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This paper investigates the effect of inverse kinemat-ics (IK) on operator performance during the telemanipulation of an industrial robot. Robotic teleoperation is often preferred when manipulating objects in extreme conditions. In many applications, e.g., hazardous and high-consequence environments, operators cannot directly perceive the robot motions and have to rely only on CCTV views of the scene for situational awareness while teleoperating the heavy-duty industrial robots. Making best guesses for the IK plays a significant role on the task success rate and increases the operator cognitive load significantly. In this context, we develop a new optimisation-based IK solver that is robust with respect to the robot's singularities and assists the operator in generating smooth trajectories. Inspired by a successful algorithm used in computer graphics to solve the IK problem and devise smooth movements (FABRIK), our algorithm takes advantage also of the kinematic structure of the robot in order to decouple the notoriously difficult IK problem of orientation and position. To evaluate the effectiveness of the proposed method, we have compared its performance to that of the commonly used Jacobian pseudo inverse-based method in terms of positional accuracy and task-space reachability. We also report the results of telemanipulation experiments with human test-subjects. Our proposed IK algorithm outperforms classical IK methods on both objective metrics of task success, and subjective metrics of operator preference.

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... Most classical solutions operate with inversions of potentially singular matrices, motivating the development of singularity robust techniques [12]. This topic has been investigated recently and is not entirely solved for serial manipulators [13,20,22]. ...

... In an investigation of singularity robustness for teleoperated robots, Ortenzi [13] developed an algorithm which first finds the positions for wrist and elbow using FABRIK and then, to find the joint angles, solved a two-objective optimization problem (wrist and elbow). In their experiments, they compared the performance of the algorithms and user experience in teleoperating the robot. ...

This paper presents a Jacobian-based solution for inverse kinematics of serial rigid link robots while providing an intuitive way to control joint priorities with virtual inertia parameters. We improve the Transposed Jacobian by pre-multiplying it with the Joint Space Inertia Matrix, resulting in an algorithm that we named JTi-IK, which has shown equivalent convergence performance in iterations when compared to the Damped Least Squares method in our experiments. Pre- or post-multiplication of the Transposed Jacobian by a matrix is a concept pursued by the most basic methods. In our approach, we explore the matrices related to dynamics as an implementation of this concept in a way that only positive-definite matrices are inverted, so there are no singularities. After simplifications on the Joint Space Inertia Matrix, we reach the Virtual Inertia Matrix, which allows modifications on virtual weight distributions as control parameters for prioritization of different joints, affecting body posture without extra algorithm steps. In this paper, we also discuss the stabilizing effects of pre-multiplication matrices that appear in most Jacobian-derived methods. As it is also based on the Jacobian, our method inherits its generality, being valid for any serial manipulator structure. When evaluating JTi-IK for a planar robot, we reach the same results obtained in our previous paper. This is a very interesting result, since the methods are derived in distinct ways.

... Multiple methods have been proposed to solve the inverse kinematics problem of 7-DOF manipulators, and the most widely used approach is velocity solution based on Jacobian matrix, either in closed or iterative form [8][9][10][11][12] . Although these solutions are versatile and have a wide range of applications, they still exhibit several disadvantages: i) require complicated calculations and expansive time costs; ii) have low solution accuracy due to cumulative errors; iii) have Jacobian singularities; iv) need to pre-assign task trajectory. ...

The inverse solution calculation speed and the configuration control are very important for anthropomorphic manipulators with 7 degree-of-freedom (DOF). In order to realize the combined control of global arm configuration manifolds and local self-motion of 7-DOF anthropomorphic manipulators without offset, an analytical inverse kinematics solution method is proposed in this paper. By defining a new arm reference plane for 7-DOF anthropomorphic manipulators, it uses arm angle to deal with local self-motion and orientation control to deal with global arm configuration manifolds. All possible inverse kinematics solutions for target pose can be intuitively expressed with the assistance of the parameters of arm angle and orientation control. Meanwhile, the corresponding singularity is also taken into account and a detection metric is provided. Furthermore, the problem of joint limits avoidance is mapped to the selection of arm angle in null space, and the Self-Adaptive Particle Swarm Optimization algorithm is introduced to obtain the optimal arm angle that can maximize the avoidance of joint limits. Simulation results show the proposed method has such advantages as fast calculation speed, high precision and high stability.

... If the above approaches are used to plan motions on a surface for a contacting end-effector, the resulting path may pass through regions: that lie outside the robot's reachable workspace; that correspond to singular configurations of the robot; where the robot is unable to exert required cutting forces. Hence, for tasks requiring contact between the robot and a surface, path-planning should include additional costfunctions and metrics such as manipulability [2], [13], [14]. ...

This paper presents a path planner, which enables a nonholonomic mobile manipulator to move its end-effector on an observed surface with a constrained orientation, given start and destination points. A partial point cloud of the environment is captured using a vision-based sensor, but no prior knowledge of the surface shape is assumed. We consider the multi-objective optimisation problem of finding robot paths which account for the nonholonomic constraints of the base, maximise the robot's manipulability throughout the motion, while also minimising surface-distance travelled between the two points. This work has application in industrial problems of rough robotic cutting, e.g. demolition of legacy nuclear plants, where dismantling does not require a precise path. We show how our approach embeds the nonholonomic constraints of the mobile platform into an extended Jacobian, while additionally encoding the constraint that the end-effector must remain in contact with the cut surface throughout the motion. We use this constrained Jacobian to plan a time-series of robot configurations. Additionally, we show how our novel cost function is suitable for combining with a variety of well-known path planners, such as RRT*. We present several empirical experiments in different scenarios, where a simulated non-holonomic mobile manipulator follows a trajec-tory, which is generated on noisy point clouds derived from real depth-camera images of real objects. Our planner (RRT*-CRMM) enables successful task completion by optimising the path over the travelled distance, the manipulability of the arm, and the movements of the mobile base.

... Interestingly, humans prefer robot configurations that are more natural or human-like as they are more readable [143]. Inverse kinematics algorithms mapping Cartesian motions to the robot's joint space can also aim at devising overall movements for the robot that are legible to the human partner [144], [145]. ...

This article surveys the literature on human-robot object handovers. A handover is a collaborative joint action where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: 1) a pre-handover phase, and 2) the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, learning) and the physical level (e.g., motion, grasping, grip release) of the handover, we briefly discuss also the concepts of safety, social context, and ergonomics. We compare the behaviours displayed during human-to-human handovers to the state of the art of robotic assistants, and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.

Modular manipulators have broad application prospects in the field of narrow confined space owing to their characteristics of superior dexterity. However, compared with traditional ones, their mechanism design, modeling and inverse kinematics (IK) are challenging due to their special structures and redundant degrees of freedom. In this paper, a modular cable-driven manipulator (CDM) is designed. A lightweight and expandable structure is proposed to reduce weight of the whole manipulator and improve its environmental adaptability. To calibrate its global posture, angle sensors are equipped with its joints. Its kinematics are rigorously analyzed. To obtain the IK of a hyper-redundant CDM in real-time, a fast heuristic method with adaptive joint constraints is introduced. Then, a segmented IK strategy is proposed by extending the IK solver to local CDM, which realizes the local joint migration motion under the stable overall configuration. Finally, numerical simulations are conducted and a physical prototype is developed to carry out experiments. The results show that the designed CDM has great performance in dexterity and accuracy.

This article surveys the literature on human–robot object handovers. A handover is a collaborative joint action, where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: a prehandover phase and the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, and learning) and the physical level (e.g., motion, grasping, and grip release) of the handover, we also discuss safety. We compare the behaviors displayed during human-to-human handovers to the state of the art of robotic assistants and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches.

In this letter, we present a method for resolving kinematic redundancy using a human motion database, with application to teleoperation of bimanual humanoid robots using low-cost devices. Handheld devices for virtual reality applications can realize low-cost interfaces for operating such robots but available information does not uniquely determine the arm configuration. The resulting arm motions may be unnatural and inconsistent due to the kinematic redundancy. The idea explored in this paper is to construct a human motion database in advance using an interface that can directly measure the whole arm configuration such as motion capture. During teleoperation, the database is used to infer the appropriate arm configuration, grasp forces, and object trajectory based on the end effector trajectories measured by low-cost devices. The database employs Bayesian Interaction Primitives that have been used for modeling human-robot interactions.

We present early pilot-studies of a new international project, developing advanced robotics to handle nuclear waste. Despite enormous remote handling requirements, there has been remarkably little use of robots by the nuclear industry. The few robots deployed have been directly teleoperated in rudimentary ways, with no advanced control methods or autonomy. Most remote handling is still done by an aging workforce of highly skilled experts, using 1960s style mechanical Master-Slave devices. In contrast, this paper explores how novice human operators can rapidly learn to control modern robots to perform basic manipulation tasks; also how autonomous robotics techniques can be used for operator assistance, to increase throughput rates, decrease errors, and enhance safety. We compare humans directly teleoperating a robot arm, against human-supervised semi-autonomous control exploiting computer vision, visual servoing and autonomous grasping algorithms. We show how novice operators rapidly improve their performance with training; suggest how training needs might scale with task complexity; and demonstrate how advanced autonomous robotics techniques can help human operators improve their overall task performance. An additional contribution of this paper is to show how rigorous experimental and analytical methods from human factors research, can be applied to perform principled scientific evaluations of human test-subjects controlling robots to perform practical manipulative tasks.

This paper addresses the problem of grasping arbitrarily shaped objects, observed as partial point-clouds, without requiring: models of the objects, physics parameters, training data, or other a-priori knowledge. A grasp metric is proposed based on Local Contact Moment (LoCoMo). LoCoMo combines zero-moment shift features, of both hand and object surface patches, to determine local similarity. This metric is then used to search for a set of feasible grasp poses with associated grasp likelihoods. LoCoMo overcomes some limitations of both classical grasp planners and learning-based approaches. Unlike force-closure analysis, LoCoMo does not require knowledge of physical parameters such as friction coefficients, and avoids assumptions about fingertip contacts, instead enabling robust contacts of large areas of hand and object surface. Unlike more recent learning-based approaches, LoCoMo does not require training data, and does not need any prototype grasp configurations to be taught by kinesthetic demonstration. We present results of real-robot experiments grasping 21 different objects, observed by a wrist-mounted depth camera. All objects are grasped successfully when presented to the robot individually. The robot also successfully clears cluttered heaps of objects by sequentially grasping and lifting objects until none remain.

Inverse kinematics (IK) is the use of kinematic equations to determine the joint parameters of a manipulator so that the end effector moves to a desired position; IK can be applied in many areas, including robotics, engineering, computer graphics and video games. In this survey, we present a comprehensive review of the IK problem and the solutions developed over the years from the computer graphics point of view. The paper starts with the definition of forward and IK, their mathematical formulations and explains how to distinguish the unsolvable cases, indicating when a solution is available. The IK literature in this report is divided into four main categories: the analytical, the numerical, the data-driven and the hybrid methods. A timeline illustrating key methods is presented, explaining how the IK approaches have progressed over the years. The most popular IK methods are discussed with regard to their performance, computational cost and the smoothness of their resulting postures, while we suggest which IK family of solvers is best suited for particular problems. Finally, we indicate the limitations of the current IK methodologies and propose future research directions.

Despite enormous remote handling requirements, remarkably very few robots are being used by the nuclear industry. Most of the remote handling tasks are still performed manually, using conventional mechanical Master-Slave devices. The few robotic manipulators deployed are directly tele-operated in rudimentary ways, with almost no autonomy or even a pre-programmed motion. In addition, majority of these robots are under-sensored (i.e., with no proprioception), which prevents them to use for automatic tasks. In this context, primarily this chapter discusses the human operator performance in accomplishing heavy-duty remote handling tasks in hazardous environments such as nuclear decommissioning. Multiple factors are evaluated to analyse the human operators’ performance and workload. Also, direct human tele-operation is compared against human-supervised semi-autonomous control exploiting computer vision. Secondarily, a vision-guided solution towards enabling advanced control and automating the under-sensored robots is presented. Maintaining the coherence with real nuclear scenario, the experiments are conducted in the lab environment and results are discussed.

It is a common belief that service robots shall move in a human-like manner to enable natural and convenient interaction with a human user or collaborator. In particular, this applies to anthropomorphic 7-DOF redundant robot manipulators that have a shoulder-elbow-wrist configuration. On the kinematic level, human-like movement then can be realized by means of selecting a redundancy resolution for the inverse kinematics (IK), which realizes human-like movement through respective nullspace preferences. In this paper, key positions are introduced and defined as Cartesian positions of the manipulator’s elbow and wrist joints. The key positions are used as constraints on the inverse kinematics in addition to orientation constraints at the end-effector, such that the inverse kinematics can be calculated through an efficient analytical scheme and realizes human-like configurations. To obtain suitable key positions, a correspondence method named wrist-elbow-in-line is derived to map key positions of human demonstrations to the real robot for obtaining a valid analytical inverse kinematics solution. A human demonstration tracking experiment is conducted to evaluate the end-effector accuracy and human-likeness of the generated motion for a 7-DOF Kuka-LWR arm. The results are compared to a similar correspondance method that emphasizes only the wrist postion and show that the subtle differences between the two different correspondence methods may lead to significant performance differences. Furthermore, the wrist-elbow-in-line method is validated as more stable in practical application and extended for obstacle avoidance.

The article develops two novel feedback control-based Inverse Kinematics (IK) solvers. They are evaluated for a dual-manipulator mobile robotic system with application to nuclear decommissioning. The first algorithm has similarities to other feedback control based solvers, and borrows ideas from the Cyclic Coordinate Decent and the Jacobian Transpose methods. This yields a particularly straightforward algorithm with tunable Proportional-Integral-Derivative (PID) gains to determine performance. The second approach utilises a discrete-time state space modelling framework to solve the IK problem. Although the second solver is more complex to implement, preliminary simulation results for the case study example, show that it can converge quicker, and has improved immunity to the kinematic singularities that can occur in Jacobian based methods.

The Inverse Kinematics (IK) algorithms implemented in the open-source Orocos Kinematics and Dynamics Library (KDL) are arguably the most widely-used generic IK solvers worldwide. However, KDL's only joint-limit-constrained IK implementation, a pseudoinverse Jacobian IK solver, repeatedly exhibits false-negative failures on various humanoid platforms. In order to find a better IK solver for generic manipulator chains, a variety of open-source, drop-in alternatives have been implemented and evaluated for this paper. This article provides quantitative comparisons, using multiple humanoid platforms, between an improved implementation of the KDL inverse Jacobian algorithm, a set of sequential quadratic programming (SQP) IK algorithms that use a variety of quadratic error metrics, and a combined algorithm that concurrently runs the best performing SQP algorithm and the improved inverse Jacobian implementation. The best alternative IK implementation finds solutions much more often than KDL, is faster on average than KDL for typical manipulation chains, and (when desired) allows tolerances on each Cartesian dimension, further improving speed and convergence when an exact Cartesian pose is not possible and/or necessary.

In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (α, β, γ, δ) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10−3 seconds with a position error fitness around 3.116 × 10−8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10−9.

We propose a robust and fast solution for the inverse kinematic problem of general serial manipulators - i.e. any number and any combination of revolute and prismatic joints. The algorithm only requires the Denavit-Hartenberg (D-H) representation of the robot as input and no training or robot-dependent optimization function is needed. In order to handle singularities and to overcome the possibility of multiple paths in redundant robots, our approach relies on the computation of multiple (parallel) numerical estimations of the inverse Jacobian while it selects the current best path to the desire configuration of the end-effector. But unlike other iterative methods, our method achieves sub-millimeter accuracy in 20.48ms in average. The algorithm was implemented in C/C++ using 16 POSIX threads, and it can be easily expanded to use more threads and/or many-core GPUs. We demonstrate the high accuracy and the real-time performance of our method by testing it with five different robots, at both non-singular and singular configurations, including a 7-DoF redundant robot.

Research interest in human-robot interaction (HRI) has been increasing in recent decades and has now reached an appealing degree of maturity, which encourages first steps toward a technology transfer from research centers to robot manufacturers. Making robots able to cooperate with humans in a safe and intuitive manner is no longer a futuristic vision but a concrete opportunity that is just around the corner. On the other hand, measuring the quality of this interaction is a novel field in robotics research and will be extremely useful for future assessments of all efforts in the direction of more effective HRI. First results exist, ranging from a simple approach, like the so-called "uncanny valley" proposed in [1], to a more scientific interpretation supported by physiological measurements [2].

Planar two and three-link manipulators are often used in Robotics as testbeds for various algorithms or theories. In this paper, the case of a three-link planar manipulator is considered. For this type of robot a solution to the inverse kinematics problem, needed for generating desired trajectories in the Cartesian space (2D) is found by using a feed-forward neural network.

An equation is dangerous both if it is known and unknown. Three prime examples of such equations are as follows: Kelly's equation which indicates that the truth is estimated best when its observed value is regressed toward the mean of the group that it came from; the standard linear regression equation; and de Moivre's equation which provides the standard deviation of the sampling distribution of the mean. Of these three equations, de Moivre's equation is perhaps the most dangerous because of the extreme length of time over which ignorance of it has caused confusion and the variety of fields that have gone astray and the seriousness of the consequences that such ignorance has caused.

The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realiza- tion of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robot’s movement generation. The explicit modeling of such criteria usually require experts to adapt the robot’s movement gener- ation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configura- tion space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.

In this work we show how precomputed reachability information can be used to efficiently solve complex inverse kinematics (IK) problems such as bimanual grasping or re-grasping for humanoid robots. We present an integrated approach which generates collision-free IK solutions in cluttered environments while handling multiple potential grasping configurations for an object. Therefore, the spatial reachability of the robot's workspace is efficiently encoded by discretized data structures and sampling-based techniques are used to handle arbitrary kinematic chains. The algorithms are employed for single-handed and bimanual grasping tasks with fixed robot base position and methods are developed that allow to efficiently incorporate the search for suitable robot locations. The approach is evaluated in different scenarios with the humanoid robot ARMAR-III.

This work addresses the inverse kinematics problem for the 7 Degrees of Freedom Barrett Whole Arm Manipulator with link offsets. The presence of link offsets gives rise to the possibility of the in-elbow & out-elbow poses for a given end-effector pose and is discussed in the paper. A parametric solution for all possible geometric poses is generated for a desired end-effector pose (position and orientation). The set of possible geometric poses are completely defined by three circles in the Cartesian space. A method of computing the joint-variables for any geometric pose is presented. An analytical method of identifying a set of feasible poses for some joint-angle constraints is also addressed.

There is ample research on the effect of haptic teleoperation under delayed communication channels in terms of stability and system performance. Little attention, however, has been paid to the effect of delayed force feedback on users' task performance and whether force feedback is beneficial under significant communication delays. This paper investigates whether force feedback improves user's task performance in delayed teleoperation. We study peg-in-the-hole insertion/retraction, dexterous manipulation tasks involving high degrees of freedom and high forces at certain points during task execution. A user study involving unilateral (without force feedback), bilateral (with force feedback) and graphical feedback teleoperation under various delays is presented. We observed that for all feedback modalities, task completion times increase as delay increases. Haptic feedback helps reduce contact forces and the occurrence of large robot/environment forces. Furthermore, graphical feedback helps users maintain the lowest range of forces at the cost of higher task completion times. With users mindful of minimizing contact forces, haptic/graphical feedback causes the task to take more time than unilateral control. Therefore, when short completion times are crucial given a tolerance for larger forces, force feedback only serves to increase the time required to perform the task; thus, unilateral control may be sufficient.

Inverse Kinematics is defined as the problem of determining a set of appropriate joint configurations for which the end effectors move to desired positions as smoothly, rapidly, and as accurately as possible. However, many of the currently available methods suffer from high computational cost and production of unrealistic poses. In this paper, a novel heuristic method, called Forward And Backward Reaching Inverse Kinematics (FABRIK), is described and compared with some of the most popular existing methods regarding reliability, computational cost and conversion criteria. FABRIK avoids the use of rotational angles or matrices, and instead finds each joint position via locating a point on a line. Thus, it converges in few iterations, has low computational cost and produces visually realistic poses. Constraints can easily be incorporated within FABRIK and multiple chains with multiple end effectors are also supported.

A new method for computing numerical solutions to the inverse
kinematics problem of robotic manipulators is developed. The method is
based on a combination of two nonlinear programming techniques and the
forward recursion formulas, with the joint limitations of the robot
being handled implicitly as simple boundary constraints. This method is
numerically stable since it converges to the correct answer with
virtually any initial approximation, and it is not sensitive to the
singular configuration of the manipulator. In addition, this method is
computationally efficient and can be applied to serial manipulators
having any number of degrees of freedom

This paper addresses the problem of estimating the configuration of robots with no proprioceptive sensors and with kinematic constraints while performing tasks. Our work is motivated by the use of unsensored (industrial) manipulators, currently tele-operated in rudimentary ways, in hazardous environments such as nuclear decommissioning. For such robots, basic proprioceptive sensors are often unavailable. Even if radiation-hardened sensors could be retrofitted, such manipu-lators are typically deployed on a mobile base, while equipped with powerful end-effector tools for forceful contact tasks, which significantly perturb the robot base with respect to the scene. This work contributes a step towards enabling advanced control and increased autonomy in nuclear applications, but could also be applied to mechanically compliant, under-actuated arms and hands, and soft manipulators. Our proposed framework: estimates the robot configuration by casting it as an optimisation problem using visually tracked information; detects contacts during task execution; triggers an exploration task for detected kinematic constraints, which are then modelled by comparing observed versus commanded velocity vectors. Unlike previous literature, no additional sensors are required. We demonstrate our method on a Kuka iiwa 14 R820, reliably estimating and controlling robot motions and checking our estimates against ground truth values, and accurately reconstructing kinematic constraints.

We present early pilot-studies of a new international project, developing advanced robotics to handle nuclear waste. Despite enormous remote handling requirements, there has been remarkably little use of robots by the nuclear industry. The few robots deployed have been directly teleoperated in rudimentary ways, with no advanced control methods or autonomy. Most remote handling is still done by an aging workforce of highly skilled experts, using 1960s style mechanical Master-Slave devices. In contrast, this paper explores how novice human operators can rapidly learn to control modern robots to perform basic manipulation tasks; also how autonomous robotics techniques can be used for operator assistance, to increase through-put rates, decrease errors, and enhance safety. We compare humans directly teleoperating a robot arm, against human-supervised semi-autonomous control exploiting computer vision, visual servoing and autonomous grasping algorithms. We show how novice operators rapidly improve their performance with training; suggest how training needs might scale with task complexity ; and demonstrate how advanced autonomous robotics techniques can help human operators improve their overall task performance. An additional contribution of this paper is to show how rigorous experimental and analytical methods from human factors research, can be applied to perform principled scientific evaluations of human test-subjects controlling robots to perform practical manipulative tasks.

The solution of the inverse kinematics problem is fundamental in robot control. Many traditional inverse kinematics problem solutions, such as the geometric, iterative, and algebraic approaches, are inadequate for redundant robots. Recently, much attention has been focused on a neural-network-based inverse kinematics problem solution in robotics. However, the precision of the result obtained from a neural network requires improvement for certain sensitive tasks. In this paper, neural network and genetic algorithms are used together to solve the inverse kinematics problem of a six-joint Stanford robotic manipulator to minimize the error at the end effector. The proposed hybrid approach combines the characteristics of neural networks and evolutionary techniques to obtain more precise solutions. Three Elman neural networks were trained using separate training sets because one of the sets yields better results than the other two. The floating-point portions of each network were placed in the initial population of the genetic algorithm with the floating-point portions from randomly generated solutions. The end-effector position error was defined as the fitness function, and the genetic algorithm was implemented. Using this approach, the floating-point portion of the neural-network result was improved by up to ten significant digits using a genetic algorithm, and the error was reduced to micrometer levels. These results were compared with those from studies in the literature and found to be significantly better.

This paper describes a procedure of avoiding singularity for a redundantly-driven, dual-arm, master-slave robotic system. In cooperative operation, the procedure starts with a periodic check if one of the dual arms is close to a singular configuration by examining the manipulability of the arm. If it is close to a singularity, then the system stops its cooperative operation and control scheme switches to redundancy control. Then the operator manipulates the joint until the arm moves away from the singular configuration by inputing angular velocity to a redundant axis. The fact that the operator can select a new arm configuration to avoid singular configuration in teleoperation is the contribution of this research. After moving away from the singular configuration, the system can resume cooperative operation. Because this manual operation applies to a redundant axis only (in the null space of the Jacobian matrix), it does not affect the current end-effector pose and force status. Experimental examples are provided to demonstrate the proposed method.

An adoptive learning strategy using an artificial neural network ANN has been proposed here to control the motion of a 6 D.O.F manipulator robot and to overcome the inverse kinematics problem, which are mainly singularities and uncertainties in arm configurations. In this approach a network have been trained to learn a desired set of joint angles positions from a given set of end effector positions, experimental results has shown an excellent mapping over the working area of the robot, to validate the ability of the designed network to make prediction and well generalization for any set of data, a new training using different data set has been performed using the same network, experimental results has shown a good generalization for the new data sets.The proposed control technique does not require any prior knowledge of the kinematics model of the system being controlled, the basic idea of this concept is the use of the ANN to learn the characteristics of the robot system rather than to specify explicit robot system model. Any modification in the physical set-up of the robot such as the addition of a new tool would only require training for a new path without the need for any major system software modification, which is a significant advantage of using neural network technology.