Robot Control Algorithms Research Articles

Position-Posture Control of Multilegged Walking Robot Based on Kinematic Correction

Posture-position control is the fundamental technology among multilegged robots as it is hard to get an effective control on rough terrain. These robots need to constantly adjust the position-posture of its body to move stalely and flexibly. However, the actual footholds of the robot constantly changing cause serious errors during the position-posture control process because their foot-ends are basically in nonpoint contact with the ground. Therefore, a position-posture control algorithm for multilegged robots based on kinematic correction is proposed in this paper. Position-posture adjustment is divided into two independent motion processes: robot body position adjustment and posture adjustment. First, for the two separate adjustment processes, the positions of the footholds relative to the body are obtained and their positions relative to the body get through motion synthesis. Then, according to the modified inverse kinematics solution, the joint angles of the robot are worked out. Unlike the traditional complex closed-loop position-posture control of the robot, the algorithm proposed in this paper can achieve the purpose of reducing errors in the position-posture adjustment process of the leg-foot robot through a simple and general kinematic modification. Finally, this method is applied in the motion control of a bionic hexapod robot platform with a hemispherical foot-end. A comparison experiment of linear position-posture change on the flat ground shows that this method can reduce the attitude errors, especially the heading error reduced by 55.46%.

A review on simulation in digital twin for aerospace, manufacturing and robotics

The Digital Twin (DT) is known as a computer-generated equivalent of a physical system. It is used to simulates for different objectives through the real-time harmonization of data received from the field. The prime interpretation of DT is very close link to simulation technique and software used. This relation can be viewed in two approaches viz., (i) DT is model to present the system based upon the various kinds of simulations techniques and tools and, (ii) DT as simulation of the system itself. In the aerospace, simulations replicate the continuous time history of flights, producing vast amount of data of simulations in order to recognize what the aircraft has undergone and to project forthcoming maintenance requirements and intrusions using various applications based simulation techniques, Computational Fluid Dynamics (CFD), Computer-Aided Engineering (CAE), Finite Element Methods (FEM) and Monte Carlo simulation. In case of manufacturing, the simulation helps to mimic the complex behavior of production procedures considering the effects of design constraints, human interventions, and any other external disturbances. In robotics, the simulation is mainly concern with the virtual commissioning of task in order to improve the control algorithms for robots in the course of development stage. In this paper, various recent contributions on simulation-based DT and DT-based simulation models proposed by researchers in the field of aerospace, manufacturing and robotics have been presented. The motivation behind this review is to pave the way and clarify the conceptual foundations on the adaptation of simulation techniques and software to develop DT for various applications.

Research on control algorithm of the upper limb rehabilitation robot based on PI hysteresis compensation

The upper limb rehabilitation robot is used to assist patients with upper limb hemiplegia for rehabilitation training. During the passive rehabilitation training of the patient, the trajectory of the rehabilitation training must be as smooth as possible, so that the patient will not feel discomfort when pulling the affected limb. The passive control algorithm of the existing upper limb rehabilitation robot is not ideal for the lag processing of the system. This paper designs a passive control algorithm based on PI hysteresis compensation. The hysteresis effect of the system can be greatly reduced by introducing the hysteresis compensation term uc . Experimental results show that the algorithm can effectively eliminate the hysteresis effect of the system.

Security methods for a group of mobile robots according to the requirements of Russian and foreign legislation

This paper is devoted to the problem of creating security methods for mobile robotic systems. The urgency of the problem of ensuring the security of mobile robotic systems is associated with the presence of a contradiction between the growing popularity of the mobile robotic systems and the presence of many vulnerabilities. Development of a universal security system which should provide protection not only against an malicious attack, but also allow the system to cope with unintentional errors, interference or changes in the external environment, thereby improving the operation of group control algorithms for robots, and increasing the reliability and stability of the mobile robotic systems.

RTD-SEPs: Real-time detection of stem emerging points and classification of crop-weed for robotic weed control in producing tomato

A novel technique for enabling robotic weed control in a commercial processing tomato field having densely populated weeds is described. It is necessary to accurately locate the stem emerging points (SEPs) of crop plants for the successful application of a mechanical weeding actuator to remove weeds during automated weeding. However, it is a difficult and challenging task to locate the SEPs in complex natural scenarios such as when the main stem is occluded by weeds or crop foliage, the crop plants are lying on the soil surface, there are non-uniform planting bed conditions, or there is leaf damage due to insects etc. To overcome these challenges a novel crop signalling concept has been proposed to mark the crop plants at planting to make them machine-readable. Plants lacking this crop signal were classified as weeds and removed by the robotic weed knife actuator. A machine-vision algorithm was developed to analyse the seven views of the crop plants taken by camera with help of a specially designed imaging chamber and locate the SEPs of tomato plants, which was passed to the robotic weed knife control algorithm to remove weeds. The algorithm was successfully detected and located the main stems of tomato plants in outdoor environment with success rate of 99.19% while traveling at a speed of 3.2 km h−1 with a processing time for all views of 30 ms f−1.

Modeling and Trajectory Tracking Control for Magnetic Wheeled Mobile Robots Based on Improved Dual-Heuristic Dynamic Programming

In this article, a mathematical model of a magnetic-wheeled mobile robot (MWMR) used for wall climbing in some special industrial sites is established, and to realize the precise motion control of the robot, an intelligent discrete algorithm for trajectory tracking control of the MWMR is presented. The robot is subjected to nonholonomic constraints when moving on the wall. The discrete mathematical model of the MWMR is established with the description of the kinematics and dynamics, where the dynamics are described by the second-order Lagrange's equations. Improved dual-heuristic dynamic programming (DHP) where the actor-critic structure adopts random vector functional link neural networks (RVFL NNs) is the main configuration of the trajectory tracking control algorithm. Moreover, the tracking control algorithm is supplied by a PD controller and a supervisory element to generate overall control signals. The improved strategy is to optimize the input layer weights of RVFL NNs by a genetic algorithm to improve the approximation performance of DHP. Simulations are performed to test the trajectory tracking control algorithm for this wall-climbing robot, and the results are compared with those of neural network tracking control algorithm. Comparative analysis verifies the effectiveness and advancement of the proposed method.

Toward Design of a Drip-Stand Patient Follower Robot

A person following robot is an application of service robotics that primarily focuses on human-robot interaction, for example, in security and health care. This paper explores some of the design and development challenges of a patient follower robot. Our motivation stemmed from common mobility challenges associated with patients holding on and pulling the medical drip stand. Unlike other designs for person following robots, the proposed design objectives need to preserve as much as patient privacy and operational challenges in the hospital environment. We placed a single camera closer to the ground, which can result in a narrower field of view to preserve patient privacy. Through a unique design of artificial markers placed on various hospital clothing, we have shown how the visual tracking algorithm can determine the spatial location of the patient with respect to the robot. The robot control algorithm is implemented in three parts: (a) patient detection; (b) distance estimation; and (c) trajectory controller. For patient detection, the proposed algorithm utilizes two complementary tools for target detection, namely, template matching and colour histogram comparison. We applied a pinhole camera model for the estimation of distance from the robot to the patient. We proposed a novel movement trajectory planner to maintain the dynamic tipping stability of the robot by adjusting the peak acceleration. The paper further demonstrates the practicality of the proposed design through several experimental case studies.

Research on Key Technologies of Elders’ Exoskeleton Robot Assisted by Physical Exercise Based on Fuzzy PID Control

In the past, walking aids (such as crutches and walking aids) widely used by the elderly in physical exercise had relatively single functions, low intelligence, insufficient safety and comfort, and could not meet the needs of users for independent living. Therefore, it is necessary to apply robot technology to the field of traditional walking aid equipment and design a more intelligent, comfortable and safe walking aid robot system. The gait control of the exoskeleton robot assisted by physical exercise for the elderly is studied, and an algorithm based on fuzzy PID is proposed. Firstly, a fuzzy PID control algorithm for the exoskeleton robot assisted by physical exercise for the elderly is given. Secondly, membership functions are selected according to the parameters of the robot joint angle error and error variation. Finally, combined with practical operation experience, a fuzzy rule table is established, and online self-tuning of PID parameters is completed. The experimental results show that the fuzzy PID control method can make the exoskeleton robot move smoothly and realize the gait control of the robot initially.

Study on the Genetic and Fuzzy PID Control Algorithm of Soccer Robot

Study on the Genetic and Fuzzy PID Control Algorithm of Soccer Robot

Inference of Manipulation Intent in Teleoperation for Robotic Assistance

In teleoperation, predicting an operator’s intent and providing subsequent assistance have demonstrated great advantages in reducing an operator’s workload and a task’s difficulty as well as enhancing the task performance. Current research aims to tackle target-approaching intent, while our work focus on inferring manipulation (task) intent after the user grasps the object. We model how an object is grasped when being utilized in different manipulation tasks (intents) and then adopt this object grasping model in teleoperation for the intent inference. Our paper focuses on determining if direct interaction models can be used for indirect interaction. As the nature of one’s grasping pose may satisfy multiple tasks (intents), we explore a form of classification modeling known as multi-label classification for multiple broad categories of tasks and objects. We also comprehensively compare classification techniques to determine the most suitable method for determining manipulation intent. With knowing the manipulation intent, future robot control algorithms can provide more helpful and appropriate assistance to facilitate task accomplishment.

I-Support: A robotic platform of an assistive bathing robot for the elderly population

In this paper we present a prototype integrated robotic system, the I-Support bathing robot, that aims at supporting new aspects of assisted daily-living activities on a real-life scenario. The paper focuses on describing and evaluating key novel technological features of the system, with the emphasis on cognitive human–robot interaction modules and their evaluation through a series of clinical validation studies. The I-Support project on its whole has envisioned the development of an innovative, modular, ICT-supported service robotic system that assists frail seniors to safely and independently complete an entire sequence of physically and cognitively demanding bathing tasks, such as properly washing their back and their lower limbs. A variety of innovative technologies have been researched and a set of advanced modules of sensing, cognition, actuation and control have been developed and seamlessly integrated to enable the system to adapt to the target population abilities. These technologies include: human activity monitoring and recognition, adaptation of a motorized chair for safe transfer of the elderly in and out the bathing cabin, a context awareness system that provides full environmental awareness, as well as a prototype soft robotic arm and a set of user-adaptive robot motion planning and control algorithms. This paper focuses in particular on the multimodal action recognition system, developed to monitor, analyze and predict user actions with a high level of accuracy and detail in real-time, which are then interpreted as robotic tasks. In the same framework, the analysis of human actions that have become available through the project’s multimodal audio–gestural dataset, has led to the successful modeling of Human–Robot Communication, achieving an effective and natural interaction between users and the assistive robotic platform. In order to evaluate the I-Support system, two multinational validation studies were conducted under realistic operating conditions in two clinical pilot sites. Some of the findings of these studies are presented and analyzed in the paper, showing good results in terms of: (i) high acceptability regarding the system usability by this particularly challenging target group, the elderly end-users, and (ii) overall task effectiveness of the system in different operating modes.

Two-dimensional obstacle avoidance control algorithm for snake-like robot in water based on immersed boundary-lattice Boltzmann method and improved artificial potential field method

In order to study the adaptability of a multi-redundancy and multi-degree-of-freedom snake-like robot to underwater motion, a two-dimensional (2-D) obstacle avoidance control algorithm for a snake-like robot based on immersed boundary-lattice Boltzmann method (IB-LBM) and improved artificial potential field (APF) is proposed in this paper. Firstly, the non-linear flow field model is established under the framework of LBM, and the IB method is introduced to establish a fluid solid coupling of a 2-D soft snake-like robot. Then, the obstacle avoidance of a snake-like robot in a flow field is realized by optimizing the curvature equation of the serpentine curve and eliminating the local minimum in APF method. Finally, the effects by exerted different control parameters on a snake-like robot’s obstacle avoidance capability are analyzed via MATLAB simulation experiment, by which we can find the optimal parameter of the obstacle avoidance and testify the validity of the proposed control algorithm.

2D Underwater Obstacle Avoidance Control Algorithm Based on IB-LBM and APF Method for a Multi-Joint Snake-Like Robot

In order to study the rapidity and stability of multi-joint snake-like robots in underwater obstacle avoidance, a new underwater obstacle avoidance control algorithm for multi-joint snake-like robots based on the combination of IB-LBM (Immersed Boundary-Lattice Boltzmann Method) and APF (Artificial Potential Field) is proposed in this paper. Firstly, the IB-LBM is used to establish the non-linear flow field model and the fluid-structure coupling model of a multi-joint snake-like robot. Secondly, the 2-D Serpentine curve equation of motions of a multi-joint snake-like robot is improved, and APF method is added to each joint of the robot to control its motion direction, so as to achieve effective obstacle avoidance of the robot in the flow field. Then by MATLAB simulation experiment, influences of different control parameters on obstacle avoidance performance of a multi-joint snake-like robot are analyzed, and the best parameters suitable for obstacle avoidance of a robot are found to improve the rapidity and stability of the whole system. Finally, an underwater obstacle avoidance experiment of a multi-joint snake-like robot is carried out to verify the effectiveness of the proposed control algorithm in real environment.

Legged Fire Fighter Robot Movement Using PID

Proportional Integral Derivative (PID) control is a control system commonly used by industry. Approximately 90% of industrial equipment uses a PID controller because it is easy to use. In the Indonesian Fire Extinguisher Robot Contest (IFERC), the contested robots must follow the contours of the walls of the arena. A Fire extinguisher robot navigation was chosen because the race arena of the competition consisted of walls with different aisles and rooms. The navigation robots used PID control. This study designed and implemented a control algorithm for legged fire extinguishing robots using the PID method, where the PID control was processed in a microcontroller. The angles for each servo motor generated by the calculation of the PID enable the robot to navigate by taking decisions to move quickly or slowly, turn right, turn left and stop. The robot's proximity sensor data and fire sensors enable the fire to be extinguished. The result showed that the robot can carry out its duties optimally.

A Novel Dynamic Locomotion Control Method for Quadruped Robots Running on Rough Terrains

Quadruped robots have excellent application prospects whereas the locomotion control of them on rough terrains is still a challenging problem, especially for those of large scales. The existing methods are either too complicated or lack of accuracies due to assumptions used. This paper presents a novel control algorithm for quadruped robots running on rough terrains inspired by the virtual model control and the model predictive control. State recursions are carried out based on the dynamic model of the trunk during the standing phase. The modeling of the body is implemented in the self-defined motion reference frame that avoids global state estimations and accumulative errors. The force distribution of the standing legs is realized by quadratic optimization involving state predictions. Forces of the swing legs are calculated by the virtual spring-damping model that follow the desired trajectory which is robust to external disturbances. These two sub-controllers are combined by the time-force based state machine. Simulation results show that the quadruped robot obtains the adaptability to rough terrains and robustness to lateral pushes with the proposed method.

Control of Two-wheeled Mobile Robots Moving in Formation

Abstract This paper presents control algorithm for multiple non-holonomic mobile robots moving in formation. Method from Canudas et al. (1994) is used to track desired trajectory. In the new algorithm this approach is combined with collision avoidance. Artificial potential functions are used to generate repulsive component of the control. Stability analysis is based on Lyapunov-like function. Effectiveness of the presented method is illustrated by simulation results for a large formation of mobile platforms. Robots avoid collisions with each other and with static obstacles. Position and orientation reach values close to steady state in 50s.

Adaptive Nonlinear Control Algorithm for a Self-Balancing Robot

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.

МЕТОД УПРАВЛЕНИЯ ДВУНОГИМИ ШАГАЮЩИМИ РОБОТАМИ С ИНТЕГРИРОВАННЫМИ УПРУГИМИ ЭЛЕМЕНТАМИ

The study deals with the control of a two–legged walking robot with elastic elements integrated into its links between the respective actuators and gearboxes. A mathematical model of the movement of the device in the sagittal plane, as well as a model of series elastic actuators, have been presented. A robot control algorithm based on the use of the CLQR controller is proposed. As a result of numerical simulation, the influence of elastic elements and their parameters on the robot dynamics has been established, taking into account the limitations of the drive torque (torque saturation) and the presence of nonlinearities in the sensor system of the object. The simulation was performed taking into account that the control system does not have access to a drive model with elastic elements.

Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks

This paper reports the kinematic, kinetic and electromyographic (EMG) dataset of human locomotion during level walking at different velocities, toe- and heel-walking, stairs ascending and descending. A sample of 50 healthy subjects, with an age between 6 and 72 years, is included. For each task, both raw data and computed variables are reported including: the 3D coordinates of external markers, the joint angles of lower limb in the sagittal, transversal and horizontal anatomical planes, the ground reaction forces and torques, the center of pressure, the lower limb joint mechanical moments and power, the displacement of the whole body center of mass, and the surface EMG signals of the main lower limb muscles. The data reported in the present study, acquired from subjects with different ages, represents a valuable dataset useful for future studies on locomotor function in humans, particularly as normative reference to analyze pathological gait, to test the performance of simulation models of bipedal locomotion, and to develop control algorithms for bipedal robots or active lower limb exoskeletons for rehabilitation.

Model-free Optimal Tracking Control for an Aircraft Skin Inspection Robot with Constrained-input and Input Time-delay via Integral Reinforcement Learning

This paper presents a model-free optimal tracking control algorithm for an aircraft skin inspection robot with constrained-input and input time-delay. To tackle the input time-delay problem, the original system is transformed into a delay-free system with constrained-input and unknown input coupling term. In order to overcome the optimal control problem subject to constrained-input, a discounted value function is employed. In general, it is known that the HJB equation does not admit a classical smooth solution. Moreover, since the input coupling term of the delay-free system is unknown, a model-free integral reinforcement learning(IRL) algorithm which only requires the system sampling data generated by arbitrary different control inputs and external disturbances is proposed. The model-free IRL method is implemented on an actor-critic neural network (NN) structure. A system sampling data set is utilized to learn the value function and control policy. Finally, the simulation verifies the effectiveness of the proposed algorithm.