Traditional motion capture systems are the current standard in the assessment of knee joint kinematics. These systems are, however, very costly, complex to handle, and, in some conditions, fail to estimate the varus/valgus and internal/external rotation accurately due to the camera setup. This paper presents a novel and comprehensive method to infer the full relative motion of the knee joint, including the flexion/extension, varus/valgus, and internal/external rotation, using only low cost inertial measurement units (IMU) connected to the upper and lower leg. Furthermore, sensors can be placed arbitrarily and only require a short calibration, making it an easy-to-use and portable clinical analysis tool. The presented method yields both adequate results and displays the uncertainty band on those results to the user. The proposed method is based on an fixed interval smoother relying on a simple dynamic model of the legs and judicially chosen constraints to estimate the rigid body motion of the leg segments in a world reference frame. In this pilot study, benchmarking of the method on a calibrated robotic manipulator, serving as leg analogue, and comparison with camera-based techniques confirm the method’s accurateness as an easy-to-implement, low-cost clinical tool.

Scheduling under non-deterministic uncertainty is a highly complicated problem. It is commonly known and observed that these type of projects can be late and over budget. It has been pointed out that the main reason is that the uncertainty is not, realistically, taken into account in any form of scheduling methods. We propose a modification of the common method for automated optimal scheduling under non-deterministic uncertainty by use of a realistic non-deterministic uncertainty model and by taking this explicitly into account in the optimization. Furthermore, as the non-deterministic nature of the uncertainty results in the need for frequent rescheduling in the future, it is proposed to take this into account explicitly as well. Incorporating more realistic uncertainty models into the decision making process enables (i) a more realistic projection of the project objectives, and (ii) the possibility to make decisions while realistically balancing risks and rewards. Numerical simulations compare the classic method based on stochastic uncertainty, the proposed method with interval uncertainty, and the proposed method with p-box uncertainty, for an industrial reference case and a dataset obtained from a manufacturing plant in Flanders. The results illustrate the improved performance and relevance of the proposed method. This paper uses state of art branch and bound optimization algorithms and adapts those where necessary to incorporate non-deterministic descriptions.

Increasingly more people cycle with electrically-powered pedal assistance. The reduced pedalling effort attracts physically challenged people and seniors, who have a higher risk of falling. Since electric bicycles are heavier and the centre of masses are located higher, accidents happen easily. This study analyses the influence of the addition of a battery and motor unit on the stability behaviour of common bicycles for women. Based on market research, seven typical bicycle configurations are determined. Geometrics, mass values, and cycling postures are measured, and the theoretical stability behaviour is determined analytically based on the stability model of Whipple and Carvallo. The research shows that bicycles without pedal assistance have a smaller self-stable and semi-stable range than most electric bicycles. The electric bicycle with a motor implemented in the front wheel perform best, as the extra weight of the motor enhances the gyroscopic self-stabilization of the front wheel. Furthermore, a battery in the lower mid-tube is preferred over one in the luggage rack as it lowers the center of mass of the rear frame assembly. Knowledge about the optimal configuration to maximize the stability will enhance the cycling comfort and minimize the chance of accidents.

The goal of this research is to explore the potential of adding counterweights to the robot structure in order to decrease the optimal motion time for path following problems. Through the addition of counterweights to the robot links, the torques required to move the robot links could be compensated, and hence the actuator can use the excess of torque to move the link faster. The aim is hence to find the motion timing along a predetermined geometric path and simultaneously synthesise counterweights for the robot links. In general this is a strongly non-convex problem. However, by projecting the motion along the predetermined geometric path and transforming the dynamic parameters, a more efficient bi-linear optimization problem is obtained. This is illustrated with two numerical examples for basic robot systems, illustrating the proposed approach and relevance towards practical applications.

This paper addresses the challenge of control for systems with varying topologies. These changes in topology, which occur during system operation, have a significant impact on the overall system dynamics; hence the control should be adaptive with respect to these topology changes. The paper proposes an approach consisting of efficiently modeling the system and the development of a single model-based controller. For validation of the proposed approach, the paper focuses on a complex air net (pneumatic system). The simulation result and preliminary experimental results show a significant improvement in performance with a limited effort of modeling and controller development, due to the proposed approach, over classic approaches based on limited modeling.