Robot Arm Research Articles

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.

Chain-based lattice printing for efficient robotically-assembled structures

Due to the nature of their implementation, nearly all low-level fabrication processes produce solidly filled structures. However, lattice structures are significantly stronger for the same amount of material, resulting in structures that are much lighter and more materially efficient. Here we propose an approach for fabricating lattice structures that echoes 3D printing techniques. In it, a modular chain of specially designed links is “extruded” onto a substrate to produce various lattices configurations depending on the chosen assembly algorithm, ranging from rigid regular lattices with nodal connectivity of 12, octet-truss, to significantly less dense configurations. Compared to conventional additive manufacturing methods, our approach allows for efficient use of nearly any material or combination of materials to construct lattices with programmed arrangements. We experimentally demonstrate that a 3x3x2 lattice structure (287 total links) is fabricated in 27 minutes via a modified robotic arm and can support approximately 1000 N in compression testing.

Motion controller for multi-joint robotic arm with deep cascade gated Bayesian broad learning system

Intelligent controllers based on the broad learning system can simplify the process of model parameter adjustment, finding wide applications in the motion control of multi-joint robotic arms. However, motion controllers for multi-joint robotic arms based on broad learning system exhibit insufficient precision and overlook the impact of joint motion commonalities on controller design. Therefore, this paper proposes a novel motion control strategy for a multi-joint robotic arm based on a deep cascade feature-enhancement gated Bayesian broad learning system. Firstly, the motion controller of the deep cascade feature-enhancement Bayesian broad learning system is constructed to enhance the robotic arm motion control precision. Secondly, an incremental node generation module with an attention-gated mechanism is constructed to capture the unique motion characteristics of the target joints, which is further combined with model generalization to simplify the motion control process of the multi-joint robotic arm. Finally, controller convergence is enhanced by combining it with the Lyapunov theory to constrain the learning parameters. Simulations and physical experiments are designed to verify the feasibility and superiority of the proposed motion control strategy. The results demonstrated that the strategy improved the accuracy of robotic arm motion control, with the root mean square error in position tracking reduced to 0.0019 rad. This represents a 93.39% reduction in error compared to existing techniques.

Design and Control Co-Optimization for Dynamic Loco-Manipulation With a Robotic Arm on a Quadruped Robot

Abstract Research in quadrupedal robotics is transitioning to studies into loco-manipulation, featuring fully articulated robotic arms mounted atop these robots. Integrating such arms enhances the practical utility of legged robots, paving the way for expanded applications like industrial inspection and search and rescue. Existing literature commonly employs a six-degree-of-freedom (six-DoF) arm directly mounted to the robot, which inherently adds significant weight and reduces the available payload for manipulation tasks. Our study explores an optimized combination of arm configuration and control framework by strategically reducing the DoFs and leveraging the quadruped robot’s inherent agile mobility. We demonstrate that by minimizing the DoFs to just one, a range of canonical loco-manipulation tasks can still be accomplished. Some tasks even show improved performance with fewer robotic arm DoFs due to the higher torque motor used in the design, allowing more of the robot’s payload to be used for manipulation. We designed our optimized one-DoF robotic arm and the control framework and tested it on top of a Unitree Aliengo. Our design outperforms conventional six-DoF counterparts in lifting capacity, achieving an impressive 8 kg payload compared to the 2 kg maximum payload of industry-standard six-DoF robotic arms on the same quadruped platform.

Reproduction and generalization of robot trajectory tracking control method using reinforcement learning and neural network techniques

Abstract. This paper introduces a method for controlling the trajectory of a manipulator robot with uncertainty, using reinforcement learning. The control is designed to work even when there are limitations on the inputs to the system. Reinforcement learning and neural networks are employed alongside standard robust control techniques to enhance the fixed-time convergence of the system state. A novel algorithm is proposed to develop a reinforcement learning-based approach. This approach utilizes radial basis function neural networks and nonsingular fast terminal sliding mode control to ensure error convergence within a predetermined time. This paper presents the task of monitoring the intended path followed by robotic arms in the presence of uncertain and unfamiliar disruptions. The experimental results validate that the suggested approach substantially improves both the stability and accuracy of trajectory tracking, making it more feasible for real-world applications in robotic systems.

Effects of control parameters of wearable robotics on muscle activity during assisted elbow flexion

One way to provide assistance in a dynamic lifting task is to pre-emptively move the exoskeleton based on a predicted reference trajectory. However, the level of aggressiveness in the prediction (i.e., how far ahead in time) and the exoskeleton's degree of adherence to the reference trajectory (stiffness) are not yet fully understood. This study investigated the effects of stiffness and pre-emptive offset parameters in an impedance-controlled robotic arm on muscle activation and perceived exertion of the user. Thirteen participants were instructed to lift a load equivalent to 15% of their maximal voluntary contracted force in collaboration with a robotic arm with 40°–135° of elbow flexion in 1.12 s. Three levels of stiffness (lower: 0.1 N m deg−1, medium: 0.2 N m deg−1, and higher: 0.31 N m deg−1) and two levels of pre-emptive offsets (shorter: 0.1 s and longer: 0.4 s) were investigated. We found that (1) during 0–0.5 s (acceleration stage) of elbow flexion, a higher stiffness level and a longer pre-emptive offset decreased muscle activity; (2) during 0.5–1 s (deceleration stage) of elbow flexion, medium and higher stiffness with a shorter pre-emptive offset decreased muscle activity; (3) the perceived exertion and assistance of participants were improved with a higher stiffness and a longer pre-emptive offset, whereas cooperation was rated higher at a shorter pre-emptive offset under higher stiffness. This study reveals that the optimal parameters for stiffness and pre-emptive offsets for predictive impedance controls are different for different stages of elbow flexion.

Visual localization of robotic end effector via fusion of 3D Gaussian Splatting and heuristic optimization algorithm

The visual localization of the robotic end effector holds significant importance and value in intelligent manufacturing. Regarding this issue, a pose estimation framework integrating the radiance field rendering and an improved differential evolution algorithm is proposed in this paper. Firstly, multi-view images of the object are collected by an industrial camera carried on the robotic arm. Then, a 3D Gaussian Splatting model is trained, and the observation trajectory of the virtual camera is designed based on the query image in Nerfstudio for visualization. The key frames and the corresponding pose matrices of the rendered videos along the trajectory are extracted. Subsequently, the improved differential evolution algorithm through double-layer optimization proposed in this paper is employed to conduct similarity matching between the query image and the extracted rendered images. The pose matrix corresponding to the most similar rendered image is the estimated pose of the query image. Through experiments, this paper explores the influence of the frame sampling rate on the pose estimation accuracy, and conducts comparative and ablation experiments with the commonly used methods at present. The experimental results demonstrate that the proposed method possesses good feasibility and outstanding performance.

Automated self-adjustment of array probe with a robotic ultrasonic test system

Ultrasonic testing of objects with complex geometries often requires the use of a robotic arm to position the probe perpendicular to the local surface. Using immersion makes it possible to test these objects with standard ultrasonic linear array probes. Here, the probe positions and orientations provided by the robot are used for merging the locally acquired image data into a 3D-reconstruction. The quality of this reconstruction is highly dependent on the alignment of the position of the physical probe with the position used in the digital model. For common industrial tools, the tool center point (TCP) is usually acquired using geometric features of the tools. However, for ultrasonic arrays in immersion, there is a water standoff between the probe and the test object, therefore the TCP is in free space in front of the array and cannot be acquired with the common method. To overcome this challenge, we propose a method that allows the robotic ultrasonic system to automatically self-adjust the position and orientation of the ultrasonic probe using a test block made of steel with defined geometric features as a target for referencing. For each of the six degrees of freedom, a scan and adjustment routine are established using the data based on the actual ultrasound characteristics of the probe. Given a coarse pre-definition of the tool position and the known target test block, minimal human interaction is required to supervise the adjustment method, leading to higher quality reconstructions than with manual adjustment.

Optimized Kalman filtering in dynamical environments for thumb robot motion estimation

<span>Stroke, a prevalent nerve disorder in Indonesia, necessitates post-stroke rehabilitation like physical and occupational therapy. Hand and finger muscle training, crucial for restoring movement, often involves innovative solutions like finger prosthetic robotics arms. In particular, the advancement in thumb robotics emphasizes the estimation of thumb motion, where the ensemble Kalman filter square root (EnKF-SR) and H-infinity methods are deemed dependable for both linear and nonlinear models. Simulation results, using 400 ensembles, demonstrated nearly identical accuracy between the methods, exceeding 99%, with a 6-7% increase in accuracy compared to 200 ensembles. These advancements offer promising prospects for effective post-stroke rehabilitation and improved thumb movement restoration.</span>

Robust Control of Chat-PM: A Switched Singular System with Mode-dependent Bounded Nonlinearity

This paper is concerned with the robust control of the composite hybrid aerial-terrestrial precise manipulator (Chat-PM), a quadrotor-based robot with aerial/terrestrial movement and payload transportation capabilities. Given the distinct dynamics between aerial and terrestrial locomotion modes, Chat-PM is modeled as a type of switched singular system. A nonlinear term is contained in the system to model the coupling between translational and rotational movements of Chat-PM, which is proven to be norm-bounded and mode-dependent. Besides, the estimation error of the robotic arm's interaction force/torque is treated as the disturbance of the system. By means of the mixed H2/H∞ approach, numerically testable stability criterion are obtained, based on which the existence conditions for controllers with satisfactory transient and disturbance attenuation performance are provided. Compared to traditional studies assuming the nonlinearity term to be mode-independent, the conservatism in the controller design is reduced. Experimental results are provided to demonstrate the effectiveness of the proposed approach.

Handx Robotic Arm Assisted vNOTES Hysterectomy With Contained Transvaginal Specimen Retrieval

Handx Robotic Arm Assisted vNOTES Hysterectomy With Contained Transvaginal Specimen Retrieval

Design, Optimization, and Autonomous Construction of Martian Infrastructure including Slabs Using In-situ CO2-Based Polyethylene

A crucial development in 21st-century space exploration is the establishment of human settlements on Mars. In recent years, Mars exploration rovers have been deployed, and studies investigating human journeys to Mars have been conducted, with the objective of a human-crewed mission to Mars before 2050. This study presents an innovative approach to designing and optimizing Martian habitats by using in-situ CO2-based polyethylene (PE) as a construction material for Martian buildings and greenhouses. It seeks to develop sustainable and resilient habitats on Mars, leveraging local resources to address the environmental challenges specific to the Martian environment. The research targets several key areas: realistic construction techniques, effective utilization of in-situ materials, and building resilience under the harsh Martian conditions. These conditions include radiation, micrometeoroid impacts, low atmospheric pressure, low gravity, wind storms, temperature extremes, Paschen discharge, and marsquakes. Finite element analysis (FEA) of several pressurized spherical structures is used to design and optimize for minimum material usage, inspired by soap bubbles that naturally stick together with separating walls. The proposed habitat design features a central spherical structure surrounded by nine smaller spheres, optimized for material efficiency. It is demonstrated that utilizing two layers of 0.1 m thick PE and a 0.5 m thick CO2 layer in between them offers significant protection against Martian environmental loads such as radiation shielding and thermal insulation. It facilitates light transmission into the building. This study offers a realistic structural system for buildings and greenhouses that can be constructed using in-situ materials and is suitable for an optimal 539-day stay on Mars. The proposed autonomous robotic arm technology can perform all necessary tasks, from producing PE to automatically assembling the Mars Spherical Building (MSB), including in-place slab printing.

Robotic Dexterous Hands’ Structure Design and Drive-Transmission System

The robotic dexterous hand is one of the most important components of robots. As a new type of end-effector, it can help the robot to interact with the environment, enabling the function of grasping and dexterous operation to objects in various situations such as daily life, medical treatment and industry. This paper investigates the development history and current situation of the robotic dexterous hand in the world, conducts the research and comparative analysis focusing on the characteristics of the existing robotic dexterous hands’ mechanical transmission and drive system, comprehensively obtains its development trend, and finally draws the conclusion and makes the prospect of the future development of the robotic dexterous hand.

Control of Multi-Robot Arms in Object Retrieval Based on Human-Machine Interface

Multi-robot arm control is the result of the development of advanced robotics technology. With the advancement in the field of human-machine interface, controlling robot arms has become more efficient and can be done intuitively by humans. In this study, we designed three robot arms of 4-DoF, each of which is controlled by a computer in a visual program interface. This research aims to develop a human-machine interface-based multi-robot arm control system that allows humans to interact with the robots directly. The movement method of each robot uses Trajectory Planning, which works when the operator gives motion commands through the interface display. Multi-robot communication with a computer using USB hub serial format. The computer is the master, and each Arduino on the robot is the enslaved. Three robot arms have been tested and controlled by the HMI computer, and all of them move according to the commands of the operator. The time required by each robot to complete its mission is ± 10 seconds. The results of this study are expected to open new opportunities in the application of robotics in various fields, such as the manufacturing industry, health services, and transportation.

A low-cost prosthetic arm with real-time haptic feedback and gesture control for enhanced user autonomy

In daily life, manipulating and feeling objects is essential for autonomy and engagement in various activities. However, individuals relying on prosthetic limbs often encounter barriers such as high costs, ineffective feedback, and limited design adaptability. Unlike conventional prosthetic limbs, which frequently struggle to integrate seamlessly and provide intuitive control, the robotic arm outlined in this study represents a leap in functionality and user experience. At the core of its design lies a specialized glove outfitted with a calibrated array of flex-sensing resistors capable of precisely detecting and interpreting the wearer's movements. These sensors input to the robotic arm, translating the user's gestures into motions that follow human hands. The fingertips are equipped with force-sensing resistors to discern the magnitude of force applied during gripping actions. This real-time sensory feedback is conveyed to the user through a haptic system integrated into the glove. By modulating the intensity and pattern of vibrations, users can gauge their grip strength and manipulate their grasp of the object accordingly. Comprehensive experiments were conducted across various scenarios to assess its performance, agility, strength, and sensory accuracy. Through refinement, the author aims to optimize functionality and reliability, enhancing practicality for users. This project has broader implications for the prosthetics community. By utilizing the scalability and cost-effectiveness of this model, the author envisions tailored solutions that empower individuals with limb differences to live more independently.

Design and development of machine vision robotic arm for vegetable crops in hydroponics

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.

A new hybrid robotic surgery by minimally invasive laparoscopic and robotic (MILAR) system using da Vinci single-port (SP) in distal gastrectomy for gastric cancer.

The da Vinci SP (SP) has only 1 robotic arm, containing 4 channels. It provides a wide space for assistant surgeons using laparoscopic forceps. We performed a new type of minimally invasive surgery using laparoscopy and a robot (MILAR) utilizing the SP in distal gastrectomy for gastric cancer. An incision at the umbilicus was made for the SP, and 2 ports were inserted from the right abdomen for laparoscopic surgery. In the above view, where surgeons look down at the surgical field, the scope was inserted from the channel at 12 o'clock, and the round tooth retractor was inserted from 6 o'clock. In the below view, where surgeons look up at the surgical field, the scope and retractor were inserted from opposite directions. The robotic operator uses 3 forceps and a scope. The laparoscopic operator uses 2 forceps. On the greater curvature side, the left or right epiploic artery pedicles was retracted by the robot operator under the below view. The suprapancreatic area was dissected with the pedicle of the left gastric artery retracted by the laparoscopic operator under the above view. By setting the scope and the retractor in a diagonal direction of 12-6 o'clock, robotic surgeons have a wide space bilaterally for using forceps for nodal dissection under the co-axial setting. Laparoscopic surgeons can use this space easily. Gastrectomy by MILAR using SP could provide quality surgery within a short operative time.

Modeling Backlash-like Hysteresis and Feedforward Control of Tendon-Sheath Mechanism Pair with Arbitrary Shape: Excluding Distal Sensory Input

Abstract The tendon-sheath mechanism (TSM) has garnered attention for its versatile applications in soft robotics, robotic hands, and surgical robots due to its adeptness in transmitting force through tortuous and narrow channels. However, precise control of the TSM is challenging due to its shape-dependent deformation and force generation, highlighting the need to address shape variations in dynamic environments where direct measurement is impractical. This paper introduces a novel methodology leveraging the concept of the equivalent circle in TSM to model its shape under varying conditions. The equivalent circle substitutes an arbitrarily shaped TSM with a circle, whose radius is derived from TSM's deformation and forces measured at the proximal end during calibration. This approach provides a concise and effective framework for understanding and analyzing the distinct characteristics of TSM. Additionally, a control strategy is proposed, utilizing the estimated equivalent circle to adapt to dynamic environments. Experimental results demonstrate promising performance without distal end sensory feedback, with mean percentage errors (MPE) of 0.78% and 2.33% for predicting cumulated curve angle and equivalent circle radius, respectively. Moreover, by utilizing the equivalent circle to calculate and feedforward deadband, effective control is achieved. The root mean square error (RMSE) reduced from 1.31 mm to an average of 0.19 mm in two feedforward experiments, representing an average reduction of 85.5%. These findings shed light on effective control strategies for TSM, offering valuable insights for robotics and surgical applications where TSM shape configurations are uncertain and subject to dynamic change.

Rapid Prototyping of Multiscale Flexible Printed Circuit Boards with NeXus, a Robotic Additive Manufacturing System

Abstract This paper demonstrates the NeXus system, a multi-scale robotic additive manufacturing platform developed at the Louisville Automation and Robotics Research Institute (LARRI), as a rapid prototyping tool through additively manufacturing a multi-layer flexible printed circuit board (FPC) with a printed strain sensor and soldered Surface Mounted Devices (SMD). Manufacturing of the demonstrator requires the application and curing of multiple materials with specialized properties, tools for automated assembly, and software advances to streamline the process enabling the use of industry-standardized programs to command the NeXus system. Additive manufacturing processes supported by the NeXus include Aerosol Jet printing (AJP) for fine feature silver conducting lines, direct write ink-jet printing for insulating materials, and Intense Pulsed Light (IPL) for curing materials between depositions. The NeXus system transports and manipulates parts using a 5-DOF high-precision positioner. Solder paste deposition and pick-and-place (PnP) procedures are performed by a 4-DOF Selective Compliance Articulated Robot Arm (SCARA). Conversion methods between traditional PCB design software and production-ready command scripts were developed to translate basic drawings into command scripts. Multilayer structures with AJP 50-micron wide lines, an insulating bridge with a thickness of around 100 microns, and SMDs soldered to silver AJP pads, were integrated within the demonstrator. An operational amplifier, and other SMDs, reduce the complexity of the accompanying control circuit and amplifies the sensor?s response by 1,830 times. The successful fabrication of the demonstrator FPC highlights the rapid prototyping ability of the NeXus System.

The biohybrid assembly

In the field of contemporary architectural design, novel approaches have emerged to redefine the concept of sustainability through the utilization of bio-composites. One area of exploration involves investigating mycelium as an architectural material across various scales and functions. In this context, the presenting research explores the hybrid assembly combining mycelium and custom 3D printing incorporating robotic fabrication techniques. The Biohybrid Assembly project is a double-layered hybrid structure printed by FGF extruder for the inner layer and a customized mycelium extruder for the outer layer. Furthermore, the Biohybrid Assembly project demonstrates the possibility of large-scale construction, addressing challenges associated with existing mycelium applications. Various types of mycelium-based composites were tested to determine their optimal viscosity, growth, and strength, which were subsequently utilized to fabricate the desired model geometry. A specialized 3D printing extruder was designed and manufactured to nozzle the selected composite. Ultimately, a custom workflow, incorporating an industrial robotic arm, was established to fabricate the hybrid structure consisted of organic and inorganic materials. This research significantly contributes to the redefinition of sustainability by exploring biohybrid construction technology. Through establishing a system for a large-scale prototype, it reinterprets the concept of ecofriendliness in architecture and highlights the advantages and potential of innovative bio-integrated fabrication methods.