Path Planning for Robotic Arms Based on an Improved RRT Algorithm

The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.

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.

Iterative sliding mode control strategy of robotic arm based on fractional calculus

In order to improve the control performance of industrial robotic arms,an efficient fractional-order iterative sliding mode control method is proposed by combining fractional calculus theory with iterative learning control and sliding mode control.In the design process of the controller,fractional approaching law and fractional sliding mode control theories are used to introduce fractional calculus into iterative sliding mode control,and Lyapunov theory is used to analyze the system stability.Then taking a two-joint robotic arm as an example,the proposed control strategy is verified by MATLAB simulation.The simulation experiments show that the fractional-order iterative sliding mode control strategy can effectively improve the tracking speed and tracking accuracy of the joint,reduce the tracking error,have strong robustness and effectively suppress the chattering phenomenon of sliding mode control.

A DoS Attacks Detection Aglorithm Based on Snort-BASE for Robotic Arm Control Systems

In response to the frequent safety accidents of industrial robots, this paper designs and implements a safety detection system for robot control. It can perform real-time security detection of robot operations on industrial production lines to improve the security and reliability of robot control systems. This paper designs and implements a robot control system based Snort-BASE for real-time online detection of DoS attacks. The system uses a six-degree-of-freedom robotic arm as an example, uses Snort to record the network communication data of the robot arm control system in real time, and filters the network traffic through self-defined rules, and then uses the BASE analysis platform to achieve security analysis of the network traffic. The solution verifies the effectiveness of online real-time detection of attacks and visualisation of attack records by designing simulated robotic arm and real robotic arm attack experiments respectively, thus achieving the security of network communication of the robot remote control system.

Planning Movement of a Robotic Arm for Assembly of Products

This paper presents an algorithmic proposal of a movements＇ planner for a robotic manipulator. Its application is the planning of trajectories from wooden cuttings in a band saw. This algorithm is based on heuristics. A simulator was developed in order to allow the user to define the table＇s shape and figure that you want to trim. The simulator was developed in Java from Sun Microsystems, and next it will be applied in TM Cyberbotics Webots.

Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm

The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual links, following a fixed(pre-defined) order of link selection. The right(left)hand motor imagery is used to turn a link clockwise(counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels: clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and4.6% respectively.

Bio-inspired Design and Inverse Kinematics Solution of an Omnidirectional Humanoid Robotic Arm with Geometric and Load Capacity Constraints

Inspired by the driving muscles of the human arm,a 4-Degree of Freedom(DOF)concentrated driving humanoid robotic arm is proposed based on a spatial double parallel four-bar mechanism.The four-bar mechanism design reduces the inertia of the elbow-driving unit and the torque by 76.65%and 57.81%,respectively.Mimicking the human pose regulation strategy that the human arm picks up a heavy object by adjusting its posture naturally without complicated control,the robotic arm features an integrated position-level closed-form inverse solution method considering both geometric and load capacity limitations.This method consists of a geometric constraint model incorporating the arm angle(φ)and the Global Configuration(GC)to avoid joint limits and singularities,and a load capacity model to constrain the feasible domain of the arm angle.Further,trajectory tracking simulations and experiments are conducted to validate the feasibility of the proposed inverse solution method.The simulated maximum output torque,maximum output power and total energy consumption of the robotic arm are reduced by up to 2.0%,13.3%,and 33.3%,respectively.The experimental results demonstrate that the robotic arm can bear heavy loads in a human-like posture,effectively reducing the maximum output torque and energy consumption of the robotic arm by 1.83%and 5.03%,respectively,while avoiding joints beyond geometric and load capacity limitations.The proposed design provides a high payload–weight ratio and an efficient pose control solution for robotic arms,which can potentially broaden the application spectrum of humanoid robots.

Pneumatically Actuated Soft Robotic Arm for Adaptable Grasping

Developing robotic manipulators capable of performing effective physical interac- tion tasks is a challenging topic. In this study, we design a soft robotic arm （SRA） with multiple degrees of freedom inspired by the flexible structures and the unique motion mechanism of the octopus arm. The SRA is fabricated with elastomeric materials, which consists of four series of integrated pneumatic chambers that play similar roles as the muscles in the octopus arm can achieve large bending in various directions with variable stiffness. This SRA displays specified movements via controlling pressure and selecting channels. Moreover, utilizing parallel control, the SRA demonstrates complicated three-dimensional motions. The force response and motion of the SRA are determined both experimentally and computationally. The applications of the present SRA include tightly coiling around the objects because of its large bending deformation （nearly 360°）, grasping multiple objects, and adjusting the grabbing mode in accordance with the shape of objects.

A Bio-inspired Soft Robotic Arm： Kinematic Modeling and Hydrodynamic Experiments

Soft robotics has several promising properties for aquatic applications, such as safe interaction with environments, lightweight, low cost, etc. In this paper, we proposed the kinematic modeling and hydrodynamics experiments of a soft robotic arm with 3D locomotion capacity. We developed a mathematical model that incorporates the angle correction, as well as the open-loop model-based motion control. The model could precisely predict the three-dimensional （3D） movement, and the location error is less than 5.7 mm in different attitudes. Furthermore, we performed the hydrodynamic investigations and simultaneously measured the hydrodynamic forces and the wake flows at different amplitudes （50 mm, 100 mm, 150 mm, 200 mm） and frequencies （0.3 Hz, 0.4 Hz, 0.5 Hz） of the soft arm. Surprisingly, we found that the magnitudes of the hydrodynamic force （〈1 N） and the torques （〈0.08 N-m） of dynamically moving soft arm were tiny, which leads to negligible inertial effect for the underwater vehicle than those of the traditional rigid underwater manipulator. Finally, we demonstrated underwater picking and placing tasks of the soft manipulator by using a computer program that controls the tip attitude and velocity. This study may inspire future underwater manipulators that have properties of low-inertial, low power cost and can safely interact with the aauatic environments.

Design and performance of a robotic arm for farm use

Smart technology which is the backbone of high-efficiency production opens a new horizon in sustainable agriculture.Nowadays,harvesting the heavy-weight crops is considered an arduous job,specifically in Japan which has faced a serious labor shortage in agricultural fields.In this study,a development procedure and evaluation of a 4-degrees-of-freedom articulated robotic arm is presented,and it provides an appropriate infrastructure to develop a smart harvesting robotic system for heavy-weight crops such as pumpkin,watermelon,melon,and cabbage.This robotic arm designed as an actuating unit of a robot tractor for the agricultural outdoor environment.In this regard,different degree of freedom was evaluated under consideration of economic and technical indexes to find an optimized mechanism.The controlling algorithm of the system was developed by consideration of kinematic and dynamic aspects of the real-world condition.A special harvesting methodology was developed based on optimum harvesting conditions.A controlling unit was developed by using PLC system.Experimental performance,accuracy,payload per weight,and repeatability of the system were measured.The payload per weight,overall average accuracy,and overall average repeatability of the robot were 0.21,1.85 mm,and±0.51 mm,respectively.The results indicated that the developed system had a front access,harvesting length,and workspace volume of 2.024 m,1.36 m,and 8.27 m^(3),respectively.One of the significant advantages of the proposed robotic arm is its capability to use in different industries with minimum modifications.

Robotic arm-assisted arthroplasty:The latest developments

Joint arthroplasty is an effective method for treating end-stage joint lesions and damages.Robotic arm-assisted arthroplasty,a rapidly developing technology that combines navigation technology,minimally invasive technology,and precise control technology of the robotic arm,can achieve accurate preoperative planning,optimal selection of implants,minimally invasive surgery,precise osteotomy,and accurate placement of the artificial joint.It has the characteristics of high accuracy and stability,and thus is more and more widely used in the field of joint surgery.In this paper,we systematically reviewed the application and clinical efficacy of robotic arm-assisted technology in hip and knee arthroplasty to provide reference for its future promotion.

Kinematic modeling and control of a novel pneumatic soft robotic arm

A novel soft robotic arm(SRA)composed of two soft extensible arms(SEAs)and a soft bendable joint(SBJ)for space capture systems is presented in this paper.A diamond origami pattern was applied in the design of the SEAs,and large deformations of the SEAs in positive pressure were simulated using the nonlinear finite element method.A kinematic model of the SRA using the Denavit–Hartenberg method based on the assumption of constant curvatures was proposed.A closed-loop model-free control system based on a PID controller was developed using real-time data from a vision sensor system.The kinematic model and closed-loop model-free control system are experimentally evaluated on an SRA prototype by four experiments.The experimental results demonstrate that the derived kinematic model can finely describe the movement of the SRA and that the closed-loop control system can control the SRA to reach the desired destination or trajectory within an acceptable error and performs well in long-term repeated operations.

Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment perception.However,traditional calibrations suffer inaccuracy and inconsistency.To address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying framework.In our method,the spatial relationship is geometrically correlated between the sensing units and robotic arm.The decoupled estimations on rotation and translation could reduce the coupled errors during the optimization.Additionally,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and consistently.The calibration has been evaluated on our developed platform.In the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,respectively.The comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

Development of an Anthropomorphic Robotic Arm and Hand for Interactive Humanoids

Humanoid robots are designed and built to mimic human form and movement. Ultimately, they are meant to resemble the size and physical abilities of a human in order to function in human-oriented environments and to work autonomously but to pose no physical threat to humans. Here, a humanoid robot that resembles a human in appearance and movement is built using powerful actuators paired with gear trains, joint mechanisms, and motor drivers that are all encased in a package no larger than that of the human physique. In this paper, we propose the construction of a humanoid-applicable anthropomorphic 7-DoF arm complete with an 8-DoF hand. The novel mechanical design of this humanoid ann makes it sufficiently compact to be compatible with currently available narrating-model humanoids, and to be sufficiently powerful and flexible to be functional; the number of degrees of freedom endowed in this robotic arm is sufficient for executing a wide range of tasks, including dexterous hand movements. The developed humanoid arm and hand are capable of sensing and interpreting incoming external force using the motor in each joint current without conventional torque sensors. The humanoid ann adopts an algorithm to avoid obstacles and the dexterous hand is capable of grasping objects. The developed robotic ann is suitable for use in an interactive humanoid robot.

Robotic Smart Prosthesis Arm with BCI and Kansei/Kawaii/Affective Engineering Approach.Pt I: Quantum Soft Computing Supremacy

A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given.As a result,a prototype of man-made smart prosthesis on a 3D printer as well as a foundation for computational intelligence presented.The application of soft computing technology(the first step of IT)allows to extract knowledge directly from the physical signal of the electroencephalogram,as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state.The possibilities of applying quantum soft computing technologies(the second step of IT)in the processes of robust filtering of electroencephalogram signals for the formation of mental commands of robotic prosthetic arm discussed.Quantum supremacy benchmark of intelligent control simulation demonstrated.

Intelligent Garbage Recycling: Design and Implementation Exploration of Automatic Classification System

This paper introduces an intelligent waste recycling automatic classification system,which integrates sensors,image recognition,and robotic arms to achieve automatic identification and classification of waste.The system monitors the composition and properties of waste in real time through sensors,and uses image recognition technology for precise classification,and the robotic arm is responsible for grabbing and disposing.The design and implementation of the system have important practical significance and application value,and help promote the popularization and standardization of waste classification.This paper details the system s architecture,module division,sensors and recognition technology,robotic arm and grabbing technology,data processing and control system,and testing and optimization process.Experimental results show that the system has efficient waste recycling efficiency and accuracy in practical applications,bringing new development opportunities to the waste recycling industry.

ADAPTIVE FUZZY CONTROL FOR ROBOT ARM MANIPULATOR WITH 5-DOF

To control the robot and track the designed trajectory with uncertain disturbances in a specified precision range, an adaptive fuzzy control scheme for the robot arm manipulator is discussed. The controller output error method （COEM） is used to design the adaptive fuzzy controller. A few or all of the parameters of the controller are adjusted by using the gradient descent algorithm to minimize the output error. COEM is adopted in the adaptive control system for the robot arm manipulator with 5-DOF. Simulation results show the effectiveness of the method and the real time adjustment of the parameters.

Dynamic Coordination of Uncalibrated Hand/Eye Robotic System Based on Neural Network

A nonlinear visual mapping model is presented to replace the image Jacobian relation for uncalibrated hand/eye coordination. A new visual tracking controller based on artificial neural network is designed. Simulation results show that this method can drive the static tracking error to zero quickly and keep good robustness and adaptability at the same time. In addition, the algorithm is very easy to be implemented with low computational complexity.

Error Modeling and Sensitivity Analysis of a Parallel Robot with SCARA(Selective Compliance Assembly Robot Arm) Motions

Parallel robots with SCARA（selective compliance assembly robot arm） motions are utilized widely in the field of high speed pick-and-place manipulation. Error modeling for these robots generally simplifies the parallelogram structures included by the robots as a link. As the established error model fails to reflect the error feature of the parallelogram structures, the effect of accuracy design and kinematic calibration based on the error model come to be undermined. An error modeling methodology is proposed to establish an error model of parallel robots with parallelogram structures. The error model can embody the geometric errors of all joints, including the joints of parallelogram structures. Thus it can contain more exhaustively the factors that reduce the accuracy of the robot. Based on the error model and some sensitivity indices defined in the sense of statistics, sensitivity analysis is carried out. Accordingly, some atlases are depicted to express each geometric error’s influence on the moving platform’s pose errors. From these atlases, the geometric errors that have greater impact on the accuracy of the moving platform are identified, and some sensitive areas where the pose errors of the moving platform are extremely sensitive to the geometric errors are also figured out. By taking into account the error factors which are generally neglected in all existing modeling methods, the proposed modeling method can thoroughly disclose the process of error transmission and enhance the efficacy of accuracy design and calibration.

A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm

Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.