Joint Robot Arm Research Articles

A Robust Online Diagnostic Strategy of Inverter Open-Circuit Faults for Robotic Joint BLDC Motors

As robots are increasingly used in remote, safety-critical, and hazardous applications, the reliability of robots is becoming more important than ever before. Robotic arm joint motor-drive systems are vulnerable to hardware failures due to harsh operating environment in many scenarios, which may yield various joint failures and result in significant downtime costs. Targeting the most common robotic joint brushless DC (BLDC) motor-drive systems, this paper proposes a robust online diagnostic method for semiconductor faults for BLDC motor drives. The proposed fault diagnostic technique is based on the stator current signature analysis. Specifically, this paper investigates the performance of the BLDC joint motors under open-circuit faults of the inverter switches using finite element co-simulation tools. Furthermore, the proposed methodology is not only capable of detecting any open-circuit faults but also identifying faulty switches based on a knowledge table by considering various fault conditions. The robustness of the proposed technique was verified through extensive simulations under different speed and load conditions. Moreover, simulations have been carried out on a Kinova Gen-3 robot arm to verify the theoretical findings, highlighting the impacts of locked joints on the robot’s end-effector locations. Finally, experimental results are presented to corroborate the performance of the proposed fault diagnostic strategy.

Visual Servo Algorithm Of Robot Arm For Pick And Place Application

The robot arm is a device consisting of a moving chain of links connected by joints. Electrical motors are frequently used to move each robot arm joint. An end-effector that can move freely in space is usually attached to one end of the robot platform, which is fixed. Robot arms can do repetitive operations at rates and precision far exceeding human operators. Nowadays, robot arm systems are widely used worldwide to increase the quality and efficiency of the manufacturing process in the industry. Typical applications of the robot arm system are assembly, painting, welding, pick and place operation, and others. Besides, many industries employ robot arms for various jobs such as selecting and putting, painting, and material handling. However, one of the most challenging issues in completing these jobs is determining the target location of the robot arm's end-effector. There are two different methods for analyzing the robot arm's movement: forward and inverse kinematic analysis. Based on the visual servo algorithm, this study uses inverse kinematics to execute the pick and place operation. First, an object recognition algorithm is implemented to identify the object to be grasped. Then, an algorithm to avoid any obstacles is done. The study's findings show that good system performance has been obtained in all three algorithms: first, object recognition algorithm, second, obstacle avoidance algorithm, and lastly, visual servobased pick and place operation. Thus, it can be concluded that the robot arm's visual servo algorithm is suitable for pick-and-place applications.

Investigation of a 2-DOF GER fluid damper in cut mode

Dampers incorporating smart materials have attracted significant interest for their application in vibration and shock isolation. Traditionally, these dampers work through shear, flow, and squeeze modes, each unique in its strengths and limitations. However, this study introduces a working mode, termed cut mode, which integrates the strengths of these existing modes. Structurally resembling the flow mode, operationally akin to the shear mode, and behaving similarly to the squeeze mode, the cut mode promises a novel approach to damping. Consequently, extensive research has been conducted at the particle level to develop a mathematical model for the cut mode, grounded in chain formation and breaking mechanisms. Thereafter, a verification prototype damper was designed to demonstrate this concept’s practicality, with the Giant Electrorheological (GER) fluid—a rheological fluid with exceptional yield strength—being employed. This damper features a multilayer cylindrical structure tailored for the cut mode, providing 2-degree of freedom (DOF) damping capabilities. This dual-DOF design is ideal for robotic arm joint applications, as it can offer damping for both axial translation and circumferential rotation simultaneously. In addition, four orifice boards were designed, fabricated, and tested to optimize and enhance the effectiveness of the cut mode further. The results are impressive, the proposed cut mode damper exhibits superior performance characteristics, indicating its great potential for engineering applications.

Advancements and Challenges in Robot-Assisted Bone Processing in Neurosurgical Procedures.

Practical applications of nerve decompression using neurosurgical robots remain unexplored. Our ongoing research and development initiatives, utilizing industrial robots, aim to establish a secure and efficient neurosurgical robotic system. The principal objective of this study was to automate bone grinding, which is a pivotal component of neurosurgical procedures. To achieve this goal, we integrated an endoscope system into a manipulator and conducted precision bone machining using a neurosurgical drill, recording the grinding resistance values across 3 axes. Our study encompassed 2 core tasks: linear grinding, such as laminectomy, and cylindrical grinding, such as foraminotomy, with each task yielding unique measurement data. In linear grinding, we observed a proportional increase in grinding resistance values in the machining direction with acceleration. This observation suggests that 3-axis resistance measurements are a valuable tool for gauging and predicting deep cortical penetration. However, problems occurred in cylindrical grinding, and a significant error of 10% was detected. The analysis revealed that multiple factors, including the tool tip efficiency, machining speed, teaching methods, and deflection in the robot arm and jig joints, contributed to this error. We successfully measured the resistance exerted on the tool tip during bone machining with a robotic arm across 3 axes. The resistance ranged from 3 to 8 Nm, with the measurement conducted at a processing speed approximately twice that of manual surgery performed by a surgeon. During the simulation of foraminotomy under endoscopic grinding conditions, we encountered a -10% error margin.

H∞ control of switched Lurie systems with dwell time

This article deals with the [Formula: see text] control problems of switched Lurie systems. Our purpose is to design switching signals and state feedback controllers with time-varying control gains that make the closed-loop switched Lurie systems absolutely stable. Based on switched time-varying Lyapunov–Lurie function, time-varying controllers corresponding to two kinds of switching signals are proposed. On this basis, the control synthesis conditions are given. It is worth mentioning that our results can deal with the situation that all subsystems are not stabilizable. Finally, the obtained results are applied to a flexible joint robotic arm model with switchings of parameter values. Another simulation example of switched Lurie systems is considered to illustrate the validity of the presented approaches.

Application of Fuzzy Logic for Robotic Arm Joint Control of an Explosive Ordnance Disposal Unit

Precise luffing of payload in mobile cranes is a crucial part of material handling and safety in industrial operations. Explosive ordnance disposal uses a bomb disposal robot to safely disable explosive devices at a safe distance. This robot is a mobile jib crane type with a gripper as an end effector instead of the typical suspension cable with a hook. The common challenge of this crane type is the arm/jib movement sensitivity with respect to tip-over stability of the crane body. This is directly influenced by the payload and constrained by the degree of freedom. This paper presents a control strategy of the joint for luffing such that the back wheels remain in contact with the surface ground and avoid bucking at any given instance of weight change in the payload. Fuzzy logic was applied to control the motor torque and luffing angle of the arm in response to the load and gripper opening size to maintain the tip-over stability margin at the highest value possible. The response curves at different configurations of the two input signals were also analyzed based on the rules set to determine its precision with respect to the expected response curve.

A Review of Robotic Arm Joint Motors and Online Health Monitoring Techniques

A Review of Robotic Arm Joint Motors and Online Health Monitoring Techniques

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.

Trajectory analysis and optimization of sea buckthorn fruit vibration separation manipulator based on I-PSO algorithm

In this paper, the optimal time planning of vibration separation trajectory of Hippophae rhamnoides fruit is studied for space manipulator using the I-PSO algorithm. The first step is to analyze the motion of the robotic arm's joints, which are limited in range and speed, in combination with a 3–5–3 polynomial interpolation, an improved Particle swarm optimization with adaptive inertia weight and asynchronous learning factor is proposed, and the specific process is given. Experimental images and data show that the improved particle swarm optimization algorithm can ensure the continuity of joint acceleration and velocity, and the optimal vibration trajectory time is 0.536539094 s Compared with the planned system trajectory time of 0.71022 s, the speed increased by 24.5%. The results of the orthogonal experiment show that the average fruit drop rate reaches 96.19%, which verifies the validity and reliability of the I-PSO algorithm for optimal time planning of seabuckthorn fruit separation vibration trajectory.

Kinematic Analysis of a Centrally Driven Humanoid Robotic Arm

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.

The Five-DOF Explosion-Removal Manipulator Based on Position and Velocity Feedforward Compensation Control.

The main problem with a robotic system arm is its sensitivity to time delays in the control process. Due to this problem, it is necessary to further optimize the control process of the system. One solution is to deal with the control accuracy and response speed issues of robotic arm joints, to improve the system's response performance and enhance the system's anti-interference ability. This paper proposes a speed feedforward and position control scheme for robotic arm joint control. The conclusion section shows that compared to traditional five-degree-of-freedom robotic arm systems, the addressed robotic arm control system has a lower tracking delay and better dynamic response performance. It can improve the system's response performance while also enhancing its anti-interference ability.

Topological Analysis of Vector-Field Guided Path Following on Manifolds

A path-following control algorithm enables a system’s trajectories under its guidance to converge to and evolve along a given geometric desired path. There exist various such algorithms, but many of them can only guarantee local convergence to the desired path in its neighborhood. In contrast, the control algorithms using a well-designed guiding vector field can ensure almost global convergence of trajectories to the desired path; here, “almost” means that in some cases, a measure-zero set of trajectories converge to the singular set where the vector field becomes zero (with all other trajectories converging to the desired path). In this article, we first generalize the guiding vector field from the Euclidean space to a general smooth Riemannian manifold. This generalization can deal with path-following in some abstract configuration space (such as robot arm joint space). Then, we show several theoretical results from a topological viewpoint. Specifically, we are motivated by the observation that singular points of the guiding vector field exist in many examples where the desired path is homeomorphic to the unit circle, but it is unknown whether the existence of singular points always holds in general (i.e., is inherent in the topology of the desired path). In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space, we provide an affirmative answer, and conclude that it is not possible to guarantee global convergence to desired paths that are homeomorphic to the unit circle. Furthermore, we show that there always exist nonpath-converging trajectories (i.e., trajectories that do not converge to the desired path) starting from the boundary of a ball containing the desired path in an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional Euclidean space where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n \geq 3$</tex-math></inline-formula> . Examples are provided to illustrate the theoretical results.

Process Simulation and Optimization of Arc Welding Robot Workstation Based on Digital Twin

For the welding cell in the manufacturing process of large excavation motor arm workpieces, a system framework, based on a digital twin welding robot cell, is proposed and constructed in order to optimize the robotic collaboration process of the welding workstation with digital twin technology. For the automated welding cell, combined with the actual robotic welding process, the physical entity was digitally modeled in 3D, and the twin welding robot operating posture process beats and other data were updated in real time, through real-time interactive data drive, to achieve real-time synchronization and faithful mapping of the virtual twin as well as 3D visualization and monitoring of the system. For the robot welding process in the arc welding operation process, a mathematical model of the kinematics of the welding robot was established, and an optimization method for the placement planning of the initial welding position of the robot base was proposed, with the goal of smooth operation of the robot arm joints, which assist in the process simulation verification of the welding process through the virtual twin scenario. The implementation and validation process of welding process optimization, based on this digital twin framework, is introduced with a moving arm robot welding example.

Workspace Description and Evaluation of Master-Slave Dual Hydraulic Manipulators

Nuclear power plant emergency robots are robots used to respond to significant public safety incidents, such as uncontrolled radioactive sources and nuclear catastrophe leaks. However, there are no standardized evaluation criteria for the optimal design of the robots. We offer a quantitative analytic algorithm for optimizing nuclear power plant emergency robots to address this issue. The method optimizes the structural parameters of the robot in accordance with the workspace by analyzing, comparing, and evaluating the workspace. The approach comprises constructing a kinematic model of the mechanical arm and proposing an optimization algorithm based on the alpha shape to accurately describe the manipulator workspace; employing the proposed convex hull algorithm to quantitatively analyze and evaluate the workspace generated by different solutions in terms of area, volume, task demand, Structural Length Index and Global Conditioning Index; and determining the robotic arm joint parameters by selecting the optimum workspace design solution. Using the suggested algorithm, we optimize the design of the master and slave robotic arms of the nuclear power plant emergency robots. Theoretical calculations and simulation results demonstrate that the method is an effective and practical evaluation technique that not only accurately describes the workspace but also optimizes the design of the nuclear power plant emergency robots.

Double-Arm Cooperation and Implementing for Harvesting Kiwifruit

Double-arm picking robots are widely used in agricultural production for their high collaborative efficiency. While picking, area planning and collision detection between the mechanical arms is a crucial challenge for the double-arm robot, which needs to establish a collision-free path for fruit picking. In this study, we developed a double-arm cooperation method for robotic picking of kiwifruit. Firstly, the problem of dividing the picking area was simplified into a multiple traveling salesmen problem (MTSP) to be solved. The picking sequence of each robotic arm was formulated by the principle of similar picking numbers, and combined with the brainstorming optimization algorithm (BSO). Secondly, a double-arm parameter model was built to solve the forward and backward movements of the robotic arms and to figure out the joint position. The spatial mathematical relationship of the bounding boxes between the robotic arms was used to detect the collision between the two robotic arms, in order to achieve the avoidance between the robotic joints. Then, simulation software was applied to the simulation and analyzed the availability of picking area planning and collision detection. The simulation results showed that the optimized picking sequence planning using BSO was more efficient; the smooth joint trajectory during the movement of the robotic arms met the limits on the range of movement and on the angular velocity of the robotic arm joints. Finally, based on the simulation result, a double-arm collaboration platform was tested. The double-arm collaboration platform harvesting trials showed that the average picking success rate was 86.67%, and collision detection time was 3.95 ± 0.83 s per fruit. These results indicated that the proposed method could plan the operation tasks of the double-arm picking robot system, and effectively implement the collision-free picking operation.

Codes application in trajectory generation of simulated robot arm dynamics

The robot arm used in this research had already been modelled and simulated using Simulink software (i.e. software environment) to yield a robot arm that will mimic the functionality and control of real human arm. The main objective is not to rebuild the robot arm to further enhance tasks performance by armless people in the society, rather to verify the significance of the code (program) application in trajectory generation of simulated robot arm or when simulating a robot arm. The common problem encountered with trajectory generation is to connect the initial configuration to a final configuration while satisfying other specified constraints at the endpoints (e.g., velocity constraints). The trajectory parameters (such as angle and angular velocity) of the robot arm joint had already been simulated with reference to the real human arm in order to generate a robot dynamics (trajectory) of same and equal functionality to real human arm. The code system which implies writing of appropriate languages was confirmed to contain the performance and controlling information of the robot arm. This performance and control of the robot arm appears to be the trajectory generation of the simulated robot arm. Hence, the input languages into the software environment (Simulink) were presented as codes which resulted to (have output of the desired functions) to trajectory generation of the simulated robot arm.

Design of a lightweight robotic arm for kiwifruit pollination

Robotic simulation is an important method for verifying robot design parameters and the feasibility of various schemes. We simulated and analysed the effectiveness of the design parameters of a robotic arm for kiwifruit pollination and verified the rationality of the robotic arm’s movement during pollination. First, the basic parameters and working space of the robotic arm for kiwifruit pollination were identified based on a trellis system that supports kiwifruit vines. MATLAB software was employed for the simulation to prove that the working space of the lightweight robotic arm for pollination can satisfy the operating requirements. Appropriate joint motors were selected based on the maximum theoretical torque on each joint of the robotic arm obtained through the analysis. SolidWorks software was used to verify the strength of the robotic arm materials and joint connectors, and the operating environment of the robotic arm and MATLAB software were applied to simulate and analyse the feasibility of trajectory planning for pollination. Based on the simulation results, we built a prototype of the robotic arm and a motion control system. The experiment indicated that the maximum operating speed of this lightweight pollination robotic arm was ωmax = 0.105 rad/s, the pollination success rate was 85%, the average pollination time of a single flower was 5 s, and the pollination efficiency was 78 min/mu, which was significantly higher than the 86 min/mu of artificial brush pollination. The lightweight pollination robotic arm satisfied the automatic operation requirements of kiwifruit pollination.

R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm.

Neuromorphic computing is a recent class of brain-inspired high-performance computer platforms and algorithms involving biologically-inspired models adopting hardware implementation in integrated circuits. The neuromorphic computing applications have provoked the rise of highly connected neurons and synapses in analog circuit systems that can be used to solve today's challenging machine learning problems. In conjunction with biologically plausible learning rules, such as the Hebbian learning and memristive devices, biologically-inspired spiking neural networks are considered the next-generation neuromorphic hardware construction blocks that will enable the deployment of new analog in situ learning capable and energetic efficient brain-like devices. These features are envisioned for modern mobile robotic implementations, currently challenging to overcome the pervasive von Neumann computer architecture. This study proposes a new neural architecture using the spike-time-dependent plasticity learning method and step-forward encoding algorithm for a self tuning neural control of motion in a joint robotic arm subjected to dynamic modifications. Simulations were conducted to demonstrate the proposed neural architecture's feasibility as the network successfully compensates for changing dynamics at each simulation run.

Development of a programmable robotics arm using MATLAB

A robotic arm is a manipulator for robots that can be programmed to do tasks that a human arm can perform. As with an articulated robot, the manipulator's linkages are joined by joints that enable either rotational motion or linear displacement. A kinematic chain may be thought of as being formed by the manipulator's linkages. The robot arms may be operated autonomously or manually and are capable of performing a wide range of activities with high precision. Both stationary and wheeled versions of the robotic arm may be used in industrial and domestic settings. This study examines a robotic arm that uses accelerometers as sensors to collect data on natural arm motions to mimic those of a human arm. This method of control allows greater flexibility in controlling the robotic arm rather than using a controller where each actuator is controlled separately. The processing unit takes care of each actuator’s control signal according to the inputs from an accelerometer, to replicate the movements of the human arm. Robotic arms are programmed manipulators that have a striking resemblance to a human arm in terms of design. Joints connect the various parts of the arm. Robotic arm joints, in contrast to the rotating joints seen in human arms, may be capable of varying the link length (prismatic joints). The term

Adaptive Fuzzy Robust Tracking Control Using Human Electromyogram Signals for Elastic Joint Robots

Sliding mode control is often used for systems with parametric uncertainties due to its desirable robustness and stability, but this approach carries undesirable chattering. Similarly, joint elasticity is a common phenomenon induced by transmission systems in robots, but it presents additional complexity in robot dynamics that could lead to robot vibrations or even instability. Coupling these two phenomena presents further compounded challenges, particularly when faced with the human interface's added uncertainties. Here, a stable voltage-based adaptive fuzzy strategy to sliding mode control is proposed for an elastic joint robot arm that uses a human's upper limb electromyogram (EMG) signals to direct its movement. The concurrent use of EMG with the elastic joint arm provides a suitable framework for human-robot interaction. EMG signals represent human's ‘intention’ on motion, i.e., they move between 50–100 ms before the mechanical motion begins. Hence this strategy potentially builds better synchronization between the robot and human intention. Furthermore, the adaptive fuzzy strategy eliminates the system chattering while also providing robustness against parametric uncertainties and time delay. Lyapunov analysis also shows bounded-input bounded-output stability of the closed-loop system. Finally, the proposed approach is experimentally implemented in the Sport Science Research Institute. Comparisons with a competing strategy, as well as a torque mode controller, shows that the proposed approach leads to a computationally faster and more accurate controller.