Robot Control Research Articles

Magnetic motors in interphases: Motion control and integration in soft robots.

Magnetic motors are a class of out-of-equilibrium particles that exhibit controlled and fast motion overcoming Brownian fluctuations by harnessing external magnetic fields. The advances in this field resulted in motors that have been used for different applications, such as biomedicine or environmental remediation. In this Perspective, an overview of the recent advancements of magnetic motors is provided, with a special focus on controlled motion. This aspect extends from trapping, steering, and guidance to organized motor grouping and degrouping, which is known as swarm control. Further, the integration of magnetic motors in soft robots to actuate their motion is also discussed. Finally, some remarks and perspectives of the field are outlined.

ADP-Based $\mathcal {H}_{\boldsymbol{\infty }}$ Optimal Decoupled Control of Single-Wheel Robots With Physically Coupling Effects, Input Constraints, and Disturbances

ADP-Based $\mathcal {H}_{\boldsymbol{\infty }}$ Optimal Decoupled Control of Single-Wheel Robots With Physically Coupling Effects, Input Constraints, and Disturbances

Learning predictive control of wheeled mobile robot with communication delays

Abstract This paper proposed a learning predictive control method for wheeled mobile robots with communication delays. In consideration of the property in the Networked Control System (NCS), communication delay was divided into two types: delay from sensor to controller and delay from controller to actuator. For each type of delay, state prediction and state augmentation techniques were utilized respectively to diminish their passive impact on the control of WMR. Then receding horizon reinforcement learning approach was adopted to learn the optimal policy based on the delay compensation. Finally, simulations were performed applying the proposed method to the trajectory tracking of WMR and showed the validity of the proposed scheme.

An Approach for Identifying Dynamic Parameters in Robotic Systems with Inconsistent Joint Measurements

Abstract Achieving accurate robot control and realistic robot simulation relies on the precise modeling of robotic dynamics. Although the identification method for obtaining dynamic parameters has been developed for several years, joint inconsistency has largely been disregarded in prior studies. The inconsistency of joint actuators results in varying confidence levels of their measurement results, leading to the departure of the final identification parameters from valid values, thereby impacting the control performance. This paper presents a novel identification method that effectively addresses the issue of joint inconsistency by assigning distinct weights to each joint. The presented approach extends the least square (LS) method and incorporates the particle swarm optimization (PSO) algorithm to calculate the weights of each individual joint. This approach is referred to as PWLS (PSO-based weighted least square). Simulation experiments demonstrated the superior identification accuracy of the PWLS method compared to the LS method in a robot system characterized by joint inconsistency. Moreover, the experiments were performed on a 3-degree-of-freedom (DoFs) robotic limb, which exhibited improved identification performance in both the excitation and verification trajectories. These findings have promising implications for enhancing the control and simulation of robotic systems.

ABDALAMEER. DEVELOPMENT OF AUTONOMOUS MOBILE ROBOTS CONTROL SYSTEMS

The development of an efficient control system for mobile autonomous robots is explored. It delineates key stages in this process, starting with the necessity of precise trajectory planning followed by the creation of a mathematical model for controlling the robotic device. Subsequently, the implementation of navigation algorithms in the management system is emphasized. The importance of conducting comprehensive error analysis to address discrepancies during robot movement is underscored. Post the primary phase of control system creation, evaluating the cost-effectiveness and ensuring workplace safety are deemed crucial management steps. Localization emerges as a pivotal factor enabling the robot's effective autonomous movement relative to its target position. This, in turn, aids in orienting the robot within its external environment. Spatial information acquisition is underscored through the significance of traffic planning, cartography and specialized robotics sensors. Furthermore, the article delves into proprioceptive sensors, detailing their components, such as optical and electrical conductor sensors operating via the Hall effect discovered in 1879. It provides a formula to accurately ascertain conductivity state, involving parameters like hole and electron concentration, electron and hole mobility and elementary charge. In essence, the control system parallels the human brain, requiring a recognition system, action planning and execution functions. Navigation is identified as a critical element facilitating the robot's movement in three-dimensional space. The article integrates technical intricacies about proprioceptive sensors, highlighting their pivotal role within robot control systems.

Spatial guidance and control of a space robot during flights in a low-orbit constellation of earth observation mini-satellites

Spatial guidance and control of a space robot during flights in a low-orbit constellation of earth observation mini-satellites

DiffSkill: Improving Reinforcement Learning through diffusion-based skill denoiser for robotic manipulation

Although Reinforcement Learning (RL) has demonstrated impressive success in various applications, addressing complex robotic manipulation tasks remains a formidable challenge. Recently, Skill-based approaches that extract reusable skills from offline data and encode them into a latent space are proposed to leverage prior knowledge for accelerating robot learning. However, existing skill learning methods predominantly rely on regularization constraints or reversible mappings to guide skill prior generation, lacking explicit control over the trade-off between exploiting offline knowledge and exploring novel skill behaviors. In this paper, we point out that the challenge of skill exploration lies in the noise within skill embeddings and propose a novel denoising-based skill-based RL framework, DiffSkill. Specifically, our DiffSkill integrates a diffusion-based skill denoiser into the hierarchical architecture, effectively bridging the gap between offline knowledge and learned skill prior embeddings through iterative denoising. Nevertheless, incorporating diffusion models into the skill-based RL framework for robot control faces two main challenges: (i) Uncertain noisy levels of skill embeddings and (ii) Action oscillation during skill transitions. In this regard, we propose a cycle anneal scheduler for dynamic timestep adjustment and an online momentum smoothing strategy to effectively mitigate oscillations during skill transitions, resulting in more stable and superior performance. Extensive comparison experiments across six challenging robotic manipulation tasks demonstrate that DiffSkill consistently outperforms state-of-the-art methods by a significant margin in all downstream tasks. Ablation studies and additional discussions further validate the effectiveness of each component and strategy.

Trajectory tracking control of wearable upper limb rehabilitation robot based on Laguerre model predictive control

Wearable rehabilitation robots have become an important auxiliary tool in rehabilitation therapy, providing effective rehabilitation training and helping to recover damaged muscles and joints. In response to the difficulty of traditional control methods in solving various constraints in the trajectory tracking process of the Upper Limb Rehabilitation Robot (ULRR), this study uses model predictive control to study the trajectory tracking problem of the upper limb rehabilitation robot. Firstly, based on the Lagrangian dynamic model of wearable rehabilitation robots, an extended state space model with pseudo linearization of the system was established. Given the performance indicators and various constraints of the system, a corresponding model predictive controller is designed based on the Laguerre model to ensure system performance while greatly reducing the computational complexity of predictive control. Secondly, the stability of the model predictive controller is demonstrated, and a disturbance observer is introduced into the controller to achieve compensation for slow-varying perturbations; a joint space sliding mode variable is also introduced to achieve simultaneous tracking of the joint’s desired position and desired velocity. Finally, taking a planar two bar robot as an example, comparative simulation verification was conducted on unconstrained joint trajectory tracking and constrained joint trajectory tracking. The simulation results show that the model predictive controller can achieve simultaneous tracking of joint expected trajectory and expected speed while meeting various constraints. It has good effects in improving patient motion control ability and reducing patient fatigue, providing new research ideas and methods for the field of rehabilitation therapy.

Analysis of the PID Controller Parameters for A Surface Mobility Platform Mobile Robot

Surface mobility control is a fundamental challenge in robotics, and its applications range from autonomous vehicles to industrial automation. The main aim of the paper is to analyze the PID controller parameters for the speed control of a surface mobility platform mobile robot. MATLAB simulation toolbox and mathematical model of the DC motor are used to find the optimal PID controller parameters to complete the DC motor speed control system. For the SMP motion control system, the mobile robot kinematic model is supported as a differential drive system. In this paper, the PID controller parameters for the DC motor control system are analyzed with simulation results using various KP, KI, KD gains. The experimental results of the SMP mobile robot with optimal PID controller parameters are analyzed by driving on the rough, slope and even terrains. According to simulation results and experimental tests, the proposed system effectively controls the speed of the SMP mobile robot, demonstrating satisfactory performance in settling time, rise time, and system overshoot. Among PI and PID controllers, the designed PID controller can quickly control to reach the desired DC motor speed. Using the optimal PID coefficient parameters, all motors reached their desired speed in 0.12 seconds during simulations. In the experimental tests on various terrain (even, slope and rough), all motors achieved their desired speed simultaneously within 25 seconds.Keywords: Differential drive, PID controller, motion trajectory, real-time speed control. skid steering, SMP mobile robot, surface mobility control

Dynamic Modeling and Tracking Of 3-Wheels Omni-Directional Mobile Robot with Manual PID Tuning Method

This paper presents the dynamic modelling and tracking control of a holonomic 3-wheel omni-directional mobile robot. A proportional integral derivative (PID) controller is used, and its performance depends on the dynamic model's accuracy. The controller design for the switched non-linear system is based on both a dynamic model and a kinematic design. A proposed dynamic model averages the contact radius of the 3-wheels. The resultant dynamic model, on average, is non-linear and smooth, serving as a potential solution for model-based control design. The closed-loop simulation results clearly illustrate the dynamic characteristics of the 3-wheels omni-directional mobile robot, offering valuable insights into its behaviour for further analysis and optimisation. Comparative simulation results using MATLAB are presented and demonstrate the effectiveness and legitimacy of the control mechanism. Keywords: Holonomic, kinematic, dynamic model, PID controller, non-linear, omni-directional robot

A Novel Flying Robot Swarm Formation Technique Based on Adaptive Wireless Communication using MUSIC Algorithm

This paper presents a novel technique to address the challenge of coordinating swarm flying robots in a leader-follower configuration. A combination of the Multi Signal Classification (MUSIC) estimation algorithm, based on a wireless MIMO array antenna, along with onboard robot control are used for precise route tracking of an individual robot. Employing an array antenna reduces energy consumption for followers in passive mode and reduces computational complexity when measuring the angles of leader angle interferences, which depends on the phase difference of the impinging signal on the antenna elements of the array. Additionally, the angles estimation and beamforming processes, utilizing MUSIC algorithm, form an inner loop that furnishes orientation angles in 3D (Azimuth and elevation angles) for both the leader and potential interference sources. The outer loop, contingent on the onboard controller and the robot's GPS system, enabling fine adjustments in angle and position relative to the leader's location. The simulation results illustrated the efficiency of the proposed technique in estimating the orientation angles of the leader and the interference sources. The technique robustness is confirmed through testing the performance on different trajectories. Where the follower perfectly generates a main radiation beam directed towards the leader, effectively mitigates interference signals from neighboring group leaders, and successfully tracks the leader path.

Stable Rapid Sagittal Walking Control for Bipedal Robot Using Passive Tendon

This paper presents the development, control, and experimental validation of a novel bipedal robot with a passive tendon. The robot, featuring foldable legs, coaxial actuation, and compact folded size, is endowed with a leg configuration with a five-bar mechanism. Based on biological observations of human walking, a passive artificial tendon made of emulsion is fabricated to work in conjunction with a tensioning device, providing adaptive heel touchdown and toe push-off in sync with single-leg movement. The tailored control framework for the bipedal robot is further established with the double-layer architecture. The regulation layer employs the linear inverted pendulum (LIP) model to generate reference trajectory of the center of mass (CoM) with a dead-beat style of parameter adjustment. An inverse-dynamics-based whole-body controller (WBC) is applied to enforce the full-order dynamics of the bipedal robot to reproduce the LIP model’s behavior. We carry out the experiments on the physical prototype to evaluate the walking performance of the developed bipedal robot. The results show that the robot achieves stable walking at the speed of 0.8 m/s (almost twice the leg length/s) and exhibits robustness to external push disturbance.

Impedance-based null-space control of redundant torque-controlled robot

PurposeThe null-space projection method is commonly adopted for controlling redundant robots, which undoubtedly requires the robot Jacobian matrix inverse. This paper aims to provide a novel control scheme, which enables null-space control of redundant robots without conflict with the main task space.Design/methodology/approachIn this paper, an impedance-based null-space control approach for redundant robots is proposed. The null-space degrees of freedom are separated from the primary task space by using the eigenvalue decomposition. Then, a joint impedance controller spans the null space and is reflected into the joint space to manage the redundancy. Finally, several experiments have been conducted to evaluate and validate the performance of the proposed approach in comparison with the null-space projection method under various situations.FindingsExperiment results show that no significant differences were observed between the different filling eigenvalues in the proposed approach under different null-space dimensions and motion velocity. Besides, comparative experiment results demonstrate that the proposed method can achieve comparable results to the null-space projection method. Nevertheless, the suggested approach has benefits regarding the quantity of control parameters in addition to not requiring a Jacobian inverse. Notably, the performance of the proposed method will improve as the null-space dimension increases.Originality/valueThis study presents a new control method for redundant robots, which has advantages for dealing with the problems of controlling redundant robots compared to the existing methods.

Effects of robot rehabilitation on the motor function and gait in children with cerebral palsy: a systematic review and meta-analysis.

This study was to determine the effects of robot rehabilitation on motor function and gait in children with cerebral palsy (CP) and the effect of robot type. Inclusion criteria were children with any type of CP, robot rehabilitation studies, non-robot rehabilitation comparison groups, outcomes related to motor function and gait, and randomized controlled trials. PubMed, Embase, Cochrane Library, CINAHL, and Web of Science databases were searched. Risk of bias was assessed using physiotherapy evidence database. Seven studies with a total of 228 participants were selected. Motor function was significantly improved in three studies comparing robot rehabilitation and control groups (standard mean difference [SMD], 0.79; 95% confidence intervals [CIs], 0.34-1.24; I 2=73%). Gait was not significantly improved in five studies comparing robot rehabilitation and control groups (SMD, 0.27; 95% CI, -0.09 to 0.63; I 2=45%). When comparing effects by robot type, robotic-assisted gate training (RAGT) showed significant improvements in both motor function (SMD, 0.89; 95% CI, 0.36-1.43; I 2=77%) and gait (SMD, 0.62; 95% CI, 0.12-1.11; I 2=44%). Robot rehabilitation effectively improved motor function, and among the robot types, RAGT was found to be effective in improving motor function and gait.

Hybrid Nonlinear Model Predictive Motion Control of a Heavy-duty Bionic Caterpillar-like Robot

This paper investigates the motion control of the heavy-duty Bionic Caterpillar-like Robot (BCR) for the maintenance of the China Fusion Engineering Test Reactor (CFETR). Initially, a comprehensive nonlinear mathematical model for the BCR system is formulated using a physics-based approach. The nonlinear components of the model are compensated through nonlinear feedback linearization. Subsequently, a fuzzy-based regulator is employed to enhance the receding horizon optimization process for achieving optimal results. A Deep Neural Network (DNN) is trained to address disturbances. Consequently, a novel hybrid controller incorporating Nonlinear Model Predictive Control (NMPC), the Fuzzy Regulator (FR), and Deep Neural Network Feedforward (DNNF), named NMPC-FRDNNF is developed. Finally, the efficacy of the control system is validated through simulations and experiments. The results indicate that the Root Mean Square Error (RMSE) of the controller with FR and DNNF decreases by 33.2 and 48.9%, respectively, compared to the controller without these enhancements. This research provides a theoretical foundation and practical insights for ensuring the future highly stable, safe, and efficient maintenance of blankets.

Embedded technique-based formation control of multiple wheeled mobile robots with application to cooperative transportation

The cooperative transportation problem for multiple wheeled mobile robots (WMRs) via formation control is investigated in this paper. A formation control algorithm based on an embedded technique is proposed for cooperative transportation of a shared object using n-WMRs. Instead of relying on the conventional design philosophy directly based on formation errors, the proposed algorithm is divided into two parts by considering the communication topology and WMRs’ dynamics “separately”. The first part involves a distributed signal generator that generates desired trajectories for the WMRs based on their initial positions, the formation vector, and the desired trajectory of the object. The second part consists of tracking controllers to enable the WMRs to track their desired trajectories. The proposed algorithm is distributed and differs from the existing cooperative transportation algorithms, as it eliminates the requirement for all WMRs to know the object’s position. Moreover, it exhibits remarkable compatibility and features a concise modular design. With the proposed algorithm, WMRs achieve formation in finite time. Theoretical proof supports the effectiveness of the proposed algorithm, which is further validated through several conducted experiments.

Design and motion control of a tendon-driven continuum robot for aerospace applications

Continuum robots are flexible and compliant. Compared to the case in conventional articulated manipulators, the driving unit can be placed outside the workspace of the robot, so that the motion orientation has a relatively complete linear configuration flow, which can be applied to a special environment with narrow and multiple obstacles such as aerospace. This study presents the development process of a tendon-driven continuum robot (TCR) with a high length-diameter ratio. The skeleton structure which imitates a snake is composed of continuous joints in series. The driving device is operated by using a tendon-driven method, which reduces the complexity of the driving box and control system significantly. The diameter of the robot is designed to be 5 mm, which enables it to work in a narrow and slender space with certain flexibility. Subsequently, a kinematic model of the robot is established. The mode function backbone method is applied to realize TCR trajectory planning. An idea of segmented solving is adopted to achieve trajectory tracking control of the continuum robot. Finally, a prototype of the continuum robot is produced, and the rationality of the robot design and the effectiveness of the motion control method are verified through trajectory simulations and experiments. The robot can perform inspection tasks within a narrow gap of 20 mm with good environmental adaptability.

GAN-BodyPose: Real-time 3D human body pose data key point detection and quality assessment assisted by generative adversarial network

With the rapid advancement of deep learning and computer vision, these technologies are becoming increasingly vital in areas like virtual reality, medical diagnosis, and sports training. Existing methods for real-time 3D human body pose keypoint detection and quality assessment face significant challenges such as insufficient detection accuracy, low computational efficiency, and high data quality requirements. To address these challenges, we propose an innovative solution, GAN-BodyPose. This approach integrates 3D convolutional neural networks, self-attention mechanisms, and generative adversarial networks to deliver efficient and accurate detection and assessment in real time. The GAN-BodyPose framework combines 3D-CNN and self-attention for effective feature extraction and keypoint detection, enhanced further by generative adversarial networks for superior data quality and accuracy. Our extensive evaluations using a large-scale 3D human body pose dataset demonstrated that GAN-BodyPose outperforms traditional methods, showing improvements in processing speed (15% faster), accuracy in terms of Mean Per Joint Position Error (reduced by approximately 2.2%), and an Area Under the Curve (AUC) score increased by approximately 9.5% compared to HR-Net and other datasets. Additionally, it achieves lower Floating-Point Operations (FLOPs) by about 9.3%, indicating more efficient computational performance. These advancements underline the potential of our approach to significantly enhance user experiences in virtual reality, motion capture, and other real-time applications. The successful application of GAN-BodyPose promises greater efficiency and precision in fields ranging from game development to medical diagnostics, and robust support for human-computer interaction and gesture recognition. This research represents a substantial contribution to deep learning applications in robot control, decision-making, and broadens the research foundation in these domains.

Rules-reduced fuzzy neural network-based learning control for multiple constraints robots using online identification and compensation methods

This paper presents a novel fuzzy-learning adaptive control approach for uncertain robotic systems with input dead zone and output saturation constraints. To address the input dead zone and output saturation and improve the control stability of the robot, an internal model compensation method was proposed, which utilizes online identification of an arctanh function to approximate the output saturation of actuators. The paper also introduces an adaptive learning control system based on a rules-reduced fuzzy neural network (RRFNN) algorithm, which considers the dead zone and output saturation function to enhance control performance, and an adaptive law driven by approximation mistakes is employed to handle multiple constraints. Furthermore, the controller utilizes the integral Lyapunov stability theorem and RRFNN to design the fuzzy-learning adaptive control law, ensuring global convergence and stability of the control system. Extensive simulations and experiments on a robotic manipulator are conducted to verify the feasibility of the proposed control method. Without the proposed method, the robot’s joint tracking error exceeds 0.5 rad, while with the proposed controller, it is less than 0.05 rad and does not violate output saturation constraints. Thus, the proposed method can realize the desired performance and overcome multiple constraints.

Robot control based on motor primitives: A comparison of two approaches

Motor primitives are fundamental building blocks of a controller which enable dynamic robot behavior with minimal high-level intervention. By treating motor primitives as basic “modules,” different modules can be sequenced or superimposed to generate a rich repertoire of motor behavior. In robotics, two distinct approaches have been proposed: Dynamic Movement Primitives (DMPs) and Elementary Dynamic Actions (EDAs). While both approaches instantiate similar ideas, significant differences also exist. This paper attempts to clarify the distinction and provide a unifying view by delineating the similarities and differences between DMPs and EDAs. We provide nine robot control examples, including sequencing or superimposing movements, managing kinematic redundancy and singularity, control of both position and orientation of the robot’s end-effector, obstacle avoidance, and managing physical interaction. We show that the two approaches clearly diverge in their implementation. We also provide a real-robot demonstration to show how DMPs and EDAs can be combined to get the best of both approaches. With this detailed comparison, we enable researchers to make informed decisions to select the most suitable approach for specific robot tasks and applications.