Robot Control Algorithms Research Articles

Use of U-sensor for motion safety of a collaborative manipulative robot

One approach to increasing the operational efficiency of robotics is to utilize the capabilities of modern collabora-tive applications of robots. Within the framework of the paper, issues related to the possibility of utilizing the availa-ble sensor base, namely ultrasonic sensors for the sensing of robots for further collaborative use are discussed. A model of a sensing robot is presented and elements of a control algorithm for a collaborative robot are proposed.

High-density EMG, IMU, kinetic, and kinematic open-source data for comprehensive locomotion activities

Novel sensor technology enables new insights in the neuromechanics of human locomotion that were previously not possible. Here, we provide a dataset of high-density surface electromyography (HDsEMG) and high-resolution inertial measurement unit (IMU) signals, along with motion capture and force data for the lower limb of 10 healthy adults during multiple locomotion modes. The participants performed level-ground and slope walking, as well as stairs ascent/descent, side stepping gait, and stand-to-walk and sit-to-stand-to-walk, at multiple walking speeds. These data can be used for the development and validation of locomotion mode recognition and control algorithms for prosthetics, exoskeletons, and bipedal robots, and for motor control investigations.

Event-triggered critic learning impedance control of lower limb exoskeleton robots in interactive environments

In this paper, we present an event-triggered critic learning impedance control algorithm for a lower limb rehabilitation exoskeleton robot in an interactive environment, where the control objective is specified by a desired impedance model. In comparison to many other traditional impedance controller design algorithms, in this paper, the impedance control problem is transformed into an optimal control problem. Firstly, the interactive environment accounts for the interaction between the exoskeleton, the human, and the environment, and is modeled by a linear time-invariant exogenous system. Secondly, in contrast to time-triggered control design mechanisms, the event-triggered controller is updated only when the system states deviate from prescribed threshold values. To obtain the event-triggered optimal controller, a critic neural network is developed through the framework of reinforcement learning. A modified gradient descent method is introduced to update the weights of the critic network with an additional stable term employed to eliminate the need for an initial admissible control. Meanwhile, with the simultaneous application of historical and transient state data to the critic neural network, the persistent excitation conditions are relaxed. The Lyapunov method is used to rigorously demonstrate the stability of the overall system. Finally, the effectiveness of the proposed algorithm is demonstrated via simulation.

Research on Motion Control and Wafer-Centering Algorithm of Wafer-Handling Robot in Semiconductor Manufacturing.

This paper studies the AWC (Active Wafer Centering) algorithm for the movement control and wafer calibration of the handling robot in semiconductor manufacturing to prevent wafer surface contact and contamination during the transfer process. The mechanical and software architecture of the wafer-handling robot is analyzed first, which is followed by a description of the experimental platform for semiconductor manufacturing methods. Secondly, the article utilizes the geometric method to analyze the kinematics of the semiconductor robot, and it decouples the motion control of the robot body from the polar coordinates and joint space. The wafer center position is calibrated using the generalized least-square inverse method for AWC correction. The AWC algorithm is divided into calibration, deviation correction, and retraction detection. These are determined by analyzing the robot's wafer calibration process. In conclusion, the semiconductor robot's motion control and AWC algorithm are verified through experiments for correctness, feasibility, and effectiveness. After the wafer correction, the precision of AWC is <± 0.15 mm, which meets the requirements for transferring robot wafers.

Robotic sensing for buried pipes with sound waves

The length of buried sewer and drainage network in Europe is several million kilometres. Autonomous robots are being developed to inspect this massive network of pipes pervasively. These inspection technologies traditionally rely on CCTV images. However, detection of the condition of buried pipes with autonomous robots is challenging computationally. Acoustic waves provide an efficient alternative to conventional CCTV methods to detect a range of artefacts that can lead to pipe failure and map these conditions. This paper presents an acoustic method for simultaneous condition detection, localization, and classification in air-filled pipes. A microphone array is used to estimate the reflection coefficient from a range of artefacts. This information is used together with a regularization method. A wavelet basis function is adapted to enhance the fidelity of collected acoustic data. It is shown that the wavelet components can also be used to train and to test a support vector machine (SVM) classifier for the condition identification. This work can also inform the route planning and low-level control algorithms for autonomous robots which are being developed for the inspection of buried pipes.

Collaborative obstacle avoidance algorithm and simulation of swarm robots with limited viewing angle

In this paper, the problem of collaborative obstacle avoidance of swarm robots with limited view angles is studied. In the case of limited view angles, the control model of swarm robots system is designed. The swarm robots can safely avoid a single static obstacle when the obstacle is within the robot’s visual angle. The main research result of this paper is to design a collaborative obstacle avoidance control algorithm for swarm robots, and subdivide the overall control algorithm into three sub algorithms: formation, obstacle avoidance and navigation. The condition of limited view angle is added to the collaborative obstacle avoidance control algorithm of swarm robots, and a control model of collaborative obstacle avoidance of swarm robots with limited view angle is constructed. Then based on the Simulink platform, the simulation is carried out in the case of static obstacle to verify the correctness and stability of the algorithm.

Spherical acquisition trajectories for x-ray computed tomography with a robotic sample holder

This work presents methods for the seamless execution of arbitrary spherical trajectories with a seven-degree-of-freedom robotic arm as a sample holder. The sample holder is integrated into an existing x-ray computed tomography setup. We optimized the path planning and robot control algorithms for the seamless execution of spherical trajectories. A precision-manufactured sample holder part is attached to the robotic arm for the calibration procedure. Different designs of this part are tested and compared to each other for optimal coverage of trajectories and reconstruction image quality. We present experimental results with the robotic sample holder where a sample measurement on a spherical trajectory achieves improved reconstruction quality compared to a conventional circular trajectory. Our results demonstrate the superiority of the discussed system as it outperforms single-axis systems by reaching nearly 82% of all possible rotations. The proposed system is a step towards higher image reconstruction quality in flexible x-ray CT systems. It will enable reduced scan times and radiation dose exposure with task-specific trajectories in the future, as it can capture information from various sample angles.

Online Outdoor Terrain Classification Algorithm for Wheeled Mobile Robots Equipped with Inertial and Magnetic Sensors

Terrain classification provides valuable information for both control and navigation algorithms of wheeled mobile robots. In this paper, a novel online outdoor terrain classification algorithm is proposed for wheeled mobile robots. The algorithm is based on only time-domain features with both low computational and low memory requirements, which are extracted from the inertial and magnetic sensor signals. Multilayer perceptron (MLP) neural networks are applied as classifiers. The algorithm is tested on a measurement database collected using a prototype measurement system for various outdoor terrain types. Different datasets were constructed based on various setups of processing window sizes, used sensor types, and robot speeds. To examine the possibilities of the three applied sensor types in the application, the features extracted from the measurement data of the different sensors were tested alone, in pairs and fused together. The algorithm is suitable to operate online on the embedded system of the mobile robot. The achieved results show that using the applied time-domain feature set the highest classification efficiencies on unknown data can be above 98%. It is also shown that the gyroscope provides higher classification rates than the widely used accelerometer. The magnetic sensor alone cannot be effectively used but fusing the data of this sensor with the data of the inertial sensors can improve the performance.

Fast terminal sliding mode control for dual arm manipulators

In this paper, present the recent advances in bimanual industrial manipulators have led to an increased interest in the specific problems pertaining to dual arm manipulation. This paper presents a control algorithm for dual arm robot that can move the object in a working plane both in translation and rotation ways. Different from other research that extend the control algorithms for a single robot to a dual arm robot because of fixed grasp assumption, this research has considered the frictional contact constraints to guarantee object grasping during moving of the object. Fast terminal sliding mode control (FTSMC) technique is used to design the controller and comparison to traditional and super-twisting sliding mode controls have been done. Simulations show the effectiveness and outperformance of the proposed control algorithm in comparison to considered sliding mode control techniques.

Unified Mapping Function-Based Neuroadaptive Control of Constrained Uncertain Robotic Systems.

For the existing adaptive constrained robotic control algorithms, the demanding "feasibility conditions" on virtual controller is normally inevitable and the extra limits on constraining functions have to be imposed, making the corresponding approaches more demanding and less user friendly in control development. Here, we develop a new neuroadaptive constrained control strategy for uncertain robotic manipulators in the presence of position and velocity constraints. First, a novel unified mapping function (UMF) is constructed so that the restriction on constraining boundaries is removed and more kinds of constraining forms can be handled. Second, by integrating the UMF-based coordinate transformation with the "universal" approximation characteristic of neural networks over some compact set, the developed neuroadaptive control completely obviates the complicated yet undesired "feasibility conditions." Furthermore, it is proven that all closed-loop signals are semiglobally bounded and the constraints are not violated. The effectiveness of the proposed control is validated via a two-link rigid robotic manipulator.

Neurorehabilitation robotics: how much control should therapists have?

Robotic technologies for rehabilitating motor impairments from neurological injuries have been the focus of intensive research and capital investment for more than 30 years. However, these devices have failed to convincingly demonstrate greater restoration of patient function compared to conventional therapy. Nevertheless, robots have value in reducing the manual effort required for physical therapists to provide high-intensity, high-dose interventions. In most robotic systems, therapists remain outside the control loop to act as high-level supervisors, selecting and initiating robot control algorithms to achieve a therapeutic goal. The low-level physical interactions between the robot and the patient are handled by adaptive algorithms that can provide progressive therapy. In this perspective, we examine the physical therapist's role in the control of rehabilitation robotics and whether embedding therapists in lower-level robot control loops could enhance rehabilitation outcomes. We discuss how the features of many automated robotic systems, which can provide repeatable patterns of physical interaction, may work against the goal of driving neuroplastic changes that promote retention and generalization of sensorimotor learning in patients. We highlight the benefits and limitations of letting therapists physically interact with patients through online control of robotic rehabilitation systems, and explore the concept of trust in human-robot interaction as it applies to patient-robot-therapist relationships. We conclude by highlighting several open questions to guide the future of therapist-in-the-loop rehabilitation robotics, including how much control to give therapists and possible approaches for having the robotic system learn from therapist-patient interactions.

Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control

This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method.

From humans to robots with the SAHR project

Researchers in the SAHR project are studying how humans acquire fine motor skills and looking to develop new learning and control algorithms for robots, as Professor Aude Billard explains.

Motion Control Method of Bionic Robot Dog Based on Vision and Navigation Information

With the progress and development of AI technology and industrial automation technology, AI robot dogs are widely used in engineering practice to replace human beings in high-precision and tedious industrial operations. Bionic robots easily produce control errors due to the influence of spatial disturbance factors in the process of pose determination. It is necessary to calibrate robots accurately to improve the positioning control accuracy of bionic robots. Therefore, a robust control algorithm for bionic robots based on binocular vision navigation is proposed. An optical CCD binocular vision dynamic tracking system is used to measure the end position and pose parameters of a bionic robot, and the kinematics model of the controlled object is established. Taking the degree of freedom parameter of the robot’s rotating joint as the control constraint parameter, a hierarchical subdimensional space motion planning model of the robot is established. The binocular vision tracking method is used to realize the adaptive correction of the position and posture of the bionic robot and achieve robust control. The simulation results show that the fitting error of the robot’s end position and pose parameters is low, and the dynamic tracking performance is good when the method is used for the position positioning of control of the bionic robot.

Deep Reinforcement Learning Based Three-dimensional Path Tracking Control of An Underwater Robot

This paper presents a deep reinforcement learning (DRL)-based three-dimensional path tracking control algorithm of an underwater robot to learn the path-tracking capability by interacting with the environment. To be specific, a hybrid path tracking guidance and controller based on three-dimensional line-of-sight (3D LOS) guidance and twin delayed deep deterministic policy gradient (TD3) algorithm is applied to complete kinematics and dynamics controller design. The reference angle is obtained by LOS algorithm, and TD3 algorithm is used to output the control laws. Aiming at the chattering problem in the output of the reinforcement learning controller, the commands filter and chattering penalty term are designed respectively. The tracking experiment of ten waypoints proves the feasibility of the algorithm proposed in this paper.

Control Algorithm and Realization of Adaptive Two-Wheeled Robot

This system selects STC12C5A60S2 single chip microcomputer as the core processor to control the algorithm and balance state of the balance robot. The robot body is loaded with a gyroscope accelerometer sensor mpu6050 and a magnetoelectric encoder motor 330. The single chip microcomputer filters the data collected by the gyroscope and accelerometer, and processes the upper limit of the integral of the motor output value. Through the conditioning circuit, the sensor collects the horizontal offset angle of the robot body and the wheel rotation speed, and corrects the signal noise and zero offset, so as to complete the attitude monitoring. Using proportional integral differential algorithm, the positive feedback of speed loop and the negative feedback of angle loop are adjusted comprehensively to realize the robot body attitude correction. After debugging, the trolley can achieve self-regulation and balance without the severe impact of the external environment.

Improved LVS guidance and path-following control for unmanned sailboat robot with the minimum triggered setting

This paper presents a dynamic event-triggered control algorithm for the unmanned sailboat robot (USR) to carry out the waypoints-based path-following mission. The proposed scheme consists of the guidance module and control one. For the guidance module, the improved logic virtual ship (LVS) guidance law is proposed to plan the reference route for the USR considering the marine environment with time-varying wind direction. The navigation zones for USR are divided into crosswind zone and non-crosswind zone. It is beneficial for the sailboats to achieve tracking and gybe in different navigation zones. As for the control part, the dynamic event-triggered control is proposed by setting tuning threshold parameter. It is advantageous to decrease the resource waste and channel occupancy. Compared with the existing event-triggered control with static threshold parameter, the proposed algorithm is with the better performance in data transmission. The closed-loop control system has been proved to be with the semi-global uniform ultimate bounded (SGUUB) stability. Finally, the simulation experiment illustrates the superiorities of the proposed algorithm.

Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy

The task of controlling a wheeled mobile robot is an important element of navigation algorithms. The control algorithm manages the robot’s movement in accordance with the path determined by the planner module, where the accuracy of mapping the given route is very important. Most often, mobile robots are battery-powered, which makes minimizing energy consumption and shortening travel time an important issue. For this reason, in this work, the mobile robot control algorithm was tested in terms of energy consumption, travel time and path mapping accuracy. During the research, a criterion was developed, thanks to which it was possible to select the optimal parameters of the pure pursuit algorithm that controls the movement of the tested robot. The research was carried out in the Laboratory of Intelligent Mobile Robots using the QBot2e mobile robot operating on the basis of differential drive kinematics. As a result of the research, optimal values of the control parameters were obtained, minimizing the travel time, energy consumption and mapping error of the given paths.

Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction

Simulation with a reasonable physical model is important to develop control algorithms for robots quickly, accurately, and safely without damaging the associated physical systems in various environments. However, it is difficult to choose the suitable tool for simulating a specific project. To help users in selecting the best tool when simulating a given project, we compare the performance of the four widely used physics engines, namely, ODE, Bullet, Vortex, and MoJoco, for various simple and complex industrial scenarios. We first summarize the technical algorithms implemented in each physics engine. We also designed four simulation scenarios ranging from simple scenarios for which analytic solution exists to complex industrial scenarios to compare the performance of each physics engine. We then present the simulation results in the default settings of all the physics engines, and analyze the behavior and contact force of the simulated objects.

A Generalized Approach to Impedance Control Design for Robotic Minimally Invasive Surgery

Energy based control methods are at the core of modern robotic control algorithms. In this paper we present a general approach to virtual model/mechanism control, which is a powerful design tool to create energy based controllers. We present two novel virtual-mechanisms designed for robotic minimally invasive surgery, which control the position of a surgical instrument while passing through an incision. To these virtual mechanisms we apply the parameter tuning method of Larby and Forni (2022), which optimizes for local performance while ensuring global stability.