A facile fabricating method for smart soft robotic hand

A facile fabricating method was proposed for a smart soft robotic hand (SSRH) composed of shape memory alloy (SMA) wires, stainless-steel springs, and silicon rubber matrix by one-step curing technology. The SMA wires were embedded into a flexible matrix by pouring silicon rubber into a 3D-printed hand-like polylactic acid mold. The good demoulding property was verified after curing at room temperature for 6 h. In addition, the reversible bending behaviors of SSRH were demonstrated by Joule heating actuation. Moreover, the high load-bearing capability was evaluated by suspending the weight experiment. All the results can provide a new guide for soft robotic hands in future medicinal applications.     

A novel fixed-time sliding mode control for nonlinear manipulator systems based on adaptive disturbance observer

Considering that in the trajectory tracking control of a nonlinear robotic manipulator system, the control effect is easily limited by the initial state of the system, and the system has modeling error, unknown disturbance, and friction in the actual control, to overcome the above problems, a fixed-time sliding mode control (SMC) strategy based on adaptive disturbance observer (ADO) is developed in this paper. Firstly, feedforward compensation of the system is achieved by developing an ADO to accurately estimate the compound disturbances in the system. Second, a new fixed-time sliding mode (SM) surface is presented to overcome the singularity issue and accelerate the error convergence. In addition, to enhance the performance of the reaching phase, a variable exponential power reaching law (VEPRL) is developed, which can effectively adjust the convergence rate. Through rigorous theoretical analysis, it is shown that the system state can be stabilized at a fixed time, and an upper bound on the convergence time is also given. Finally, the effectiveness of the control method is verified by comparing it with different control schemes in simulation.     

Modeling and Adaptive Neural Network Control for a Soft Robotic Arm With Prescribed Motion Constraints

This paper presents a dynamic model and performance constraint control of a line-driven soft robotic arm. The dynamics model of the soft robotic arm is established by combining the screw theory and the Cosserat theory. The unmodeled dynamics of the system are considered, and an adaptive neural network controller is designed using the backstepping method and radial basis function neural network. The stability of the closed-loop system and the boundedness of the tracking error are verified using Lyapunov theory. The simulation results show that our approach is a good solution to the motion constraint problem of the line-driven soft robotic arm.      

Design and experimentation of remote driving system for robotic speed sprayer operating in orchard environment

The automation of agricultural machines is an irreversible trend considering the demand for improved productivity and lack of labor in handling agricultural tasks. Unstructured working environments and weather often inhibit a seemingly simple task from being fully autonomously performed. In this context, we propose a remote driving system (RDS) to aid agricultural machines designed to operate autonomously. Particularly, we modify a commercial speed sprayer for orchard environments into a robotic speed sprayer to evaluate the proposed RDS's usability and test three sensor configurations in terms of human performance. Furthermore, we propose a confidence error ellipse-based task performance measure to evaluate human performance. In addition, we present field experimental results describing how the sensor configurations affect human performance. We find that a combination of a semiautonomous line tracking device and a wide-angle camera is the most effective for spraying. Finally, we discuss how to improve the proposed RDS in terms of usability and obtain a more accurate measure of human performance.     

Research on robotic manipulator fault detection and diagnosis technology based on machine vision in complex environments

Currently, machine vision-based fault detection and diagnosis technology for robotic manipulators is widely used. However, traditional machine vision has difficulty identifying manipulator failure in complex environments with dim lighting and large texture differences and the influence of such factors as image motion blur caused by robotic manipulator movement. This article discusses the failure factors of mechanical manipulators and systematically analyzes various links leading to failure and the current technology limitations. First, a gradient-based semantic segmentation method is proposed to extract targets quickly and accurately for the grasped object and complex surrounding environment. Second, when the vision and grasped object have relative movement in dim environment, a multiframe image registration and fusion method are proposed to obtain high-quality, clear image data. Then, a machine-based method is adopted to learn the fault detection and diagnosis methods that fuse internal and external sensors. Finally, a physical system is built to verify the three aspects of the target extraction effect: image clarity, fault detection speed, and diagnosis accuracy, reflecting the superiority of this algorithm.