Smart farming technology is becoming of the actual topics in the modern world of technology. Contemporary farming technology expands robot applications by using AI for the recognition of variable patterns. Moreover, the agriculture field demands a safety robot, due to the fragile surrounded confined space and it must be adaptable to extremely constrained working environments. Therefore, this research paper presents a novel tomato harvesting robot arm based on a continuum robot structure. The proposed continuum robot arm flexible backbone structure provides safety and efficient work in a confined workspace. This research paper consists of three parts: the first part of the paper contains the robot design and the newly designed tomato harvesting gripper tool. The second part of the paper describes the machine learning part for detecting matured tomatoes and the distance measuring technique with a single camera. The third part of the research paper explains robot kinematics and control algorithms. The final part of the research paper explains the experimental results. As a result of the conducted experiment, the tomato harvesting speed of the proposed robot was 56 s for a single tomato. Meanwhile, the tomato recognition accuracy was 96 percent.

Wire-driven continuum manipulators are gaining more attention due to their flexibility and dexterity features. In comparison with traditional manipulators, the continuum structure is compliant and safe for human tissue and is able to easily adapt to the unstructured environment. Despite its advantages, wire-driven mechanisms have a serious problem with tension. While pushing and pulling, the wire loses tension, which leads to an ineffective way of driving the pulleys. Therefore, in this research, we propose a novel discrete continuum robot arm with a passive pre-tension mechanism that avoids the wire tension problem. Moreover, this paper will describe the backbone design of the discrete continuum arm and pre-tension mechanism structure, as well as forward and inverse kinematics and kinetic solutions, with simulation results.

This paper presents cable-driven bio-inspired continuum robot with variable backbone hardness and with mounted compliant universal joints. Proposed continuum robot has nine segments and each one of which has two rotary joints. Kinetics, kinematics and design concerns are very challenging in continuum robots. So, in this research, we will present a novel approaches of kinetic, kinematic modeling and design solutions of the wire-driven universal joint backboned continuum robot. Moreover, experimental validation results will be presented as well.

In the contemporary world, inspection operations have become a critical component of infrastructure maintenance. Over the years, the demand for comprehensive inspection of pipes, both internally and externally, has grown increasingly complex and challenging. Consequently, there is a pressing need for significant advancements in in-pipe robots, particularly in the areas of inspection speed, defect detection precision, and overall reliability. Recent developments in new devices and sensors have markedly improved our capability to inspect and diagnose defects within pipes with greater accuracy. Furthermore, the application of machine learning tools has optimized the inspection process, enhancing the detection and recognition of potential pipe defects, such as rust, blockages, and welding anomalies. This research introduces a novel mobile robot platform specifically designed for pipe inspection. It integrates an advanced machine learning model that effectively detects and identifies key pipe defects, including rust, compromised welding quality, and pipe deformation. Additionally, this platform offers enhancements in inspection speed. The integration of these technologies represents a significant stride in the field of infrastructure maintenance, setting a new standard for efficiency and precision in pipe inspection.

Nowadays contemporary economic growth and increasing industrial needs demand robots to have a multifunctional properties such as a wide applicability, precision, wide workspace and simple robot-human interface. Modern, robot industry presents two types of robot architecture: serial and parallel, serial robots have a wide workspace, easy forward kinematics and have a wide applicability in industry, while parallel manipulators are not applies widely in industry. However, parallel manipulator has some beneficial properties as well, such as rigidity, high payload capacity and high precision. Although majority robots in industry are serial manipulators, but some areas of industry where requires high precision and rigidity utilizes parallel manipulators such as medicine, pharmacy and microchip assembling industry. The novelty of this project is creating of new type of robot combination: common serial manipulator with Delta robot. Furthermore, to investigate capabilities of this Hybrid robot in terms kinematics, dynamics, payload capacity and accuracy. Moreover, this research will explain serial/parallel hybrid manipulator design structure, workspace control system and kinematics of both manipulators as well.

Triboelectric nanogenerators (TENGs) are getting popular as biomechanical energy harvesters to power small electronic devices and as self‐powered sensors for pressure, motion, vibration, wind, waves, biomedical information, and chemical substance detections. In this study, the TENG is designed with biocompatible materials, and concentrations of its components have been optimized to generate higher power for application as an energy source and tactile sensor. The process involves using metal‐organic frameworks (MOFs), namely MIL‐125, with high charge‐inducing and charge‐trapping capabilities incorporated into the commercial Ecoflex matrix. Electrical characterization demonstrated that the sample with 0.25 wt% MIL‐125 (0.25%MOF/Ecoflex) is the optimal concentration in the matrix with an output of up to 305 V and 13 µA, respectively. Moreover, the proposed flexible TENG converts mechanical energy to electrical, with a maximum power density of 150 µW cm⁻² (1.5 W m⁻²), which is more than twice superior to the pristine Ecoflex‐based counterparts. The TENG shows robust and stable performance without noticeable degradation during continuous 200,000 cyclic testing. Furthermore, 0.25%MOF/Ecoflex TENG can power small electronic devices such as calculators, humidity sensors, and cardiac pacemakers. A robotic gripper trained via machine learning to identify various objects is also successfully developed with a self‐powered 0.25%MOF/Ecoflex TENG sensor.