An auto-tuning method of PD controllers for robotic manipulators is proposed. This method suggests a practical implementation of the particle swarm optimization technique in order to find optimal gain values achieving the best tracking of a predefined position trajectory. For this purpose, The integral of the absolute error IAE is used as a cost function for the optimization algorithm. The optimization is achieved by performing the desired movement of the robot iteratively and evaluating the cost function for every iteration. Therefor, the necessary constraints that guarantee a safe and stable movement of the robot are defined, which are: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. A constraint handling approach is suggested for the optimization algorithm in order to adapt it to the problem in hand. Finally, the efficiency of the proposed method is verified through a practical experiment on a real robot.

A typical instrument for different tasks in minimally invasive surgery is a flexible endoscope. To overcome the problem of restricted motion of today's systems, a hyper-redundant active shaft concept is developed. It features good resistance with respect to manipulation forces through its unique binary, electromagnetic actuation concept. With an active control of each element of the hyper-redundant endoscope shaft and in combination with automated feed motion, it is controlled in a “follow-the-leader” fashion. In general, this approach is characterized by very good path following capabilities. However, due to its binary actuation concept, joint angles cannot be interpolated continuously. Therefore, an adaption to binary actuation with optimized switching sequences to achieve an appropriate path following performance despite of the restricted motion is proposed.

In this paper we summarize how the LUHbots team was able to win the 2015 RoboCup@Work league. We introduce various failure handling concepts, which lead to the robustness necessary to outperform all the other teams. The proposed concepts are based on failure prevention and failure handling. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Bone-attached robots and so-called microstereo-tactic frames are attracting increasing interest in the field of robot-assisted surgery due to the promising targeting accuracy they provide. The authors propose a passive Stewart-Gough platform which is attached to a patient's head via bone anchors. It serves as an instrument guidance in straight line incisions, such as minimally invasive cochlear implantation. In this contribution a modified version of the mechanism's kinematics is proposed, which reduces the number of required bone anchors from six to three. Furthermore, a novel statistical approach to optimize the design variables of the moving platform for accuracy is presented. It is characterized by the ability to take the probability distributions of all relevant error sources as well as the given task redundancy and reconfigurability of the mechanism into account. Based on identified ranges of trajectories and possible bone anchor locations, the optimization problem is solved for a representative number of 1,000 ‘virtual patients’. The optimum mechanism design is obtained by analyzing the resulting distributions of design variables. Monte Carlo simulation is used to compare its targeting errors to those of a previous prototype. Results reveal that the targeting error is significantly reduced in comparison to an initial prototype thanks to the proposed optimization strategy.

This paper presents a new university course combining theoretical lectures with a robot competition. The main intention is to offer a practical course including hands-on experiences being close to current research topics in the field of mobile robotics. For this purpose, a commercial mobile robot is equipped with state of the art sensors, allowing autonomous execution of manipulation tasks. The course consists of three thematical sections. At the beginning of each, lectures provide the theoretical background. On this basis, the participants are addressed to develop algorithms and to solve specific tasks, delivered in homework packages, self-reliantly or in a team. Finally, the developed software components need to be merged to solve a predefined scenario, e.g. autonomous part handling. At the end of a thematic section, students demonstrate their developed solutions within a challenge and explain their approaches in a presentation. Starting with teleoperation and object recognition, the RobotChallenge ends up with navigation in unknown terrain. Besides others, the participants acquire soft-skills, such as project and team management. Being carried out for the first time in winter term 2011/2012, the RobotChallenge successfully promotes profound understanding of mobile robotics that is applied during practical experiences. It turns out, that aspiring to win competions lead to a high motivation of the students w.r.t. development of appropriate solutions.

In this paper experimental results are presented to compare the performance of kinematically redundant parallel robots with respect to their non-redundant counterparts. The main purpose is to validate existing simulated, i.e. claimed, findings demonstrating the advantages of kinematic redundancy in terms of singularity avoidance and, therefore, accuracy and precision. Exemplarily, the kinematically redundant prototype of the Institute at Mechatronic Systems is introduced. It is based on the well known planar 3RRR mechanism. In order to achieve kinematic redundancy, a prismatic actuator is added to the structure allowing one base joint to move linearly. As a result, the mechanism is able to reconfigure, i.e. optimize, its geometry according to different performance criteria and motion strategies. While performing a geometrical reconfiguration and following several desired (optimized) trajectories the pose of the end-effector is determined using an external measurement device. Hence, in addition to the encoder data of the actuators the performance can be analyzed without using any (uncertain) kinematic models. This allows for a meaningful comparative evaluation of the performance of kinematically redundant mechanisms.

In this paper five different optimization strategies for kinematically redundant mechanisms, i.e. mechanisms having additional actuator(s) in at least one kinematic chain, are presented. They are based on two main approaches, a discrete optimization and a classical continuous optimization. Exemplarily, a planar, kinematically redundant 3RRR-based mechanism is introduced. The position of its redundant actuator, i.e. the robot geometry, is optimized according to an optimization criterion that is denoted as the gain of the maximal homogenized pose error. Several analysis examples demonstrate the effectiveness of kinematic redundancy with respect to the introduced optimization procedures. It is shown that in comparison to discrete approaches, classical continuousbased optimization strategies do not necessarily lead to more appropriate results in terms of performance improvement.

In the present paper we introduce an improved method to obtain the guaranteed singularity-free workspace of planar parallel kinematic machines. A geometric condition for the existence of singularities is extended to be used in interval analysis. Therefore, we eliminate the need of calculating the inconvenient interval form of the Jacobian's determinant. Hence, an appropriate description of the singularity-free workspace is obtained and the computational effort is reduced significantly. With the interval-based approach error sources, like manufacturing tolerances, can be considered. Consequently, regions that do not satisfy the proposed condition can be guaranteed not to have any type-two singularities. Several analysis examples demonstrate the efficiency of the proposed geometrical-based solver in comparison to existing solvers, i.e. based on the Jacobian's determinant.

In the present paper kinematic redundancy is proposed in order to improve the achievable pose accuracy of a parallel robot's traveling platform. An additional prismatic actuator is applied to a 3RRR planar parallel manipulator. It allows a selective reconfiguration of the kinematic structure according to several optimization criteria. Based on an index that we denote the gain of the Jacobian matrix the position of the redundant actuator is changed in a discrete manner. The resulting optimized switching patterns lead to a great increase of the traveling platform's pose accuracy. Between two switching operations the prismatic actuator's position is supposed to be locked. Hence, sources of error, e.g. the joint clearance, can be minimized. Several analysis examples demonstrate the effectiveness of the proposed concept in combination with the developed optimization procedure.

This article provides a tracking scheme for 6 DOFpsilas parallel robots. It is based on a controller/observer combination with an additional compensation of the desired dynamics. The latter is necessary to avoid an online calculation of the direct kinematics. However, it introduces systematic errors. To take such deviations into account a passivity based formalism is used. The resulting approach is experimentally substantiated and proven to be highly powerful and robust against initial errors and model uncertainties.