Development of Human-Like Motion Planning Method Using Task Constrained for Humanoid Robotic Arm With Non-Offset Wrist

The robot's arm used to be limited to manipulation by the industrial manipulator, and most studies on the humanoid robot focus on robots' walking methodologies. Since the multi-DOF (degree of freedom) system, like the humanoid robot's arm, has a high- dimension configuration space that cannot appear geometrically and that has many kinematics and dynamics constraints, the path planning of the system requires much computation and, as such, is very complicated. In this paper, the path planning algorithm was employed for the humanoid robot's (Maru-1) arms. The RRT (rapidly exploring random tree) algorithm, which has been proven to be an efficient approach to multi-arm motion plans, was selected for use in this study. The employed algorithm, with inverse-kinematics analysis, was evaluated in this study using 3D graphic simulation.

Modern agricultural machinery such as harvesting robotic arms are necessary to optimize the semi-automated agricultural processes to meet the ever-growing food needs of the population of the world. Production of low cost and highly efficient robotic arms are needed to meet the needs of the small-scale growers of underdeveloped countries. In this regard, mechanical design, sensor placement, and object identification are essential to optimize the performance of agricultural robots. This research is conducted to design a fully automated robotic arm for fruit picking in hydroponics system and research farm using machine vision to help the smallholders. For this purpose, a robotic arm with 1.3 m in height is designed using aluminum (18-gauge) and mild steel (MS-16) as fabrication material for the mechanical structure. The prototype design is tested for stress estimation at various payloads using SolidWorks simulations. The proposed robotic arm is based on a four degrees of freedom (4-DoF) design with the gripper having an opening range of 5.5 to 12 cm depending upon the task to handle various objects. The robotic arm is installed on an expandable vertical mobile platform that enables an expanded operational workspace with lower energy consumption. The total weight of the robotic arm is 60 kgs housing a payload capacity ranging between 8 and 10 kgs in accordance with industry-standard load-bearing specifications. The inverse kinematic algorithm using DH-table is computed with an accuracy of up to 95 % and target height of gripper is cross checked by mounting the ultrasonic sensor at the base of robotic arm. The YOLOv8 algorithm is used for object detection using the depth sense camera having angle range of 0.3 m to 3 m with the precision of up to 96 %. The time needed for recognition of fruits and pitching was about 15 s, with a success rate of up to 90 %. This research produces a low-cost and efficient solution for stallholders by developing a fully automated robotic arm for fruit picking. It optimizes agricultural productivity through improved mechanical design, sensor accuracy, and object detection which reduces labor costs by increasing efficiency of semi-automated farming systems. Further research is required to optimize the overall weight of the system and automation of the movement of robotic arm system between the consecutive rows to build a fully autonomous fruit picking operation in hydroponics.

Designing a humanoid robot is a complex issue and the exact resemblance of human arm movements has not been achieved in many of the previously developed robots. This paper is going to be much focused on the design of a humanoid robot arm which has a unique approach which has never been developed earlier. Even though all the robots that have been developed using 6-Degrees of Freedom (DOF) and 7-DOF can reach any point in the space, some of the orientation cannot be reached by the end effector plane effectively. So an 8-DOF freedom based robotic arm has been specially designed and developed to resemble the exact movements of the human being. This robot has 3-DOF for shoulder joint, 2-DOF for the elbow joint, and 3-DOF for the wrist with fingers as the end effector. Almost all the robots have only 1-DOF to the elbow joint but here 2-DOF has been proposed to resemble the exact movements of the human being (2-DOF at elbow) to solve the above mentioned problem. Literature reviews and design model are discussed in detail to support the proposal that has been made. Forward and inverse Kinematic relationships are also obtained for the joint link parameter. This humanoid robot arm which has been designed and developed is one of the modules of a human size humanoid robot RALA (Robot based on Autonomous Learning Algorithm).

Under rapid development of science and industry the industrial robots and robotic systems are more widely used in the field of industry. The robots and robotic systems help to manage and master new technological processes efficiently and effectively. In this article there was carried out the analysis of the two degree of freedom (DOF) robot arm, which is widely used in practice in order to reveal its functions and to identify its key dynamic parameters using fuzzy logic system. The mathematical model of robot arm dynamics is composed, which includes the mathematical models of the robot arm links twirling motors and the direct positions task model generating the end point of the robot. After the analysis of the robot model the expanded research was performed – after calculating the missing parameters of experimental 2DOF robot arm motors there was created the robot arm model with „V-REP“ and “MATLAB Simulink” software packages that allowed to set the control voltages and to observe the changing in time processes of link velocity trajectories. There is calculated designed planar robot end point trajectory deviation from the dynamic trajectory.

Significance: The ability of robotic arm system to become teleoperational is essential for advancing remote work. Existing solutions often require complex setups, specialized training, and expensive hardware. Developing a simplified wearable device that can be rapidly integrated and deployed can significantly enhance accessibility where remote control is paramount, including remote surgeries in healthcare and hazardous environments. Aim and approach: This work presents the design and development of a wireless glove-based control system for remotely operating a robotic arm. A reliable, responsive, and user-friendly control system offers scalability, customization options, intuitive interaction, and ergonomic comfort. The approach combines advanced motion sensing using a 9 Degrees of Freedom (DoF) Inertial Measurement Unit (IMU) and real-time data processing. The IMU estimates roll, pitch, and yaw, directly controlling the translational movements (x, y, z) of the robotic arm’s end effector. Depending on button states, these inputs can also switch to control the end-effector rotations. Results: The developed glove demonstrated user compliance. The system is rapid to deploy, and the wireless device successfully captured real-time gesture control data and redirected it to the robotic system with an average latency of 185 ms, enabling the system to perform a range of manipulations, including pick and place, reaching, and grasping objects. Conclusions: This research presents a robust and efficient teleoperation system, offering a viable solution for advancements in remote robotic arm control. This innovation bridges the gap between the full potential of complex robotic systems and user interaction, unlocking new possibilities across various fields.

In this paper, we present the design and implementation of a robotic arm system equipped with both manual and automated control capabilities, facilitated by an Arduino Uno microcontroller and various peripheral modules. The robotic arm is constructed using three SG90 and three MG996R servo motors, providing multi-degree-of-freedom movement for precise manipulation tasks. Integration of an HC- 05 Bluetooth module enables wireless communication, allowing remote control and programming flexibility. A key feature of our system is its vision-based pick and place functionality, enabled by the inclusion of an OV7670 camera module. Utilizing image processing algorithms, the camera captures real- time footage of the arm's surroundings, facilitating object recognition and localization. This enables autonomous operation, where the arm can identify and manipulate objects based on predefined criteria. The versatility of our robotic arm system makes it suitable for a wide range of applications, including industrial automation, educational purposes, and research endeavours. By combining mechanical design, electronics, and software development, we demonstrate a comprehensive approach to the creation of intelligent robotic systems. Key Words: Robotic Arm, 6-DOF, Image Recognition

This paper proposes an external force detection method for humanoid robot arm without using joint torque sensors, which can detect the external force of the joint space in real time during the operation of the robot. We first analyzed the structure of the humanoid robot arm we designed, and then established the external force detection model of the robot arm based on robot dynamics and motor dynamics. Subsequently, analyses were conducted on the error of the detection model and the dynamic model error of the robot arm is compensated by using the artificial neural network method to obtain more accurate external force value for the robot arm. In experiment, the accuracy test and the collision test were performed on the detected extern forces of the robot arm. The results show that the method can effectively improve the detection accuracy of the robot arm, and the robot arm can realize the real-time collision detection during its static and running states, which can ensure the safe operation of the robot.

본 논문에서는 어떤 물체를 들어 올려서 전달하는 인간의 작업을 대신 수행하기 위한 인간형 로봇 팔의 어깨 및 팔꿈치 관절의 토오크 특성을 분석하고자 한다. 이러한 목적을 달성하기 위하여 인간의 일상적인 물체의 리프팅 행동을 고려하고, 단순한 구조의 어깨와 팔꿈치 관절을 갖는 인간형 로봇 팔 모델을 사용하여 어떤 물체를 다양한 경로로 이송하는 시뮬레이션을 수행한다. 이러한 시뮬레이션을 통하여 인간형 로봇 팔을 이용한 물체의 이송 작업에 있어서 요구되는 어깨 및 팔꿈치 관절의 토오크 패턴 및 범위를 사전에 파악할 수 있음을 보인다. 결과적으로, 이러한 어깨 및 팔꿈치 관절의 토오크 분석은 효과적인 로봇 팔 메커니즘 설계에 유용하게 활용될 수 있다. This paper analyses the torque characteristics of the shoulder and elbow joints of a humanoid robot arm that is useful for an object lifting and transferring task instead of human beings. For the purpose, some typical human lifting behaviors are considered, and various simulations for lifting and transferring an object have been performed by employing a humanoid robot arm having a simple configuration of shoulder and elbow joints. Through the simulation, it is shown that the torque patterns and ranges of the shoulder and elbow joints required for such a humanoid lifting and transferring task can be found earlier in the design of a humanoid robot arm. As a result, this effort is useful for us to design an effective robot arm.

In this paper, an interactive robotic anthropomorphic hand system is presented, which was developed as an important tool for the educational outreach activities. The robotic anthropomorphic hand incorporates 15 degrees of freedom, providing sufficient mobility in the demonstration of various postures. To increase the attractiveness of the robotic hand, pneumatic muscle actuators are used to drive the robotic hand motion through artificial tendons. The interaction of the robotic hand with a human is enabled with a control device, which allows the human operator to control the hand motion in a natural way. The robotic hand system has been successfully demonstrated in a recent engineering education outreach event, in which over 100 children at all ages operated the robotic hand through the control device.

Camera Holding Robotic Devices in Urology

The automation and intelligence of industrial manufacturing is the core of the fourth industrial revolution, and robotic arms and proprietary networked information systems are an integral part of this vision. However, with the benefits come risks that have been overlooked, and robotic arms have become a heavily attacked area. In order to improve the security of the robotic arm system, this paper proposes an intrusion detection method based on a state classification model. The closure operation process of the robotic arm is divided into five consecutive states, while a support vector machine based on the particle swarm optimization algorithm (PSO-H-SVM) classifies the operation state of the robotic arm. In the detection process, the classifier predicts the operation state of the robotic arm in real time, and the detection method determines whether the state transfer meets the logical requirements, and then determines whether the intrusion occurs. In addition, a response mechanism is proposed on the basis of the intrusion detection system to make protection measures for the robotic arm system. Finally, a physical experiment platform was built to test the intrusion detection method. The results showed that the classification accuracy of the PSO-H-SVM algorithm reached 96.02%, and the detection accuracy of the intrusion detection method reached 90%, which verified the effectiveness and reliability of the intrusion detection method.

Robots are used to provide help in unsafe, repetitive and dreary situations. This paper concentrates on a robotic arm which can be utilized for crossing of the dead wire structure to the jumper cable of an electrical transmission line. In order to achieve a good positional accuracy of the end-effector, the robotic arm is modeled and analyzed. Denavit-Hartenberg (DH) representation is used for the kinematic modeling and Lagrangian-Euler (LE) method for the dynamic modeling. Since the kinematic and dynamic modeling of robotic arm is highly nonlinear in nature, the optimum trajectory planning is a challenging issue in almost all obstacle avoidance problems. In trajectory planning, the desired trajectory needs to be followed for end-effector positioning. This paper presents kinematic modeling, dynamic modeling, workspace analysis and trajectory planning of a 5 Degree of freedom (DOF) robotic arm. The robotic arm is designed using SolidWorks 2014 software. The complete analysis of the proposed manipulator is simulated using MATLAB Software.

As one of the Artemis systems, Pressurized Rover is being developed by Japan Aerospace Exploration Agency (JAXA). Since this system has $\mathbf{1 1}$ months of uncrewed phase in a year, robotic arm system is installed to perform exploration tasks efficiently. Many robotic arms have been developed and operated in space development, but a robotic arm to operate on the Moon presents unique challenges. In this paper, requirements and concept of robotic arm system for the Pressurized Rover are introduced. Unique challenge such as regolith dust mitigation is described, and candidate technologies to mitigate the issue are presented and discussed.

The humanoid robotic arm has a broad prospect in the field of human-machine cooperation, but the large inertia of the current joint robotic arm slows down its response speed and running speed. In this study, a centrally driven humanoid robotic arm is introduced, which has the characteristics of small inertia, good rapidity, and high load. Screw Theory is used to establish the kinematic model and analyze that the robotic arm has no singular configuration in the working range. The geometric method and Monte Carlo method are used to analyze and verify its workspace, which is the basis for the subsequent dynamic analysis and control of the robotic arm.

Recent advancements in sensing technology have led to developments of lighter and smaller control systems for prosthetic and biomedical applications. In this paper, we develop a bio-inspired sensory system for a master-slave force-sensing robotic hand which allow accurate control and provide a natural sense of touch to humanoid robotic hand based on force information derived from a smart glove equipped. The slave robotic hand is fabricated using three-dimensional (3D) printing technology, with servo motors to actuate the hand components. A glove with miniaturised flexible sensors attached serves as the master robotic hand, providing movement and force signals for the slave to emulate. The signals from the force sensors are used to moderate the movement of the slave hand’s fingers, so allowing delicate objects to be handled without the risk of breakage. We show that this is a practical and versatile method to improve robotic handling, and that with careful selection and tuning, it is possible to track the master hand’s applied force to within 0.1 Newtons. The success of this approach will pave the way for the development of novel control systems using low-cost bio-inspired strain and force sensors for prosthetics applications.