This work designs a mechanical tool for robots with 2-finger parallel grippers, which extends the functionality of the robotic gripper without additional requirements on tool exchangers or other actuators. The fundamental kinematic structure of the mechanical tool is two symmetric parallelograms which transmit the motion of the robotic gripper to the tooltips. Four torsion springs are attached to the four inner joints of the two parallelograms to reopen the tool as the robotic gripper releases. The forces and transmission are analyzed in detail to make sure the tool reacts well with respect to the gripping forces and the spring stiffness. Based on the kinematic structure, various tooltips were designed for the mechanical tool to perform different tasks. The designed tool could be treated as a normal object and be picked up and used by automatically planned grasps. A robot may locate the tool through the AR markers attached to the tool body, grasp the tool by selecting an automatically planned grasp, and move the tool from any arbitrary pose to a specific pose to perform various tasks. The robot may also determine the optimal grasps and usage according to the requirements of given tasks.

This paper proposes to use a high-compliance soft wrist to improve the performance of robotic peg-in-hole in uncertain environments. In contrast to past research in this field, in which force control with relatively low compliance has been used, we propose a method that searching and aligning motions can be easily realized by taking advantage of high compliance wrist under gravity. Our proposed PiH strategy is completely passive: After the peg is trapped into the hole during the hole-searching process using the spherical helix trajectory, it is guaranteed that the peg can automatically be inserted into the hole due to the effect of gravity and wrist compliance if the configuration of the peg is included in the no-escapable area. The no-escapable area can be obtained based on the potential analysis considering the contact state combined with the wrist compliance space. The effectiveness of the proposed method is experimentally verified by using the peg with various shapes and sizes.

This article develops a mechanical screwing tool and its manipulation policies for two-finger parallel robotic grippers. The tool is based on a combined scissor-like element (SLE) and double-ratchet mechanism that converts the gripping motion of two-finger parallel grippers into a continuous rotation to realize tasks like fastening screws. The tool is entirely mechanical. There is no need for external cable connections. The manuscript includes two parts. For one thing, it shows the details of the tool design, optimizes the tool’s dimensions and effective stroke lengths, and studies the contacts and forces to achieve stable grasping and screwing. For another, it presents the related manipulation and control policies, including recognizing the tool, changing tool poses, and completing screw fastening tasks. The designed tool, together with the related manipulation and control policies, are analyzed and verified in several real-world applications. The results show that the tool has satisfying mechanical properties. Robots with parallel grippers can robustly and flexibly use the tool to fasten screws. The tool can also be used collaboratively with other tools to finish difficult tasks. In the future, similar tools are expected to replace special-purpose end-effectors or tool changers for more flexible robot integration.

The nonlinearity and uncertainty of dynamics pose significant challenges to ensuring the tracking performance of joint trajectories, especially time-varying effects on the load and temperature. In this paper, we present an adaptive neural computed torque control scheme to improve the tracking accuracy of the robot joint towards various tasks, which is a novel semiparametric model including a parametric friction model and a nonparametric compensator trained with multiple radial basis function neural networks $(\rm{MRBFNNs})$ . Specifically, the asymmetric model considers velocity-, load-, and temperature-dependent friction phenomena. The computed torque controller integrates the sliding mode method and the proposed friction model to reduce the boundary layer of fluctuated disturbances and achieve globally asymptotic convergence. MRBFNNs are trained separately to further compensate for the unmodeled nonlinearity and parameter uncertainty in real time during the trajectory tracking process. The comparative experiments were carried out on a robot joint, validating that our asymmetric model significantly improves correspondence to reality in terms of friction; the proposed control strategy exhibits the superior tracking performance of joints with variable payloads.

This paper presented a regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF’s geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method’s ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method’s robustness and efficacy. Note to Practitioners —In production lines, robots interact with peripheral devices to improve efficiency and reduce uncertainty. In this work, we focus on a particular peripheral device – a Triangular Corner Fixture (TCF) made by three inclined and mutually perpendicular plates. We study using the TCF to improve manipulation precision. The inclined plates of the TCF form a gravity bucket that holds dropped objects in stable states under gravity. In a real scenario, a robot picks up an object and releases it above the TCF. The released object will reach a stable state on the TCF. Then, the robot regrasps and moves the stabilized object to the target pose with reduced uncertainty. Using the method proposed in this paper, a robot can automatically finish the above procedure by finding all the object’s stable states in the TCF, planning grasp configurations, invalidating infeasible states and grasps, building regrasp graphs and searching the graph to find a regrasp motion sequence that moves the object to a goal pose with high precision for assembly. In industrial applications, the proposed method has the potential to improve the flexibility of robotic systems for high-precision tasks. In the research fields, it may promote the research on sensorless manipulation and extrinsic manipulation, and push forward the studies in robotic regrasp.

Picking a thin object placed on a flat surface is challenging as contact regions are limited. Scooping grasp is a commonly used method for handling thin objects. However, the sharp fingertips are required to achieve the successful scooping motion, which are dangerous. This paper proposes a novel grasping strategy, prying grasp, to pick thin objects using the general fingertips. In detail, the fingertip first rotates to reorient the object. While reaching a critical angle, the gripper switches to a pinch gripping, then contacts the larger sides to achieve a more stable grasp. We theoretically analyze the conditions of the successful prying, and propose a method of failure detection and recovery to improve the robustness. In addition, we experimentally validate the method and compare it with the conventional scooping grasp.