As an important input for the simulation and design process of powertrains, a driving cycle needs to be representative of real-world driving behavior. For the purpose of reducing the time consumption in the simulation, a novel modeling method is required to get a representative short driving cycle from the driving datasets. In this paper, a stochastic model based driving cycle synthesis is introduced. The Markov Chain process is combined with a transition probability extracted from the input driving data to determine the next possible state of the vehicle. Specifically, the velocity and slope are generated simultaneously using a three-dimensional Markov Chain model. After the generation process, the result is validated by selected criteria. Furthermore, this synthesis can generate the driving cycle with the desired length to compress the original driving cycle. The results show that the successful compression of the driving cycle can be tested for the fuel economic in the powertrain simulation. At last, the standard deviation of acceleration is found that has a positive correlation of the compression capability of the driving cycle.

Vitreoretinal eye surgery requires ultimate surgeon's precision and skills. Significant variation exists in the approaches between different surgeons. Procedures are often indicated as difficult to perform. The surgeon's personal skill level is considered critical, including hand motion stability and left/right hand dexterity. Precision of instrument manipulation is a key requirement and a limiting factor in such procedures. The collaboration between high-precision research at the Eindhoven University of Technology and a spin-off company PRECEYES has produced a working prototype for teleoperation robot-assistance during vitreoretinal surgery. This PRECEYES Surgical System enables the surgeon to achieve a higher level of precision and decrease the time span of procedures.

This paper introduces a three-dimensional volumetric representation for safe navigation. It is based on the OctoMap representation framework that probabilistically fuses sensor measurements to represent the occupancy probability of volumes. To achieve safe navigation in a domestic environment this representation is extended with a model of the occupancy probability if no sensor measurements are received, and a proactive approach to deal with unpredictably moving obstacles that can arise from behind occlusions by always expecting obstacles to appear on the robot's path. By combining the occupancy probability of volumes with the position uncertainty of the robot, a probability of collision is obtained. It is shown that by relating this probability to a safe velocity limit a robot in a real domestic environment can move close to a certain maximum velocity but decides to attain a slower safe velocity limit when it must, analogous to velocity limits and warning signs in traffic.

Bilateral control architectures include multiple control elements. In general, the relation between a single control element and the stability of the entire system is non-linear. Therefore, stability is standard evaluated a posteriori, rendering the control design process to be complex and highly iterative. A priori understanding of stability constraints would simplify the design of control elements and, as performance is fundamentally limited by stability, could provide specific guidelines whether and how performance of the bilateral teleoperation system can be optimized. This paper presents a numerical visualization method that enables stability-based control design using classical loopshaping techniques: Frequency-domain Mapping of Bilateral Stability (FMBS). Unlike current stability-based control design approaches, the FMBS method i) is not limited to a fixed control element, a fixed control architecture or system dynamics and ii) enables the implementation of all often used stability criteria. The advantages of the FMBS method are theoretically validated through the use of two test cases, extracted from literature. Using the FMBS method, it is shown that control elements can be redesigned to achieve superior performance.

The inherent transparency-stability trade-off in bilateral teleoperation poses a challenge to design controllers that find a proper balance between both requirements. Furthermore, when the environment of the teleoperation system varies within a wide range, a single controller might not be sufficient to achieve both stability and transparency. Therefore, we propose the synthesis of a switching robust controller that increases the environment stiffness range for which both stability and transparency is achieved. During the design we take into account the fact that environment estimators will have limited accuracy due to noise and uncertainty. Additionally, we use a parametric model of the human operator based on measurements of an operator's hand. We show the applicability of the approach by experiments on a 1-DOF teleoperated system interacting with a virtual spring.

Robots increasingly operate in dynamic environments and in order to operate safely, reliable world models are indispensable. A world model is the robot's view of the world and contains information about obstacle locations and velocities. A two level algorithm is proposed. It is of particular use for teams of cooperating robots and the algorithm is based on a multiple hypotheses filter. Each robot features a low level world model with a fast update rate which can be used for obstacle avoidance. The local world models are combined to one global view of the world that is shared between all robots and can be used for the implementation of team strategies. Labeling and tracking is added to the multiple hypotheses filter in order to reduce the sensitivity to track loss in case of temporary occlusions of objects or false measurements. The algorithm was extensively tested during the 2010 RoboCup Middle Size League world championships in Singapore, the results of which are presented.

Forbidden-region virtual fixtures (FRVFs) are traditionally imposed on users via proportional-derivative (PD) control. To reduce user penetration into the forbidden region, this paper proposes a new controller, hereafter called continuous impulsive force (CIF) controller. The CIF controller generates passive impulsive forces throughout users' motion into the forbidden region. These impulsive forces annihilate users' velocity into the forbidden region. Superimposed on typical PD control forces, the CIF control forces reduce users' incursion into the forbidden region, and render the feel of fully plastic collisions.

Presents closed-form kinematic and dynamic models of a robot with three rotational degrees of freedom. The derivation of the models and estimation of their parameters are explained. Relevancy of the models is investigated with a writing task. Validation results, obtained by simulation and experiment, establish correctness of the models and illuminate their practical benefits.

This paper suggests and employs a method for reducing vibrations at the tip of a robot. The vibrations, caused by structural flexibility, are detected using accelerometers. The robot control system consists of two complementary sub-systems: a nominal motion controller and a vibration compensator. The former one realizes joint motions in accordance with a prescribed tip trajectory. The latter one robustly attenuates oscillations at the tip that are due to structural flexibility. The benefits of this set-up are experimentally verified for a spatial direct-drive robot with 3 revolute degrees of freedom.