It is important to maintain safety and reliability during human-robot cooperations with service robot. Collision should be monitored and detected in real time to avoid possible damages. In this paper, we propose design and optimization of a novel strain-gauge type joint torque sensor for robot collision detection. It features thin structure, quick response and high sensitivity. A holed spoke structure is proposed to transfer maximum stress position and improve stress distribution. To achieve best sensitivity and stress evenness of the sensor, a mathematical optimization with two objective functions is formulated to topologically optimize the flexure spoke structure. Optimal structure parameters are thus obtained. Sensor signal processing is proposed based on the Wheatstone full bridge. Its corresponding hardware is designed and implemented to be integrated in the sensor. Calibration and evaluation are carried out to validate the sensor performance.

A robot navigating in dense crowds should react to the motion of nearby pedestrians. However, it could lead to unsafe, inefficient, and illegible robot motions. This article presents an anticipative framework that predicts pedestrians intentions and their interactions in crowds, and the robot accordingly seeks an optimal trajectory based on the prediction. We propose: 1) a pedestrian motion model considering both pedestrian intention and interaction and 2) a multiobjective cost function considering real-time calculation, collision avoidance, quality of motion, and progress toward the goal along the trajectory. An interactive model predictive control framework is formulated to optimize the robot trajectory. The effectiveness of the proposed approach is evaluated in multiple simulation scenarios and a real experiment. It is demonstrated that the proposed approach generates safe, efficient, and legible robot behaviors in real time in dense crowds.

This work intends to deal with the optimal kinematic synthesis problem of parallel manipulators under a unified framework. Observing that regular (e.g., hyper-rectangular) workspaces are desirable for most machines, we propose the concept of effective regular workspace, which reflects simultaneously requirements on the workspace shape and quality. The effectiveness of a workspace is characterized by the dexterity of the mechanism over every point in the workspace. Other performance indices, such as manipulability and stiffness, provide alternatives of dexterity characterization of workspace effectiveness. An optimal design problem, including constraints on actuated/passive joint limits and link interference, is then formulated to find the manipulator geometry that maximizes the effective regular workspace. This problem is a constrained nonlinear optimization problem without explicitly analytical expression. Traditional gradient based approaches may have difficulty in searching the global optimum. The controlled random search technique, as reported robust and reliable, is used to obtain an numerical solution. The design procedure is demonstrated through examples of a Delta robot and a Gough-Stewart platform.

Disturbance observers have been attracting continuing research efforts and are widely used in many applications. Among them, the Kalman filter-based disturbance observer is an attractive one since it estimates both the state and the disturbance simultaneously, and is optimal for a linear system with Gaussian noises. Unfortunately, The noise in the disturbance channel typically exhibits a heavy-tailed distribution because the nominal disturbance dynamics usually do not align with the practical ones. To handle this issue, we propose a generalized multi-kernel maximum correntropy Kalman filter for disturbance estimation, which is less conservative by adopting different kernel bandwidths for different channels and exhibits excellent performance both with and without external disturbance. The convergence of the fixed point iteration and the complexity of the proposed algorithm are given. Simulations on a robotic manipulator reveal that the proposed algorithm is very efficient in disturbance estimation with moderate algorithm complexity.

A novel class of spatial four degree-of-freedom Schönflies-motion parallel manipulators with four identical subchains is presented. Their features are that each serial subchain undergoes the pure Schönflies motion without redundant joints. The parallel mechanisms possess the simplest topology and are suitable for pick-and-place operations. Kinematic analysis of the 4-PRP_aR parallel manipulator, including its inverse and forward kinematics, singularity, and workspace, is discussed in detail. The analysis shows that the moving platform and the base must be in dissimilar dimension for good manipulability performance. The optimal design of the parallel manipulator is formulated as a multiobjective optimization problem. A novel performance index characterizing the approximation of the generated workspace to the prescribed regular workspace, the regular workspace share, is proposed to serve as one of the design objectives. The other objective is the global condition index, which measures the manipulability. The multiobjective optimization problem provides multiple optimal solutions for choice. Simulation verifies that the designed parallel manipulator can approximate the prescribed regular workspace with good condition index.

The optical motion capture (MoCap) sensor provides an effective way to capture human motions and transform them into valuable data that can be applied to certain tasks, e.g. robot learning from demonstration (LfD). In spite of the wide utilization of optical MoCaps in LfD studies, there are few works that explore their potentiality in small parts robotic assembly. Robot manipulation skill learning from demonstration has gained the attention of researchers recently and robotic 3C (Computer, Communication, and Consumer Electronics) product assembly turns out to be a promising application thanks to the increasing consumption of 3C products. To further explore the potential of optical MoCaps in robotic 3C product assembly. This work proposes a performance evaluation protocol that takes the characters of both optical MoCaps and 3C product assembly operations into account. The proposed evaluation protocol includes static and trajectory evaluations. The former refers to the widely used evaluation indicators such as precision and accuracy. Meanwhile, the trajectory evaluation takes contour error as an error metric. Three popular optical MoCaps are studied in the experiment. Experiment results show that the static performance of all of the three optical MoCaps can meet the requirements of the 3C product assembly task. What's more, Prime X41 possesses the best trajectory performance. This work sheds light on the wider usage of optical MoCaps in manufacturing industries.