DOF Robotic Arm Research Articles

Trajectory Optimization for a 6 DOF Robotic Arm Based on Reachability Time

The design of the robotic arm's trajectory is based on inverse kinematics problem solving, with additional refinements of certain criteria. One common design issue is the trajectory optimization of the robotic arm. Due to the difficulty of the work in the past, many of the suggested ways only resulted in a marginal improvement. This paper introduces two approaches to solve the problem of achieving robotic arm trajectory control while maintaining the minimum reachability time. These two approaches are based on rule-based optimization and a genetic algorithm. The way we addressed the problem here is based on the robot’s forward and inverse kinematics and takes into account the minimization of operating time throughout the operation cycle. The proposed techniques were validated, and all recommended criteria were compared on the trajectory optimization of the KUKA KR 4 R600 six-degree-of-freedom robot. As a conclusion, the genetic based algorithm behaves better than the rule-based one in terms of achieving a minimal trip time. We found that solutions generated by the Genetic based algorithm are approximately 3 times faster than the other solutions generated by the rule-based algorithm to the same paths. We argue that as the rule-based algorithm produces its solutions after discovering all the problem’s searching space which is time consuming, and it is not the case as per the genetic based algorithm.

Modeling and Real-Time Cartesian Impedance Control of 3-DOF Robotic Arm in Contact with the Surface

Modeling and Real-Time Cartesian Impedance Control of 3-DOF Robotic Arm in Contact with the Surface

2DoF Robotic Arm using Reinforcement Learning

The 2DoF Robo-Arm Reinforcement Learning aims to develop an intelligent system that can learn to control a robotic arm with two degrees of freedom using reinforcement learning techniques. The concept here involves the use of a simulated environment in which the robotic arm can interact with different objects and learn to perform tasks such as reaching, grasping, and moving objects. The idea here seeks to improve the efficiency and effectiveness of robotic arm control in industrial and manufacturing applications, enabling them to perform complex tasks with good accuracy and speed. The overview of the proposed system’s objectives, methodology, and results, demonstrating the potential of this technology to control Robotic Arm and also its use in Automation are discussed here

Programming 2DoF Robot Arm Based on Forward Kinematics using Arduino

This project's main goal is to use Arduino to program a 2-degree-of-freedom (2Dof) robot arm based on forward kinematics. The suggested system is made to precisely regulate the robot arm's motion for a variety of activities, such as pickand-place duties and assembly procedures. The robot arm can be precisely positioned and controlled thanks to the forward kinematics model, which determines the end effector's position based on the joint angles. The system is put into practice and put through its paces in a real-world setting, and the results demonstrate its ability to conduct precise movements with decent accuracy. The 2DoF Robot Arm can be utilized to automate complex processes and boost productivity in a variety of industries, including manufacturing, healthcare, and robotics research.

2D of Robotic Arm Based on Inverse Kinematics Using Arduino

Abstract: The advancements and quick developments in the technology has made it possible to create a platform for the invention of new as well as great things. One of the concept that is being used lately to improve the technology is the concept of robotics. This project focuses on the implementation of inverse kinematics for a 2-DOF (two degrees of freedom) robot arm using an Arduino microcontroller. The inverse kinematics equations are derived and programmed to calculate the joint angles required to position the robot arm's end effector in a desired location within its workspace. The Arduino board is used to interface with the robot arm's actuators and control the movement of the arm. The Arduino programming aspect of the project involves developing a control algorithm that accepts target coordinates for the end effector and computes the necessary joint angles using inverse kinematics equations. Servo motors are manipulated through the Arduino Servo library to achieve the calculated angles, enabling the arm to reach the desired positions accurately and efficiently. To enhance the functionality and safety of the 2-DOF robotic arm, the project includes features such as calibration routines, testing procedures, and safety measures to avoid collisions and servo load. The project aims to demonstrate the application of inverse kinematics in controlling robotic manipulators for precise positioning tasks

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

AbstractMost studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm

Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm

Vacuum-powered soft actuator with oblique air chambers for easy detachment of artificial dry adhesive by coupled contraction and twisting

ABSTRACT A gecko foot-inspired, mushroom-shaped artificial dry adhesive exploiting intermolecular forces between microstructure and surface has drawn research attention for its strong adhesive force. However, the high pull-off strength corresponding to the adhesive force matters when detaching fragile substrates. In this study, we report a vacuum-powered soft actuator having oblique air chambers and a dry adhesive. The soft actuator performs coupled contraction and twisting by applying negative pneumatic pressure inward and exhibits not only high pull-off strength but also easy detachment. This effective detachment can be achieved thanks to the twisting motion of the soft actuator. The detachment performances of the actuator models are assessed using a 6-degrees-of-freedom robot arm. Results show that the soft actuators exhibit remarkable pull-off strength decrement from ~20 N cm−2 to ~2 N cm−2 due to the twisting. Finally, to verify a feasible application of this study, we utilize the inherent compliance of the actuators and introduce a glass transfer system for which a glass substrate on a slope is gripped by the flexibility of the soft actuators and delivered to the destination without any fracture.

A PID-Controlled Approach in the Design of a Physiotherapy Robot for Upper Arm Rehabilitation

The main objective to develop controller for the rebab robot, therefore to achieve the main objective, the subsequent objective is to formulate the mathematical modelling of 2 DOF of upper arm, to develop upper arm rebab robot, evaluate the performance of the controller and facilitate the creation of new and more improved devices for physiotherapy robot ; therefore, this research will investigate rehabilitation robots, it have shown a high potential for improving the patient’s mobility, improving their functional movements and assisting in daily activities. However, this technology is still an emerging field and suffers from several challenges like compliance control and dynamic uncertain caused by the human–robot collaboration. The main challenge addressed in this thesis is to develop a controller to the rehab robot and formulate the mathematical modelling of 2 DOF of upper arm. Ensure that the exoskeleton robot provides a suitable compliance control that allows it to cooperate perfectly with humans even if the dynamic model of the exoskeleton robot is uncertain. The PID controller is a widely used control algorithm that aims to regulate a system's output by continuously adjusting its input. In the context of a two-degree-of-freedom (2-DOF) robot arm, the PID controller plays a crucial role in achieving precise and accurate arm movements. The Proportional-Integral-Derivative (PID) controller employs three main components to achieve control: proportional, integral, and derivative terms.

Modeling and Simulation of Robotic Palpation to Detect Subsurface Soft Tissue Anomaly for Presurgical Assessment

Abstract Surgical Haptics is an emergent field of research to integrate and advance the sense of robotic touch in laparoscopic tools in robot-assisted minimally invasive surgery. Haptic feedback from the tooltip and soft tissue surface interaction during robotic palpation can be leveraged to detect the texture and contour of subsurface geometry. However, precise force modulation of the robotic palpating probe is necessary to determine stiff inclusions of the anatomy and maneuver successive manipulation tasks during surgery. This paper focuses on investigating the layered deformations associated with different force profiles involved in manipulating the superficial anatomy of soft tissues during dynamic robotic palpation to determine the underlying anomaly. A realistic three-dimensional (3D) cross-sectional soft tissue phantom with anatomical layers and tumor, as an anomaly, is designed, modeled, and analyzed to examine the effects of oriented palpating forces (0–5 N) of a 7 DOF robot arm equipped with a contoured palpation probe. Finite element static structural analysis of oriented robotic palpation on the developed 3D soft tissue phantoms (with and without anomaly) reveals the soft tissue layer deformations and associated strains needed to identify presence of stiffer inclusions or anomaly during Robotic palpation. The finite element analysis study shows that the difference in deformations of soft tissue layers (e.g., underlying myofascial layers) under stiffer inclusions at different force levels can facilitate haptic feedback to acquire information about subsurface tumors. The deformation variations are further compared to assess better palpation orientations for subsurface anomaly detection.

CBMC: A Biomimetic Approach for Control of a 7-Degree of Freedom Robotic Arm.

Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot's operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology.

Performance Analysis of a 5-DOF Robotic Arm by Implementing a Graphical User Interface

Today, the topic of performance in industrial robotics is being addressed by researchers at a much faster pace than a few years ago. In 2023, there are a variety of applications for industrial robotics, such as agriculture, education, and the film industry. In this paper, the implementation of Graphical User Interface (GUI) as a controller input between an Android smartphone and the Arduino was discussed. This GUI was developed to help the user in controlling the robotic arm. For the movement of 5-degree motion of a robot, the concept of inverse kinematics (IK) was applied to the system. To test the performance of the robot, a simple test had done by drawing the basic shape by putting the pencil into the gripper. Then, these drawings were compared with the original by calculating the percentage error of the dimensions from the center to the outside. The result shows that the robotic arm is capable of performing all the tasks assigned to it and can also be controlled through the graphical user interface of a smartphone. Therefore, implementing Bluetooth Wi-Fi as a communication between smartphone and robot is a good way to study the performance of robots, especially for small and lightweight robotic arms.

Wearable teleoperation controller with 2-DoF robotic arm and haptic feedback for enhanced interaction in virtual reality.

Teleoperation is an essential component in fields such as medicine and manufacturing, enabling human operators to interact with remote robotic systems. A wearable device has been designed and manufactured to enhance sensitivity, wearability, and synchronization, providing users with the ability to experience the sensation of grasping virtual objects. The device incorporates a 2-DoF robotic arm, haptic sensors for finger gestures, and a Unity-powered virtual scene system. Its effectiveness was evaluated through user experiments, where participants were asked to rank the weights of three virtual balls and identify the direction of force applied to a virtual ball in separate tests. Additionally, the device's ability to render various shapes was also examined. The experiments showed that 73.3% of participants accurately ranked the balls by mass, and an overall correctness rate of 87.3% was achieved for force direction identification. For shape rendering, the device yielded more accurate results for simple objects like spheres, whereas rendering more complex objects such as cups and cones was challenging. The findings indicate that this wearable device has potential applications in haptic feedback and virtual reality contexts. Despite the challenges with complex shape rendering, the device shows promising capability in enhancing the user's immersive experience in virtual environments.

Design and development of a low-cost 5-DOF robotic arm for lightweight material handling and sorting applications: A case study for small manufacturing industries of Pakistan

Due to the ever-increasing demand for higher production rates and the shortage of skilled labor in small industries, material handling and sorting have become extremely tedious and challenging. Industrial automation-led effective material-handling solutions like robotic arms have gained immense importance as they provide an alternative to human involvement, contribute to higher sorting accuracy, and provide enhanced safety. However, the adaptability of these robotic arms in small manufacturing industries in Pakistan is mainly hindered due to their higher costs and concerns with their structural durability. This paper presents the development of a low-cost 5-DOF robotic arm with a designed payload limit of 1 kg and automatically sorts objects fed through a conveyer belt. Catering to the compact sizing, high strength, and lower payload requirements of small industries, aluminum was selected as the material of the robotic arm due to its superior strength-to-weight ratio while being lightweight. Arm geometry was developed using SOLIDWORKS® software, which was further processed in ANSYS® software to perform the static structural analysis of the robotic arm using Finite Element Analysis (FEA). The fine meshing of the robotic arm assembly was done using triangular elements with the total number of elements and total nodes 52134 and 89104, respectively. A single point load was applied on the end effector, and the force was kept downward with an incremental loading of 1 kg starting from 100 g. These FEA simulations show that the robotic arm can hoist considerable weight while maintaining its structural integrity and directionality. The proposed robotic arm is also well-suited for manipulating objects in tight spaces due to its compact size and customizable range of motion, making it an ideal choice for applications that require precise manipulation of light loads.

Design and Analysis of a Four DoF Robotic Arm with Two Grippers Used in Agricultural Operations

Both academic and commercial interest in agricultural robots has increased recently. This is due to the fact that agricultural robots address significant issues such as seasonal labor shortages during harvest and the rising concern over environmentally friendly practices. Because of these, several distinct agricultural robots have already been created for a variety of purposes, with varying degrees of success, including monitoring, spraying, harvesting, transport, etc. As a result, agriculture automation became unfeasible and unprofitable. The purpose of this study is to provide a new methodology for multitasking in performing agriculture operations by designing a 4 Degrees of freedom (4DoF) robotic arm with a new mechanism that has a different configuration with 2 grippers. We did kinematics and kinetics calculations using Denavit-Hardenberg (D-H) method with Lagrangian mechanics, and with the help of Christoffel Symbols of the First Kind, also We used Robot Operating System (ROS) to provide potential solutions using it. It is easy to be paired with other open-source technologies, such as android or IoT technologies. The robot arm can work synchronously with other hardware, sensors, cameras, and agricultural machines concerning farming operations. In conclusion, we believe that this new configuration will open a door for agricultural tasks to be easily automated and achieved using robotic technologies.

Design, manufacture, and control motion of cartesian robot prototype with 5-DOF robot arm for possible spinning applications at laboratory-level

This document presents the development of a prototype of a Cartesian robot with a 5 degrees-of-freedom articulated robot arm for possible applications in a wet spinning process at the laboratory-level. The mechatronic system was developed using various modular electronics elements compatible with an ArduinoTM Mega 2560 microcontroller. The control algorithm recognizes the position of the servomotors and the speed of the motors so that the prototype performs a complete cycle of displacement in 67 s, which includes the transport of a polymeric filament immersed in a coagulation bath for 10 s. A structural analysis indicates that there will be no tension failures because the maximum and axial stress of the Cartesian robot was 2.89 MPa while the von Mises tension of the robotic arm was 468 MPa, both tensions below their upper limits. The pulse signals, in the order of 4000 +25 ms, of the servomotors were consistent in 96-98% repeatability and 3-19% reproducibility. Forty percent of the extrusion tests performed were satisfactory, since the transport of a polymeric filament within a coagulant solution was achieved.

An Incremental Inverse Reinforcement Learning Approach for Motion Planning with Separated Path and Velocity Preferences

Humans often demonstrate diverse behaviors due to their personal preferences, for instance, related to their individual execution style or personal margin for safety. In this paper, we consider the problem of integrating both path and velocity preferences into trajectory planning for robotic manipulators. We first learn reward functions that represent the user path and velocity preferences from kinesthetic demonstration. We then optimize the trajectory in two steps, first the path and then the velocity, to produce trajectories that adhere to both task requirements and user preferences. We design a set of parameterized features that capture the fundamental preferences in a pick-and-place type of object transportation task, both in the shape and timing of the motion. We demonstrate that our method is capable of generalizing such preferences to new scenarios. We implement our algorithm on a Franka Emika 7-DoF robot arm and validate the functionality and flexibility of our approach in a user study. The results show that non-expert users are able to teach the robot their preferences with just a few iterations of feedback.

Modeling and Simulation for a Five DOF Robotic Arm Manipulator

Nowadays, service robots are used in a large number of applications in the service industry. With the booming service industry in third world countries, service robots are expected to be used in a wider range of applications, so it is of great importance to conduct research on service robots. This paper develops the model of a kinematic models for a 5 DOF robotic arm that used in service industry in SolidWorks. The robotic arm’s mathematical model is predicated on Denavit-Hartemberg (DH) method, which determine the robot joints angle vector. The procedure aims at describing the forward and inverse kinematic analysis, and finally determine the end effector’s position and orientation through the corresponding joints angle related to the coordinate system (CS), in order to control the robotic arm to reach the designated location in space. A simulation model of the robotic arm was built using the Matlab Robotics Toolbox to verify the correctness of forward and inverse kinematics, which also can simplify the calculation and analysis.

Learning to Search in Task and Motion Planning With Streams

Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables. Recent works such as PDDLStream [1]garrett2020pddlstream have focused on optimistic planning with an incrementally growing set of objects until a feasible trajectory is found. However, this set is exhaustively expanded in a breadth-first manner, regardless of the logical and geometric structure of the problem at hand, which makes long-horizon reasoning with large numbers of objects prohibitively time-consuming. To address this issue, we propose a geometrically informed symbolic planner that expands the set of objects and facts in a best-first manner, prioritized by a Graph Neural Network that is learned from prior search computations. We evaluate our approach on a diverse set of problems and demonstrate an improved ability to plan in difficult scenarios. We also apply our algorithm on a 7DOF robotic arm in block-stacking manipulation tasks.