Soft grippers are receiving growing attention due to their compliance-based interactive safety and dexterity. Hybrid gripper with soft actuators enhanced by rigid constraints is receiving growing attention in soft gripper design. With rigid structural components actuated by soft actuators, they could achieve excellent grasping adaptability and payload while also being easy to model and control with conventional kinematics. However, existing works were mostly focused on achieving superior payload and perception with simple planar workspaces, resulting in far less dexterity compared with conventional grippers. In this work, we took inspiration from the human metacarpophalangeal (MCP) joint and proposed a new hybrid gripper design with eight independent muscles. It was shown that adding the MCP complexity was critical in enabling a range of novel features in the hybrid gripper, including in-hand manipulation, lateral passive compliance, as well as new control modes. A prototype gripper was fabricated and tested on our proprietary dual-arm robot platform with vision-guided grasping. With very lightweight pneumatic bellows soft actuators, the gripper could grasp objects over 20 times its weight with lateral compliance. Using the dual-arm platform, highly anthropomorphic dexterous manipulations were demonstrated using two hybrid grippers, from tug of war on a rigid rod to passing a soft towel between two grippers using in-hand manipulation. Matching the novel features and performance specifications of the proposed hybrid gripper, the underlying modeling, actuation, control, and experimental validation details were also presented, offering a promising approach to achieving enhanced dexterity, strength, and compliance in robotic grippers.

In this article, we present an improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators. Specifically, we not only focused on the position for the manipulator end-effectors but also considered their posture in the course of trajectory planning and obstacle avoidance. We introduced boundaries for Cartesian space components to optimize the attractive field function. Moreover, the manipulator achieved a reasonable speed to move to the target pose, regardless of the difference between the initial pose and target pose. We proved the stability using Lyapunov stability theory by introducing velocity feedforward, when the manipulator moved along a continuous trajectory. Considering the shape of the manipulator joints and obstacles, we set up the collision detection model by projecting the obstacles to link coordinates. In this case, establishing the repulsive field between the nearest points on every joint and obstacles with the closest distance was sufficient for achieving obstacle avoidance for redundant manipulators. The simulation results based on a nine-degree-of-freedom hyper-redundant manipulator, which was designed and made in our laboratory, fully substantiated the efficacy and superiority of the proposed method.

This paper proposes a novel trajectory planning algorithm to design an end-effector motion profile along a specified path. An optimization model based on the whale optimization algorithm (WOA) is established for time-optimal asymmetrical S-curve velocity scheduling. Trajectories designed by end-effector limits may violate kinematic constraints due to the non-linear relationship between the operation and joint space of redundant manipulators. A constraints conversion approach is proposed to update end-effector limits. The path can be divided into segments at the minimum of the updated limitations. On each path segment, the jerk-limited S-shaped velocity profile is generated within the updated limitations. The proposed method aims to generate end-effector trajectory by kinematic constraints which are imposed on joints, resulting in efficient robot motion performance. The WOA-based asymmetrical S-curve velocity scheduling algorithm can be automatically adjusted for different path lengths and start/end velocities, allowing flexibility in finding the time-optimal solution under complex constraints. Simulations and experiments on a redundant manipulator prove the effect and superiority of the proposed method.

The Ant Colony Optimization (ACO) is easy to fall into the local optimum and its convergence speed is slow in solving the Travelling Salesman Problem (TSP). Therefore, a Slime Mold-Ant Colony Fusion Algorithm (SMACFA) is proposed in this paper. Firstly, an optimized path is obtained by Slime Mold Algorithm (SMA) for TSP; Then, the high-quality pipelines are selected from the path which is obtained by SMA, and the two ends of the pipelines are as fixed-point pairs; Finally, the fixed-point pairs are directly applied to the ACO by the principle of fixed selection. Hence, the SMACFA with fixed selection of high-quality pipelines is obtained. Through the test of the chn31 in Traveling Salesman Problem Library (TSPLIB), the result of path length was 15381 by SMACFA, and it was improved by 1.42% than ACO. The convergence speed and algorithm time complexity were reduced by 73.55 and 80.25% respectively. What’s more, under the ten data sets of TSPLIB, SMACFA outperformed other algorithms in terms of the path length, convergence speed and algorithm time complexity by comparison experiments. It is fully verified that the performances of SMACFA is superior to others in solving TSP.

Accurate identification of manipulator dynamics parameters is crucial for achieving precise control and optimal performance in various robotic applications. Existing methods primarily utilize least squares or weighted least squares for dynamic parameters identification which cannot effectively integrate nonlinear friction model and satisfy physical feasibility constraints. Consequently, this study proposed an iteratively semi-linearized approach for identifying dynamic parameters of manipulator based on nonlinear friction models. First, the nonlinear friction model parameters of joint are pre-identified to prepare for feasibility base parameter estimation. Secondly, to establish a semi-linearized dynamic model, the robot and friction torque are separated, allowing for the linearization of the robot dynamic model while preserving the nonlinearity of the friction model. Utilizing this semi-linearized model, an iterative identification network is established to accurately identify dynamic parameters. This method effectively satisfies physical feasibility constraints and integrates various nonlinear friction models, resulting in improved identification accuracy. Furthermore, it seamlessly incorporates geometric metrics to better align the identified dynamic parameters with the actual physical system. Finally, the utilization of neural network-based nonlinear compensation technology further reduces identification errors. A series of experiments were conducted using a self-developed nine-degrees-of-freedom redundant manipulator. The experimental results demonstrated the efficiency and superiority of the proposed algorithm through performance comparison with various methods and friction models. A comprehensive analysis and discussion have revealed the underlying principles of this approach.