector and the contact force on the surface of the object while avoiding interference with the object. As a result, it has been shown that the trajectories of robot joints can be generated in real-time computation and a high accuracy of the end effector is obtained after the completion of learning. The effectiveness of the proposed control algorithm is demonstrated by the experiment.

Redundancy facilitates some of the most remarkable capabilities of humans, and is therefore omni-present in our physiology. The relationship between redundancy in robotics and biology is investigated in detail on the Series Elastic Dual-Motor Actuator (SEDMA), an actuator inspired by the kinematic redundancy exhibited by myofibrils. The actuator consists of two motors coupled to a single spring at the output. Such a system has a redundant degree of freedom, which can be exploited to optimize aspects such as accuracy, impedance, fault-tolerance and energy efficiency. To test its potential for human-like motions, the SEDMA actuator is implemented in a hopping robot. Experiments on a physical demonstrator show that the robot’s movement patterns resemble human squat jumps. We conclude that robots with bio-inspired actuator designs facilitate human-like movement, although current technical limitations may prevent them from reaching the same dynamic and energetic performance.

In this work, the motions of an underwater remotely operated vehicle (ROV) and a spatial manipulator are coordinated using a consolidated controller. The controller translates a single pilot command, the desired position and orientation of the end effector, into a coordinated set of ROV and manipulator joint motions that satisfy the pilot intent in addition to a series of secondary objectives. The controller relies on a unified dynamic model of the system. The quasi-Lagrange method is used to derive the equations of motion in terms of the ROV body-fixed frame. For the control problem, a novel sliding-mode based controller is proposed. The controller contains two layers of adaptivity. The first layer is for adjusting PID gains, whereas the second layer is for estimating the bound on a lumped uncertainty vector. The second level of adaptation is shown to relax the Lyapunov stability requirements leading to a more robust controller. To generate reference state values, a redundancy resolution technique is utilized that is based on the gradient projection method merged with a fuzzy determination of the hierarchy of the secondary objectives. The redundancy resolution method distributes the pilot's end-effector command over the ROV and the manipulator in an optimal manner using the redundant degrees of freedom. The results illustrate that detailed subsea tasks can be completed with a small, low-cost ROVM system using the proposed unified control scheme.

Camera Holding Robotic Devices in Urology

Influenced by research in biomechanics engineers attempt to achieve motion flexibility of the robot arm similar to that of a human hand motion. For this purpose the design of modern robot arms most often provides redundant degrees of freedom in executing the assigned tasks in world space. Typical industrial applications of robots involve motion planning subject to constraints in joint space. In the general case additional static constraints in world space can also be mapped into admissible ranges of variation of the joint variables. This paper presents a mathematical model for redundant robot motion control in case of hard constraints on the displacements in the joints. The novelty in this model is the application of sliding mode theory based on the separation of the immutable geometrical artefacts in path planning from the changes in the actual robot motion. This allows to replace the traditionally heavy optimization procedures used with redundant robot arms by computationally efficient and intuitive vector space methods. The sliding mode of control is seamlessly embedded in a uniform kinematic path control algorithm and it is activated only when needed. Unlike existing methods, the sliding mode for motion control in case of constraints on the joint variables can execute concurrently with other forms motion control.

While John Holland has always envisioned learning classifier systems (LCSs) as cognitive systems, most work on LCSs has focused on classification, datamining, and function approximation. In this paper, we show that the XCSF classifier system can be very suitably modified to control a robot system with redundant degrees of freedom, such as a robot arm. Inspired by recent research insights that suggest that sensorimotor codes are nearly ubiquitous in the brain and an essential ingredient for cognition in general, the XCSF system is modified to learn classifiers that encode piecewise linear sensorimotor structures, which are conditioned on prediction-relevant contextual input. In the investigated robot arm problem, we show that XCSF partitions the (contextual) posture space of the arm in such a way that accurate hand movements can be predicted given particular motor commands. Furthermore, we show that the inversion of the sensorimotor predictive structures enables accurate goal-directed closed-loop control of arm reaching movements. Besides the robot arm application, we also investigate performance of the modified XCSF system on a set of artificial functions. All results point out that XCSF is a useful tool to evolve problem space partitions that are maximally effective for the encoding of sensorimotor dependencies. A final discussion elaborates on the relation of the taken approach to actual brain structures and cognitive psychology theories of learning and behavior.

A biomimetic reach and grasp approach for mechanical hands

The solution of task of maintaining the dynamic accuracy of control of working tools of multilink manipulators (MM) when they move along arbitrary spatial trajectories is presented in this paper. In this case, constructive restrictions in all degrees of freedom (DoF) of manipulators and special cases of location of their links are taken into account. These special (singular) positions are characterized by ambiguity in solving inverse kinematics problem of these MM. Maintaining of control accuracy is proposed to be ensured by excluding the enter of all DoFs of manipulators to the restrictions, as well as excluding the enter of their working tools to the boundaries of the working area. This is accomplished by using a redundant DoF when approaching these undesirable positions. In the first part of the article, the features of the new solution of the inverse kinematics problem are considered and singular positions for 6-DoF manipulators with PUMA kinematic schemes are described. The presented solution of the inverse kinematics problem takes into account various combinations of genera- lized coordinates of the MM, which ensure the movement of their working tools to the specified positions with the required spatial orientation. This solution has low computational complexity compared to other known methods. This allows the use of low-power microprocessor computers to control the MM. The results of the performed mathematical simulation confirmed the high efficiency of using the proposed approach to the contour control of the MM.

Abstract. Safety in human–robot physical interaction and cooperation is of paramount interest. In this work, a human–humanoid interaction and cooperation scenario is considered. The robot arm is controlled by a proportional derivative (PD) controller in combination with an inherently fault-tolerant sliding-mode controller. During normal operation, if any of the joints of the robot arm develops a fault, the robot arm end effector may go into chaotic and dangerous motion. If such a scenario occurs, it poses a serious danger to the human in the loop as well as the robot. In this paper, an adaptive chaos sliding-mode controller is proposed to recover the tracking of the end effector when a fault occurs in any of the actuators. This tracking restoration is very important to complete a safety-critical task. The proposed scheme may help in addressing some safety issues arising from a joint failure, allowing it to finish the task at hand and stop it from going into a dangerous situation. The scheme is tested in simulation on the four degrees of freedom (DOF) model of the Bristol Robotics Laboratory (BRL) robot arm. Simulation results show efficient tracking recovery after a joint actuator failure. The controller demonstrated good performance in terms of tracking and stability when dealing with different joint actuator failures.