Robot Control Research Articles

External Steering of Vine Robots via Magnetic Actuation.

This article explores the concept of external magnetic control for vine robots to enable their high curvature steering and navigation for use in endoluminal applications. Vine robots, inspired by natural growth and locomotion strategies, present unique shape adaptation capabilities that allow passive deformation around obstacles. However, without additional steering mechanisms, they lack the ability to actively select the desired direction of growth. The principles of magnetically steered growing robots are discussed, and experimental results showcase the effectiveness of the proposed magnetic actuation approach. We present a 25-mm-diameter vine robot with an integrated magnetic tip capsule, including 6 degrees of freedom (DOF) localization system and camera, and demonstrate a minimum bending radius of 3.85 cm with an internal pressure of 30 kPa. Furthermore, we evaluate the robot's ability to form tight curvature through complex navigation tasks, with magnetic actuation allowing for extended free-space navigation without buckling. The suspension of the magnetic tip was also validated using the 6 DOF localization system to ensure that the shear-free nature of vine robots was preserved. Additionally, by exploiting the magnetic wrench at the tip, we showcase preliminary results of vine retraction. The findings contribute to the development of controllable vine robots for endoluminal applications, providing high tip force and shear-free navigation.

Tracking control and neural network disturbance observer for a 6 DoF underwater welding robot

This article addresses the control of an underwater welding robot in 3D space. A control approach combining a finite time sliding mode controller for position and orientation control and feedback linearization for controlling one degree of freedom of the welding arm is adopted. The utilization of underwater robots in marine environments is often hindered by uncertainties and disturbances induced by ocean waves and currents, resulting in decreased accuracy and operational disruptions. To overcome these challenges, a novel observer based on a radial basis function neural network is developed to enhance the performance of the underwater welding robot. The neural network’s weights are optimized using the Lyapunov method within the control law framework. Through simulations, the article evaluates the observer’s efficacy in accurately tracking reference trajectories in the presence of uncertainties. The results underscore the significant contribution of this estimator in mitigating uncertainties and disturbances, thereby substantially improving the overall performance and operational reliability of underwater welding robots. The control strategies and observer design presented in this study pave the way for enhanced accuracy, stability, and efficiency in complex underwater welding operations.

High-performance foot trajectory tracking control of hydraulic legged robots based on fixed-time disturbance observers

High-performance foot trajectory tracking control of hydraulic legged robots based on fixed-time disturbance observers

ORPP—An Ontology for Skill-Based Robotic Process Planning in Agile Manufacturing

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level.

Force-Field based assisted control for upper-limb rehabilitation robots

Robot-assisted training with an assist-as-needed (AAN) control strategy has been used in clinical investigations to effectively enhance the subject’s active effort and promote the rehabilitation of individuals with upper-limb motor impairment. In this paper, a force field-based AAN control scheme is proposed for robot-assisted upper limb rehabilitation training and then implemented in a robot-assisted rehabilitation training system (RaRTS). A spatial varying assistance force field is constructed around the predetermined trajectory in this scheme, and then a varying force field coefficient based on the subject’s motor performance is adopted to deliver the AAN features with temporal freedom. The normal force field, tangential force field, and viscous force field are the three components of the constructed spatial varying assistance force field. Furthermore, the proposed scheme contains three training modes that are switched according to the subject’s trajectory tracking error. Finally, task-oriented continuous training experiments with different force field factors are conducted on able-bodied subjects to evaluate and validate the performance and feasibility of the proposed scheme. Preliminary experimental results and statistics analysis suggest that the subjects were able to keep much closer to the predetermined trajectory through an appropriate selection of force-field assistance factors. Furthermore, the robot would apply an assistance force as needed as the motor performance deteriorate, which could prevent the subject from losing confidence due to poor performance.

Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control.

The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.

Evaluation on Numerical Method of Differential Formula Based on Optimal Control of Space Robot's Attitude and Motion

Evaluation on Numerical Method of Differential Formula Based on Optimal Control of Space Robot's Attitude and Motion

Realizing full-body control of humanoid robots

Realizing full-body control of humanoid robots

Research on shared control of robots based on hybrid brain-computer interface

BackgroundWith the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain–computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control. New methodsThis paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion. ResultsEight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50 %, improves the success rate of traversing obstacles by 25.00 %, and the navigation trajectory is more consistent with expectations. ConclusionsThe HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.

Model-less optimal visual control of tendon-driven continuum robots using recurrent neural network-based neurodynamic optimization

Tendon-driven continuum robots (TDCRs) have infinite degrees of freedom and high flexibility, posing challenges for accurate modeling and autonomous control, especially in confined environments. This paper presents a model-less optimal visual control (MLOVC) method using neurodynamic optimization to enable autonomous target tracking of TDCRs in confined environments. The TDCR’s kinematics are estimated online from sensory data, establishing a connection between the actuator input and visual features. An optimal visual servoing method based on quadratic programming (QP) is developed to ensure precise target tracking without violating the robot’s physical constraints. An inverse-free recurrent neural network (RNN)-based neurodynamic optimization method is designed to solve the complex QP problem. Comparative simulations and experiments demonstrate that the proposed method outperforms existing methods in target tracking accuracy and computational efficiency. The RNN-based controller successfully achieves target tracking within constraints in confined environments.

6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards. We developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs). The results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics. This work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

Robot Control Platform for Multimodal Interactions with Humans Based on ChatGPT

This paper presents the architecture of a multimodal human–robot interaction control platform that leverages the advanced language capabilities of ChatGPT to facilitate more natural and engaging conversations between humans and robots. Implemented on the Pepper humanoid robot, the platform aims to enhance communication by providing a richer and more intuitive interface. The motivation behind this study is to enhance robot performance in human interaction through cutting-edge natural language processing technology, thereby improving public attitudes toward robots, fostering the development and application of robotic technology, and reducing the negative attitudes often associated with human–robot interactions. To validate the system, we conducted experiments measuring negative attitude robot scale and their robot anxiety scale scores before and after interacting with the robot. Statistical analysis of the data revealed a significant improvement in the participants’ attitudes and a notable reduction in anxiety following the interaction, indicating that the system holds promise for fostering more positive human–robot relationships.

The Variable Stiffness Treadmill 2: Development and Validation of a Unique Tool to Investigate Locomotion on Compliant Terrains

Abstract Understanding legged locomotion in various environments is valuable for many fields, including robotics, biomechanics, rehabilitation, and motor control. Specifically, investigating legged locomotion in compliant terrains has recently been gaining interest for the robust control of legged robots over natural environments. At the same time, the importance of ground compliance has also been highlighted in poststroke gait rehabilitation. Currently, there are not many ways to investigate walking surfaces of varying stiffness. This article introduces the variable stiffness treadmill (VST) 2, an improvement of the first version of the VST, which was the first treadmill able to vary belt stiffness. In contrast to the VST 1, the device presented in this paper (VST 2) can reduce the stiffness of both belts independently, by generating vertical deflection instead of angular, while increasing the walking surface area from 0.20m2 to 0.74m2. In addition, both treadmill belts are now driven independently, while high-spatial-resolution force sensors under each belt allow for measurement of ground reaction forces and center of pressure. Through validation experiments, the VST 2 displays high accuracy and precision. The VST 2 has a stiffness range of 13kN/m to 1.5MN/m, error of less than 1%, and standard deviations of less than 2.2kN/m, demonstrating its ability to simulate low-stiffness environments reliably. The VST 2 constitutes a drastic improvement of the VST platform, a one-of-its-kind system that can improve our understanding of human and robotic gait while creating new avenues of research on biped locomotion, athletic training, and rehabilitation of gait after injury or disease.

Active constraint control for the surgical robotic platform with concentric connector joints

Robotic minimally invasive surgery (MIS) has changed numerous surgical techniques in the past few years and enhanced their results. Haptic feedback is integrated into robotic surgical systems to restore the surgeon's perception of forces in response to interaction with objects in the surgical environment. The ideal exact emulation of the robot's interaction with its physical environment in free space is a very challenging problem to solve completely. Previously, we introduced the surgical robotic platform (SRP) with a novel concentric connector joint (CCJ). This study aims to develop a haptic control system that integrates an active constraint controller into a surgical robot platform. We have successfully established haptic feedback control for the surgical robot using constraint control and inverse kinematic relationships integrated into the overall positioning structure. A preliminary feasibility study, modelling, and simulation were presented.

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Disturbance compensation based robust backstepping control for 2-DOF electro-hydraulic tunneling robot

Disturbance compensation based robust backstepping control for 2-DOF electro-hydraulic tunneling robot

Predefined-time sliding mode position and attitude coupling control for in-cabin robots on space stations

As space science advances, the significance of space robotics research escalates, particularly in control methodologies. Addressing the work efficiency requirements during the mission of the in-cabin robot, it is necessary to develop a fast and robust position and attitude coupling controller for in-cabin robots. This paper presents a predefined-time sliding mode position and attitude coupling control method, employing the SE(3) frame description. The first, the dynamic model of the in-cabin robot is developed within the SE(3) framework. Then, the predefined-time position and attitude coupling sliding mode controller is designed with a sliding surface based on the dynamic error model, and a hyperbolic tangent function is used to suppress system chattering. In addition, the stability of the proposed control method is rigorously confirmed using the Lyapunov theorem, ensuring that the convergence time is accurately predefined. Finally, simulation results are provided to demonstrate the effectiveness of this novel control strategy. A comparative analysis with fixed-time sliding mode control highlights its superior performance.

Optimal Moving-Target Circumnavigation Control of Multiple Wheeled Mobile Robots Based on Adaptive Dynamic Programming

Optimal Moving-Target Circumnavigation Control of Multiple Wheeled Mobile Robots Based on Adaptive Dynamic Programming

Embedded ML-based locomotion control for a 12-joint four-legged robot

Reinforcement learning has developed as a promising approach for robot locomotion control, which can save engineering effort compared to conventional approaches. This article presents the implementation of Reinforcement learning on a low-cost, 12 degree of freedom robot known as a quadruped (spider) to optimize locomotion control, enabling the robot to move on different surfaces such as flat surfaces, ramps, speed bumps, and rough terrain. A MATLAB Simulink model is developed as a digital twin of the spider robot. The dynamics of the model were studied and validated using an open-loop algorithm. Then, the model is utilized in a training simulation environment to apply the reinforcement learning algorithm, showing its ability to move along a predefined path as a replacement for conventional motion control systems. Moreover, the work compares the performance and effectiveness of machine learning-based locomotion control with traditional motion control systems regarding navigation accuracy, speed, and adaptability in challenging environments. The minimum hardware requirements are also studied to move the experiment from simulation to reality.

Adaptive Abrupt Disturbance Rejection Tracking Control for Wheeled Mobile Robots

Adaptive Abrupt Disturbance Rejection Tracking Control for Wheeled Mobile Robots