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The paper seeks to present a new generation of torque‐controlled light‐weight robots (LWR) developed at the Institute of Robotics and Mechatronics of the German Aerospace Center.

An integrated mechatronic design approach for LWR is presented. Owing to the partially unknown properties of the environment, robustness of planning and control with respect to environmental variations is crucial. Robustness is achieved in this context through sensor redundancy and passivity‐based control. In the DLR root concept, joint torque sensing plays a central role.

In order to act in unstructured environments and interact with humans, the robots have design features and control/software functionalities which distinguish them from classical robots, such as: load‐to‐weight ratio of 1:1, torque sensing in the joints, active vibration damping, sensitive collision detection, compliant control on joint and Cartesian level.

The DLR robots are excellent research platforms for experimentation of advanced robotics algorithms. Space and medical robotics are further areas for which these robots were designed and hopefully will be applied within the next years. Potential industrial application fields are the fast automatic assembly as well as manufacturing activities done in cooperation with humans (industrial robot assistant). The described functionalities are of course highly relevant also for the potentially huge market of service robotics. The LWR technology was transferred to KUKA Roboter GmbH, which will bring the first arms on the market in the near future.

This paper introduces a new type of LWR with torque sensing in each joint and describes a consistent approach for using these sensors for manipulation in human environments. To the best of one's knowledge, the first systematic experimental evaluation of possible injuries during robot‐human crashes using standardized testing facilities is presented.

The purpose of this paper is to present and evaluate methods of control and gait generation for the DLR Crawler – a six‐legged walking robot prototype based on the fingers of the DLR Hand II.

Following the institutes philosophy, the DLR Crawler is a highly integrated mechatronic device. As in all DLR robots, joint torque sensing plays an important role to allow actively compliant interaction with the environment. To control the Crawler a joint compliance controller is implemented and two different methods of gait generation are in use. The first method, intended for moderately uneven terrain, employs scalable patterns of fixed coordination combined with a leg extension reflex. For the second method, used in rougher terrain, a set of rules found by biologists in stick insect studies is applied. Based on these rules gaits emerge according to a velocity command. These gaits are combined with several reflexes to a reactive walking algorithm.

The compliance controller together with the reactive gaits allows the robot to autonomously master uneven terrain and obstacles with height differences within the nominal walking height. Further, the controller reduces internal forces compared to pure joint position control. The sensitive joint torque sensors allow fast collision detection and reactions thereafter.

This paper introduces a six‐legged walking robot test bed with comprehensive force‐torque sensing capability. Joint compliance controllers are implemented and successfully combined with reactive gait algorithms. For the second gait algorithm inspired by Cruse's rules, which were identified for forward walking stick insects, an implementation has been found for the DLR Crawler that gives the robot full omnidirectional mobility.

Interaction plays a significant role in robotics and it is considered in all levels of hardware and software control design. Several models have been introduced and developed for controlling robotic interaction. This study aims to address and analyze the state-of-the-art on robotic interaction control by which it is revealed that both practical and theoretical issues have to be faced when designing a controller.

In this review, a critical analysis of the control algorithms developed for robotic interaction tasks is presented. A hierarchical classification of distributed control levels from general aspects to specific control algorithms is also illustrated. Hence, two main control paradigms are discussed together with control approaches and architectures. The challenges of each control approach are discussed and the relevant solutions are presented.

This review presents an evolvement trend of interaction control theories and technologies over time. In addition, it highlights the pros and cons of each control approaches with addressing how the flaws of one control approach were compensated by emerging another control methods.

This review provides the robotic controller designers to select the right architecture and accordingly design the appropriate control algorithm for any given interactive task and with respect to the technology implemented in robotic manipulator.

Admittance control is a typical complaint control methodology. Traditionally, admittance control systems are based on a dynamical relationship described by Voigt model. By contrast, after changing connection of spring and damper, Maxwell model produces different dynamics and has shown better impact absorption performance. This paper aims to design a novel compliant control method based on Maxwell model and implement it in a robot catching scenario.

To achieve this goal, this paper proposed a Maxwell model based admittance control scheme. Considering several motion stages involved in one catching attempt, the following approaches are adopted. First, Kalman filter is used to process the position data stream acquired from motion capture system and predict the subsequent object flying trajectory. Then, a linear segments with parabolic blends reaching motion is generated to achieve time-optimal movement under kinematic and joint inherent constraints. After robot reached the desired catching point, the proposed Maxwell model based admittance controller performs such as a cushion to moderate the impact between robot end-effector and flying object.

This paper has experimentally demonstrated the feasibility and effectiveness of the proposed method. Compared with typical Voigt model based compliant catching, less object bounding away from end-effector happens and the success rate of catching has been improved.

The authors proposed a novel Maxwell model based admittance control method and demonstrated its effectiveness in a robot catching scenario. The author’s approach may inspire other related researchers and has great potential of practical usage in a widespread of robot applications.

The purpose of this paper is to estimate the deformation of soft manipulators caused by obstacles accurately and the contact force and workspace can be also predicted.

The continuum deformation of the backbone of the soft manipulator under contact is regarded as two constant curvature arcs and the curvatures are different according to the fluid pressure and obstacle location based on piecewise constant curvature framework. Then, this study introduces introduce the moment balance and energy conservation equation to describe the static relationship between driving moment, elastic moment and contact moment. Finally, simulation and experiments are carried out to verify the accuracy of the proposed model.

For rigid manipulators, environmental contact except for the manipulated object was usually considered as a “collision” which should be avoided. For soft manipulators, an environment is an important tool for achieving manipulation goals and it might even be considered to be a part of the soft manipulator’s end-effector in some specified situations.

There are also some limitations to the presented study. Although this paper has made progress in the static modeling under environmental contact, some practical factors still limit the further application of the model, such as the detection accuracy of the environment location and the deformation of the contact surface.

Based on the proposed kinematic model, the bending deformation with environmental contact is discussed in simulations and has been experimentally verified. The comparison results show the correctness and accuracy of the presented SCC model, which can be applied to predict the slender deformation under environmental contact without knowing the contact force.

The purpose of this paper is to introduce a method for stiffness modeling, identification and updating of collaborative robots (cobots). This method operates in real-time and with high precision and can eliminate the modeling error between the actual stiffness model and the theoretical stiffness model.

To simultaneously ensure the computational efficiency and modeling accuracy of the stiffness model, this method introduces the finite element substructure method (FESM) into the virtual joint method (VJM). The stiffness model of the cobots is built by integrating several 6-degree of freedom virtual joints that represent the elastic deformation of the cobot modules, and the stiffness matrices of these modules can be identified and obtained by the FESM. A model-updating method is proposed to identify stiffness influence coefficients, which can eliminate the modeling error between the actual prototype model and the theoretical finite element model.

The average relative error and the cycle time of the proposed method are approximately 6.14% and 1.31 ms, respectively. Compared with other stiffness modeling methods, this method not only has high modeling accuracy in high dexterity poses but also in low dexterity poses.

A hybrid stiffness modeling method is introduced to integrate the modeling accuracy of the FESM into the VJM. Stiffness influence coefficients are proposed to eliminate the modeling error between the theoretical and actual stiffness models.

Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced.

The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path.

The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths.

Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.

This paper aims to propose the method of automatic robotic assembly of two or more parts placed without fixing instrumentation and positioning on the pallet.

Assembly tasks performed by industrial robots are usually based on a constant program, extensive tooling, fixing objects in a given place and a relatively limited sensory system. In this study, a different approach is presented. The industrial robot program is adjusted to the location of parts for assembly in the work space. This leads to a transition from a clearly defined assembly sequence realized by the industrial robot to the one in which the order of execution of the assembly operations can be determined by the mutual position of parts to be assembled.

The method presented in this study combines many already known algorithms. The contribution of the authors is to test and select the appropriate combination of methods capable of supporting robotic assembly process based on data from optical 3D scanners. The sequence of operations from scanning to place the parts in the installation position by an industrial robot is developed. A set of parameters for selected methods is presented. The result is a universal procedure that determines the position of the preset models in partial measurements performed at a fixed relative position of the sensor, the measurement object.

The developed procedure for determining the position of the parts is essential to develop a flexible robotic assembly system. It will be able to perform the task of assembly on the basis of appropriate search algorithms taking into account the selected and implemented sequence of assembly position and orientation of parts, particularly the base unit freely placed on an assembly pallete. It is also the basis of a system for testing different algorithms to optimize the flexible robotic assembly.

The purpose of this paper is to present the design and experiment of a universal space-saving end-effector for multi-task operations.

The universal end-effector is equipped with capture and actuation transmission capabilities with two corresponding subsystems, which are highly integrated systems of mechanics, electronics and sensors. A trefoil-shaped capture system is developed for closed envelop. The worm gear pair is adopted for self-locking and space-saving, and it is used in a unique manner for three grapple chains’ synchronous motion. The combination of optimal straight path linkage and pantograph mechanism is proposed in the transmission system. The electrical structure and the multi-sensory system provide the foundation for control strategy.

Simulations and experiments demonstrated characteristics of the universal end-effector. The compliance of the manipulator guaranteed the achievement of “soft capture” by the end-effector. Due to the self-locking property, the end-effector and the grapple interface could keep rigid connection when powered off.

The design process takes practical requirements into consideration. Through experiments, it is proved that the proposed end-effector can be used for the multi-task operations with corresponding tools.

Among end-effectors with operation function, the misalignment tolerance (MT) is originally regarded as a key factor. The adoptions of the worm gear pair and the linkage make it space-saving compared to conventional designs.