Robot Motion Control Research Articles

Deep Reinforcement Learning for the Visual Servoing Control of UAVs with FOV Constraint

Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed method was verified using a monocular camera.

Converging Game Theory and Reinforcement Learning For Industrial Internet of Things

The fifth-generation (5G) wireless network provides high-rate, ultra-low latency, and high-reliability connections that can meet the Industrial Internet of Things (IIoT) requirements in factory automation, especially for robot motion control. In this paper, we address 5G service provisioning in an automated warehouse scenario, where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the coordinated multipoint (CoMP), we formulate a time-varying joint CoMP clustering and 5G ultra-reliable low-latency communication (URLLC) beamforming design problem to control the robots that move around the automated warehouse for goods storage with the planned reference tracks. Traditional iterative optimization approaches are impractical in such a dynamic wireless environment due to high computational time. We propose a game-theoretic CoMP clustering algorithm combined with the Proximal Policy Optimization method to obtain a stationary solution closed to that of the exhaustive search algorithm considered as the global optimal solution.

Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors

Scan-to-BIM technology can help construction stakeholders to obtain accurate BIM models for building information management and construction progress monitoring. The current practice of scan-to-BIM relies on laser scanning to capture a building's as-built condition, followed by processing scanned point clouds to generate BIM, which is laborious and time-consuming. This study presents a new robot-assisted mobile laser scanning approach using a legged robot and a solid-state LiDAR sensor, for automated 3D reconstruction and point cloud semantic segmentation. The proposed new approach involves integrating the Simultaneous Localisation and Mapping (SLAM) algorithm with robot motion control and pathfinding for 3D mapping to obtain the complete point cloud data. To ensure the completeness and density of scanned point clouds, new scanning fitness metrics and a grid-based direction-by-direction algorithm are designed for robot pathfinding. Then, an enhanced dynamic window approach is established to integrate the motion control of the legged robot with SLAM for reconstructing dense point clouds. Lastly, semantic segmentation is performed using ResPointNet++ to segment architectural and structural components. Experimental results show that the mobile scanning approach achieves a decent performance in point cloud completeness and density, with an overall mIoU of 81.75% for semantic segmentation. The ablation test demonstrates that the proposed approach is superior to the method that does not integrate the robot motion with SLAM. The proposed approach contributes to the efficient collection of high-quality point clouds for building interiors, with an aim to boost building information management.

Whole‐body motion planning and control of a quadruped robot for challenging terrain

AbstractQuadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event‐based state switching of the quadruped robot legs. And an on‐line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad‐144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method.

Parking Robot Path-Tracking System Based on Discrete PID Algorithm

To improve the space utilization rates of intelligent stereo garages, their key execution equipment—parking robots—must be highly flexible; therefore, the chassis of parking robots use steering wheel systems. However, this design increases the complexity of robot motion control and path-tracking systems. Because of the large sizes of parking robots, large position and angle deviations can lead to derailment events, posing significant risk to users. Thus, in this study, based on the characteristics required for parking robots, a diagonally arranged parking robot with double steering wheels was developed and its kinematic model was established using the velocity–geometry method. For the positioning of the robot in a garage, a fusion positioning algorithm based on quick response (QR) codes and track calculation was used, considering the requirements of real-time response and accuracy. A discrete proportional-integral-derivative (PID) path-tracking algorithm for the synchronous compensation of the position and angle deviations was also devised. Using the Visual C++ platform, a path-tracking system that uses an industrial computer and motion control card was developed. Experiments on the path-tracking capability of the parking robot showed that, using the discrete PID path-tracking algorithm, it can track a planned path well. On Z- and U-type paths, its maximum tracking deviations were reduced by 84.8% and 64.0%, compared with the unused path-tracking system, reaching 10.4 mm and 10.9 mm, respectively. Thus, the tracking accuracy was significantly improved, proving that the developed robot well satisfies the path-tracking requirements of parking robots.

Optimization and control of an energy-efficient vibration-driven robot

Vibration-driven robots are innovative mechanical systems that exhibit a wide range of capabilities and applications due to their simple propellers. The applications of these robots span from clearing debris after an incident, traversing pipelines for inspection and maintenance, or medical purposes. In this paper, optimization and motion control of an energy-efficient two-module vibration-driven robot are investigated. The robot contains two blocks connected by a spring as its main body, and an unbalanced rotating mass to drive the robot rectilinearly. At first, the governing dynamic equations of the robot are derived and solved using MATLAB/Simulink. Then, the dynamic model of the robot is verified by comparing the obtained results with the simulation results from MSC Adams. Subsequently, a parametric study is conducted to investigate the effect of various physical parameters of the robot on its average velocity and consumed energy. Afterward, optimization variables are determined and a proper objective function is considered. By performing an optimization process using a genetic algorithm (NSGA-II), optimal parameters of the robot are obtained. Moreover, for motion control of the optimized robot, two control schemes, PID and Fractional Order PID, are designed. To evaluate and compare the performance of the proposed controllers, parameters of both controllers are optimized using a genetic algorithm in which both tracking error and control input are minimized, simultaneously. Finally, tracking error and control effort of the mentioned controllers for motion control of the robot are compared and discussed.

Design of a decentralized Internet-based control system for industrial robot robotic arms

Abstract To meet the needs of small and medium-sized processing plants by establishing a software and hardware platform for a robotic arm control system with a hierarchical structure and designing a robot robotic arm motion control system that is adaptable to multiple environments and cost-effective. In this paper, the industrial robot robotic arm motion control system is designed based on the consistent fusion tracking algorithm under the decentralized Internet. While using the VisualC++ development environment for writing human-machine interface programs using the MFC framework, the device controls the robotic arm body to complete predetermined movements or operational tasks based on command information, sensing information, and the robot controller. The AMS1086CD-3.3 low-voltage differential linear regulator chip used in this design effectively implements the circuit design from 4.8V to 3.4V and successfully meets the 3.4V voltage supply required by the controller. The industrial robot robotic arm control system adopts an adaptive backstepping algorithm controller, and the error of the tracking system at 0 seconds in the simulation results is always kept within the range of [-1~1] that meets the requirements, which effectively reduces the control error of the robotic arm position. Therefore, the robotic arm control studied in this paper is composed of a complete control system in hardware and software, and the designed adaptive backstepping algorithm controller achieves the demand for good control performance of the robotic arm proper.

Energy efficient robots based on structures with tensegrity features and cable-driven mechanisms

The paper presents concept of energy efficient motion control of robots and other machines based on structures with tensegrity features and/or cable-driven mechanisms. The essence of concept is generalization of so called eigenmotion idea for these multi-DOF complex mechanisms. The term eigenmotion here refers to a motion of a mechanism in which the constant sum of kinetic and potential energy is maximally preserved. The operation of the drives is ideally used only to eliminate passive resistances and to minimize deviations of motion from the required trajectory. The main advantage of mechanisms with tensegrity features and cable-driven ones is a relatively high number of elements such as springs, active cables or variable bodies, whose energy absorbing properties can be suitably adjusted during design and some also continuously during operation. The variability of attainable eigenmotion trajectories of these types of mechanisms can be further extended thanks to number of drives higher than number of end-effector degrees of freedom. The concept is demonstrated on two planar systems, one structure with tensegrity features and one serial–parallel cable-driven robot. The examples show the optimization of the parameters to achieve the eigenmotion properties on the given trajectories, as well as the change of the eigenmotion trajectory by changing the adjustable parameters. The final control of mechanical models along energy efficient trajectories is realized by computed torque control method.

Convergence theorems for monotone vector field inclusions and minimization problems in Hadamard spaces

Abstract This article analyses two schemes: Mann-type and viscosity-type proximal point algorithms. Using these schemes, we establish Δ-convergence and strong convergence theorems for finding a common solution of monotone vector field inclusion problems, a minimization problem, and a common fixed point of multivalued demicontractive mappings in Hadamard spaces. We apply our results to find mean and median values of probabilities, minimize energy of measurable mappings, and solve a kinematic problem in robotic motion control. We also include a numerical example to show the applicability of the schemes. Our findings corroborate some recent findings.

Meta-Routing Paradigm for Robotic Ad-hoc Networks

With the increasing use of robotic networks, communication issues such as maintaining connections between nodes are becoming more prevalent. While previous routing protocols for wireless networks have been developed, they tend to address routing and link maintenance separately. Consequently, the separation leads to increased costs and delays in network communication. Existing routing protocols typically focus on discovering links, connecting them, finding the most efficient path, and reducing costs associated with the path. However, their limitations have led to the development of a new routing mechanism for robotic networks called Meta-Routing. Meta-Routing builds on existing routing protocols by incorporating regular routing of packets and maintenance of links in mobile agent environments. This approach aims to improve efficiency and reduce routing and link maintenance costs. In addition, meta-Routing seeks to minimize communication path costs and the overhead cost associated with discovering a route, repairing a link, or creating a new communication path among nodes. This paper presents a method for achieving Meta-Routing by controlling robot motion based on recognizing the radio frequency (RF) environment through Hidden Markov Models (HMMs) and gradient descent methods. Simulation results show that Meta-Routing, based on controlling individual robot motion, can provide self-healing capabilities in mobile robot networks, decrease network latency, and improve network performance.

Algorithmization of Guidance and Motion Control of a Space Manipulation Robot in the Service Tasks of a Non-Operative Spacecraft

Nowadays there are many non-functioning spacecraft in orbit that have run out of fuel, or have failed due to breakdown. Therefore, the concept of a serviced space and the development of space manipulation robot for extending the spacecraft service life are becoming expedient. Space manipulation robot will be able to perform a variety of tasks, from inspecting malfunctions of a serviced spacecraft, to performing repairs and refueling the target vehicle. The article proposes a strategy and algorithms for the guidance and motion control of a space manipulation robot at the stage of rendezvous with a non-cooperative spacecraft to perform maintenance tasks. The purpose of the article is to synthesize the control of the translational and rotational motion of the space manipulation robot for its convergence with the target satellite at a given distance. The control system is presented in the form of a hierarchical two-level "guidance-stabilization" system. At the guidance level, a transition quaternion of the associated coordinate system to the required position is formed, as well as thrust engine control, which ensures the translational motion of the space manipulation robot at the required velocity. At the stabilization level, a control is formed that superpose the associated coordinate system of the space manipulation robot with the direction to the served satellite. The article proposes a scheme and a mathematical model of the propulsion system, angular and translational motion of the service satellite. The modeling of the developed guidance and motion control algorithms in the SIMULINK environment has been carried out.

A Modified Dai–Liao Conjugate Gradient Method Based on a Scalar Matrix Approximation of Hessian and Its Application

We introduce and investigate proper accelerations of the Dai–Liao (DL) conjugate gradient (CG) family of iterations for solving large-scale unconstrained optimization problems. The improvements are based on appropriate modifications of the CG update parameter in DL conjugate gradient methods. The leading idea is to combine search directions in accelerated gradient descent methods, defined based on the Hessian approximation by an appropriate diagonal matrix in quasi-Newton methods, with search directions in DL-type CG methods. The global convergence of the modified Dai–Liao conjugate gradient method has been proved on the set of uniformly convex functions. The efficiency and robustness of the newly presented methods are confirmed in comparison with similar methods, analyzing numerical results concerning the CPU time, a number of function evaluations, and the number of iterative steps. The proposed method is successfully applied to deal with an optimization problem arising in 2D robotic motion control.

Agricultural Autonomous Robotic Platform Motion Control

A model of the movement of a robotic platform adapted to the conditions of an industrial orchard is proposed. (Research purpose) Development of a motion control system for an autonomous robotic wheeled platform based on inertial and satellite navigation and traversed path calculation, which will allow it to move in an apple orchard and automatically perform various technological operations, such as fertilization, growth diseases control of, fruit harvesting. (Materials and methods) A mathematical model was developed to control the movement of a robotic platform, taking into account the turning radii of three types, the length of the arc of the performed circle, the speed of movement in the garden plantation rows using a garden electronic map. The method used allows implementing a program for the robotic platform automatic movement around a typical orchard using a minimum set of sensors, significantly reducing the load on the onboard computer processor and memory. Software, developed in the Python programming language, enables plotting the robotic platform route, displaying the movement trajectory, and indicating the positioning accuracy at each point in relation to the trees in the garden plantation rows, the movement speed and the wheel rotation angle. (Results and discussion) The robotic platform managed to autonomously pass the preset routes, while the interaction of the software and the robotic platform hardware was provided. A field testing of the developed software was performed. (Conclusions) The specified accuracy of the robotic platform positioning was confirmed for the 3.5-meter aisles of intensive orchards. The maximum deviation from the task map using satellite and inertial navigation system was 164 millimeters, which complies with the agrotechnical requirements for mechanized fruit harvesting.

Online Search-Based Collision-Inclusive Motion Planning and Control for Impact-Resilient Mobile Robots

This article focuses on the emerging paradigm shift of collision-inclusive motion planning and control for impact-resilient mobile robots, and develops a unified hierarchical framework for navigation in unknown and partially observable cluttered spaces. At the lower level, we develop a deformation recovery control and trajectory replanning strategy that handles collisions that may occur at run time, locally. The low-level system actively detects collisions (via embedded Hall effect sensors on a mobile robot built in-house), enables the robot to recover from them, and locally adjusts the postimpact trajectory. Then, at the higher level, we propose a search-based planning algorithm to determine how to best utilize potential collisions to improve certain metrics, such as control energy and computational time. Our method builds upon A* with jump points. We generate a novel heuristic function, and a collision checking and adjustment technique, thus making the A* algorithm converge faster to reach the goal by exploiting and utilizing possible collisions. The overall hierarchical framework generated by combining the global A* algorithm and the local deformation recovery and replanning strategy, as well as individual components of this framework, are tested extensively both in simulation and experimentally. An ablation study draws links to related state-of-the-art search-based collision-avoidance planners (for the overall framework), as well as search-based collision-avoidance and sampling-based collision-inclusive global planners (for the higher level). Results demonstrate our method's efficacy for collision-inclusive motion planning and control in unknown environments with isolated obstacles for a class of impact-resilient robots operating in 2-D.

Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)

Indoor mobile robot (IMR) motion control for e-SLAM techniques with limited sensors, i.e., only LiDAR, is proposed in this research. The path was initially generated from simple floor plans constructed by the IMR exploration. The path planning starts from the vertices which can be traveled through, proceeds to the velocity planning on both cornering and linear motion, and reaches the interpolated discrete points joining the vertices. The IMR recognizes its location and environment gradually from the LiDAR data. The study imposes the upper rings of the LiDAR image to perform localization while the lower rings are for obstacle detection. The IMR must travel through a series of featured vertices and perform the path planning further generating an integrated LiDAR image. A considerable challenge is that the LiDAR data are the only source to be compared with the path planned according to the floor map. Certain changes still need to be adapted into, for example, the distance precision with relevance to the floor map and the IMR deviation in order to avoid obstacles on the path. The LiDAR setting and IMR speed regulation account for a critical issue. The study contributed to integrating a step-by-step procedure of implementing path planning and motion control using solely the LiDAR data along with the integration of various pieces of software. The control strategy is thus improved while experimenting with various proportional control gains for position, orientation, and velocity of the LiDAR in the IMR.

An online impedance adaptation controller for decoding skill intelligence

Variable Impedance control allows robots and humans to safely and efficiently interact with unknown external environments. This tutorial introduces online impedance adaptation control (OIAC) for variable compliant joint motions in a range of control tasks: rapid (<1s) movement control (i.e., whipping to hit), arm and finger impedance quantification, multifunctional exoskeleton control, and robot-inspired human arm control hypothesis. The OIAC has been introduced as a feedback control, which can be integrated into a feedforward control, e.g., learned by data-driven methods. This integration facilitates the understanding of human and robot arm control, closing a research loop between biomechanics and robotics. It shows not only a research way from biomechanics to robotics, but also another reserved one. This tutorial aims at presenting research examples and Python codes for advancing the understanding of variable impedance adaptation in human and robot motor control. It contributes to the state-of-the-art by providing an online impedance adaptation controller for wearable robots (i.e., exoskeletons) which can be used in robotic and biomechanical applications.

Gait Generation Method of Snake Robot Based on Main Characteristic Curve Fitting.

Gait generation method is one of the important contents of snake robot motion control. Different gait generation methods produce completely different forms of control functions, so snake robots need more complicated programming logic and processes to realize various gaits and their transformation. Therefore, we propose a new unified expression of gait method, The MCC (main characteristics control) method simplifies and unifies the control functions of different snake robots gaits by extracting the main features of the backbone curves of snake robots gaits. Since all periodic curves that meet the Dirichlet conditions can be formed by superposition of sinusoidal curves, taking the "lowest frequency" part that reflects the main characteristics of the curve as the target configuration can simplify the motion control function of snake robots' gaits. Based on the MCC method, some snake robot gaits are reconstructed, including serpentine gait, rolling gait, helix rolling gait, and crawler gait. In addition, based on MCC method, an AEH-sidewinding gait control method is proposed. The backbone of the AEH-sidewinding gait is closer to the ideal elliptic helix, thus improving the accuracy of its kinematics modeling of snake robot sidewinding gait. Finally, the validity of this gait is verified by experiments. This unified gait expression of snake robots will be helpful to realize smooth gait switching between different gaits of snake robots.

Feasibility of motion control of industrial robots, CNC machine tools and mechatronic systems. Part 2

The problem of realization of control systems in real time is investigated. The relationship between the complexity of control objects and the duration of the control cycle is revealed, according to which, as the complexity of control objects increases, the duration of the control cycle is reduced. This leads to an increase in requirements for the speed of control systems, which can be achieved by increasing the speed of the used electronic component base and other elements of the control system, as well as optimizing the architecture of the control system. One of the most promising directions in the development of real-time control systems for complex objects is the use of a memory-centric architecture of the control system. Keywords: motion control, industrial robot, machine tool, mechatronic system, control cycle, memory-centric architecture. andarmo@yandex.ru

Implicit Robot Control Using Error-Related Potential-Based Brain–Computer Interface

This article investigates the application of using error-related potential (ErrP)-based brain–computer interface (BCI) paradigm to control robot movements with implicit commands. ErrP is a neural signal that is automatically evoked when the machine’s behavior deviates from the observer’s expectations. By continuously monitoring the presence of ErrP, the system infers the observer’s reaction toward robot movements and automatically translates them into control commands, allowing the implicit control of robot movements without interfering the observer’s other tasks. However, ErrP-based BCI has a major limitation: the ErrP is evoked after the robot has committed an error, which might be costly or dangerous in contexts, such as assembly line or autonomous driving. To address these limitations, we propose a novel robotic design for ErrP-based BCI that allows humans to continuously evaluate the robot’s intentions and intervene earlier, if necessary before the robot commits an error. We evaluate the proposed robotic design and BCI system via an experiment where a ground robot performs a binary target-reaching task. The high classification accuracy (77.57%) demonstrated that the proposed ErrP-based BCI is feasible for human–robot intention communication before the robot commits an error and has the potential to broaden the range of applications for ErrP-based BCIs.

Kinematically Constrained Jerk–Continuous S-Curve Trajectory Planning in Joint Space for Industrial Robots

This work deals with jerk–continuous trajectory planning for robotic manipulators by means of the fourth-order S-curve to ensure motion smoothness. The algorithm presented in this work can cause the acceleration and jerk to stay in a saturated state in order to improve the efficiency of a robot’s programming and operation. Moreover, a multi-axis synchronization planning algorithm is proposed and integrated for enhanced motion stability in terms of generated synchronized and continuous motion trajectories, for which the effectiveness of the proposed trajectory planning algorithm is verified in both the joint and Cartesian spaces. The proposed algorithm does not involve any optimization procedures or iterative processes, as the kinematically constrained trajectory is generated by polynomial equations to realize the real-time motion control of robots. Moreover, the presented algorithm can generate the jerk continuity trajectory, rather than only the acceleration continuity, as in most reported works.