Motion planning of collaborative robots is often required to simultaneously satisfy the contradictory objectives of reliably avoiding human workers and safely approaching them. In this article, we propose a new strategy that adaptively selects one of two objective functions based on the current operational region of the robot; the objective function for avoiding the worker with a distance larger than the safe distance, and the objective function for approaching the worker with a speed limitation for human safety. This strategy improves the worker safety while limiting the negative impact of the speed limit on time efficiency. The chattering related to the switching between the two objectives is solved using a continuous approximation of the switching based on uncertainties of the predicted worker's motion. We implement the proposed motion planning with the objective-switching strategy into a collaborative assembly system to deal with the worker moving with high irregularities. We experimentally evaluate the effectiveness of the proposed motion planning from the safety and efficiency points of view.

Collaborative robots are expected to work in cooperation with humans to improve productivity and maintain the quality of products. In the previous study, we have proposed an incremental learning system for adaptively scheduling a motion of the collaborative robot based on a worker's behavior. Although this system could model the worker's motion pattern precisely and robustly without collecting the worker's data in advance, it required two different models for modeling the worker's spatial and temporal features respectively and was not well considered for generalization. In this paper, we extend the previous incremental learning system by integrating the spatial and temporal models using a mixture model. In addition, we install a new incremental learning algorithm which improves a generalization capability of the mixture model and avoids overfitting in the situation where the prior information is limited. Implementing the proposed algorithm, we evaluate the effectiveness of the proposed system by experiments for several workers and for several assembly processes.

A novel compact parallel gripper with wide stroke is proposed in this paper. Conventional parallel grippers with linear guide mechanisms have been widely utilized as end effectors of robots, especially in industrial fields, because of its simple mechanism and low cost nature. However, the width of the gripper is larger than the stroke due to the mechanical structure of its linear guide mechanism. When objects with various dimensions need to be grasped by a gripper, the width of the gripper is determined by the largest distance between fingers necessary for grasping all of the objects. This makes the collision avoidance of robot difficult even when handling a small object compared to the width of the gripper. The novel compact gripper proposed in this article has three features: long finger stroke with compact dimensions, fingers' parallel motion along a single linear trajectory, and driven by a single actuator. The finger backlash and the gripping force of the proposed mechanism are analyzed, and the parallel gripper with the mechanism is designed based on the analyses. A prototype gripper with minimum width 64 mm, whose maximum fingers stroke is 121 mm, is developed, and experimental results illustrate the performance of the developed gripper.

Industrial robots are expected to share the same workspace with human workers and work in cooperation with humans to improve the productivity and maintain the quality of products. In this situation, the worker's safety and work-time efficiency must be enhanced simultaneously. In this paper, we extend a task scheduling system proposed in the previous work by installing an online trajectory generation system. On the basis of the probabilistic prediction of the worker's motion and the receding horizon scheme for the trajectory planning, the proposed motion planning system calculates an optimal trajectory that realizes collision avoidance and the reduction of waste time simultaneously. Moreover, the proposed system plans the robot's trajectory adaptively based on updated predictions and its uncertainty to deal not only with the regular behavior of workers but also with their irregular behavior. We apply the proposed system to an assembly process where a two-link planar manipulator supports a worker by delivering parts and tools. After implementing the proposed system, we experimentally evaluate the effectiveness of the adaptive motion planning system.

In this paper, we introduce a concept of passive knee exoskeleton for cycling assistance. Considering a knee moment and a knee angle varying with a pedal crank angle, the knee extension moment can be supported by a torsion spring storing energy from knee flexion in order to release it as the knee is extended. The reduction of knee extension effort is corresponding to the torsion spring stiffness and activation range. Exoskeleton prototypes were developed for the concept validation. A crossing four-bar mechanism was chosen for the knee joint to provide kinematic compatibility covering extreme knee flexion. Constant power cycling experiment was performed on a cycling trainer by a healthy subject wearing the exoskeletons on both legs. With the torsion spring support, the surface electromyography recorded from some major leg muscles shows the decrease of knee extensor muscle activity as the leg is moving around the pedal crank top dead center. Verifying the reduction of leg muscle fatigue over repetitive contractions in multiple subjects is our future plan of study.

This study analyzed the precision grip force for the underactuated gripper for power and precision grasp (uGRIPP). uGRIPP is a robot hand developed for dual arm manipulation. It can grasp objects through power and precision grips. For a more detailed analysis of the performance on the grip force and control of the grip force, a relationship was derived between the grip and actuation forces by using an analytical method based on the principle of virtual power. Finally, theoretical values of the grip force were calculated according to the analysis, and the actual grip force was measured experimentally. The experimental results show the validity of the analysis.

In this study, we propose a control strategy for a power-assisted cart based on its motion state. The power-assisted cart we developed has one motor, a differential gear, and brakes. This cart uses the motor and the differential gear for moving forward, and applying brakes to either wheel allows the cart to turn both left and right. Therefore, the power-assisted cart can support the user when going straight and turning despite having only one motor. In the past we developed a control method that allows to control the cart's speed around the operation point in order to keep its magnitude constant when the cart starts turning. This was necessary, as the differential gear causes a speed change during turning, because of its characteristics. However, the desired behavior when transitioning from straight motion to turning motion is different to the desired behavior when going from turning motion to straight motion. Therefore, in this paper we propose a control method to adjust the speed in the direction of motion based on the state of the cart. We validated the effectiveness of the proposed method through experiments and discussed the results.

It is well known that, by properly selecting the measurement configurations in robot calibrations, the observability index of unknown parameters can be maximized, leading to high calibration accuracy. For this purpose, many configuration-search methods were proposed. However, the established methods were mainly based on derivative-free or metaheuristic techniques, whose computational costs were high. Moreover, the robustness of observability index and convergences of configuration searches were not investigated. In this paper, by extending a recent result in matrix perturbation theory to robot kinematics, we establish the closed-form mapping from configuration perturbations to singular-value variations. Based on this mapping, an efficient configuration-search method is proposed, the robustness of the observability index under bounded configuration perturbations is analyzed, and the convergence of configuration searches is studied. The proposed methods were validated by simulations on serial and parallel robots. With roughly estimated initial parameters, self-calibration experiments on a redundant cable-driven parallel robot were performed. The effectiveness of the proposed methods is demonstrated by the experiment results.

In this paper, we propose a vision based compliant motion control method for part assembly work. Some industrial parts are deformed during assembly of parts. If work progress continues, deformation of the part increases, humans check the deformation and adjust force corresponding to the progress state. The proposed method enables a robot manipulator to adjust force applied for assembly work like a human. In our proposed method, force applied for the work is generated by visual information from a camera reading the deformation of the parts. Processing the visual information quantifies the deformation and the data show the work progress. NCC is generally used for template matching, but in this paper we use it for quantifying deformation. Connectors are assembled by a robot manipulator using proposed method and impedance control in experiments. Experimental results are presented to verify the effectiveness of the proposed method.

We have developed a novel assembly task partner robot to support workers in their task. This system, PaDY (in-time Parts/tools Delivery to You robot), delivers parts and tools to a worker by recognizing the worker's behavior in the car production line; thus, improving the efficiency of the work by reducing the worker's physical workload for picking parts and tools. For this purpose, it is necessary to plan the trajectory of the robot before the worker moves to the next location for another assembling task. First a prediction method for the worker's trajectory using a Markov model for a discretized work space into cells is proposed, then motion planning method is proposed using the predicted worker's trajectory and a mixture Gaussian distribution for each area corresponding to each procedure of the work process in the automobile coordinate system. Experimental results illustrate the validity of the proposed motion planning method.

In this paper, we propose a novel human-friendly/cooperative working support robot named PaDY (intime Parts/tools Delivery to You robot). This system reduces the worker's load and improves work efficiency by recognizing the worker's behavior at a production site, and supporting the worker. We propose a method estimating the pace of work of a worker so as to adjust the motion of the robot to the pace of work, and confirm its effectiveness by performing experiments. We describe the concept of PaDY, the measurement of a worker's motion for motion planning of the robot arm, and the method of estimating the pace of work based on statistical data.