Robot Motion Control Research Articles

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

The application of Deep Learning in the field of Robotics

Abstract. As a new field with rapid development in the past two decades, deep learning has received more and more attention from researchers. Neural network adopts widely interconnected structure and effective learning mechanisms to simulate the process of information processing in the human brain, which is an important method in the development of artificial intelligence. Compared with shallow models, deep learning can achieve stronger unsupervised autonomous learning capabilities by increasing the number of layers of the network. In the past, the robots needed to be controlled by the humans; they controlled the robot to finish different tasks. In recent years, the researchers have tried to apply deep learning in the field of robots. Deep neural networks can simulate the complex cognitive laws of the human brain, so many researchers apply deep learning to robots to make robots have the thinking ability of humans. This article will review the cases of combining robot and in-depth learning in recent decades, Summarizing the achievements and problems faced in the three fields of robot vision, trajectory planning and motion control.

Motion controller for multi-joint robotic arm with deep cascade gated Bayesian broad learning system

Intelligent controllers based on the broad learning system can simplify the process of model parameter adjustment, finding wide applications in the motion control of multi-joint robotic arms. However, motion controllers for multi-joint robotic arms based on broad learning system exhibit insufficient precision and overlook the impact of joint motion commonalities on controller design. Therefore, this paper proposes a novel motion control strategy for a multi-joint robotic arm based on a deep cascade feature-enhancement gated Bayesian broad learning system. Firstly, the motion controller of the deep cascade feature-enhancement Bayesian broad learning system is constructed to enhance the robotic arm motion control precision. Secondly, an incremental node generation module with an attention-gated mechanism is constructed to capture the unique motion characteristics of the target joints, which is further combined with model generalization to simplify the motion control process of the multi-joint robotic arm. Finally, controller convergence is enhanced by combining it with the Lyapunov theory to constrain the learning parameters. Simulations and physical experiments are designed to verify the feasibility and superiority of the proposed motion control strategy. The results demonstrated that the strategy improved the accuracy of robotic arm motion control, with the root mean square error in position tracking reduced to 0.0019 rad. This represents a 93.39% reduction in error compared to existing techniques.

Research on Bionic Robot Motion Control Based on Reinforcement Learning

This research explores the application of reinforcement learning (RL) to enhance the motion control of bio-inspired robots across various environments. Focusing on underwater, terrestrial, and aerial robotic models, the study integrates model-free Q-learning, Deep Q Network (DQN), State-Action-Reward-State-Action (SARSA), and Double Deep Q Network (DDQN) algorithms. These methods are employed to achieve adaptive and efficient movement strategies without relying on pre-defined environmental models. The methodologies range from real-time data capture using live camera feeds for terrestrial robots to simulating aquatic and flight dynamics in controlled environments. Experimental results confirm the efficacy of these RL methods, demonstrating significant improvements in the robots' ability to adapt to dynamic and unknown environments, optimize movement efficiency, and navigate complex scenarios. The findings suggest a promising direction for future robotic applications, emphasizing the need for further research on optimizing these algorithms and expanding their real-world applicability. This study highlights the potential of RL to revolutionize robotic motion control, making robots more versatile and capable in varied and unpredictable settings.

Development of mathematical modeling of a mobile robot's motion control system

This study focuses on developing a mathematical model for precise control and stabilization of unmanned aerial vehicles (UAVs) in various spatial conditions. Addressing the problem of achieving precise control and stability, the proposed solution designs a control system based on a linear-quadratic controller (LQR) and simulates it using a proportional-derivative (PD) controller implemented in Matlab/Simulink. The results demonstrate high precision and stability in controlling the UAV motion parameters – roll, pitch, yaw, and altitude. This important level of performance is achieved due to the adaptivity of the LQR-based control system, which optimizes control actions according to the unsteady dynamics of the UAV. The integration of the PD controller improves responsiveness and stability, providing precise motion control over a range of spatial states. These features effectively solve the problem by handling the complex dynamics of the UAV and providing precise control. The results are explained by the ability of the LQR to provide optimal control laws that minimize deviations using a quadratic cost function, while the PD controller quickly corrects errors and responds to disturbances. The benefits of this approach include a significant reduction in control errors by about 25–30 %, increased response speed to external disturbances, and reduced computational latency due to efficient processing compared to more resource-intensive methods such as model predictive control. The developed mathematical model can be applied in practice in conditions requiring robust real-time control and adaptation to dynamic changes in the environment. It is especially suitable for industries such as logistics, surveillance, and environmental monitoring, providing an effective and optimal solution for stabilizing and controlling the motion of UAVs in various spatial states. This approach improves the performance of UAVs and expands their capabilities in various operating conditions.

A Star‐Nose‐Inspired Bionic Soft Robot for Nonvisual Spatial Detection and Reconstruction

The star‐nosed mole is recognized as a tactilely sensitive mammal due to its unique nose, which facilitates spatial detection in dark environments through touch using its appendages enveloped in numerous sensory receptors. This article introduces a bionic soft robot inspired by the star‐nosed mole, which combines a pneumatic soft platform with a polydimethylsiloxane–polyethylene terephthalate cylindrical tactile sensor array based on bilayer single‐electrode triboelectric nanogenerators, mimicking the muscle tissue of the mole's nose and the cylindrical appendages surrounded by Eimer's organs. The cylindrical sensor array enables multiangle spatial detection without an external power supply and remains unaffected by external materials. By implementing a constant curvature model for robot motion control, positional information is provided for the contact points between the cylindrical sensor array and the external environment. The robot effectively discriminates the distance and shape of various objects and achieves nonvisual 3D spatial detection and reconstruction in real‐world scenarios. This work presents a novel bionic approach for 3D spatial detection in nonvisual environments.

A novel dynamic tracking method for coded targets with complex background noise

To address the issues of low tracking efficiency and poor localization accuracy of artificial coded targets under complex background interference conditions, a new method for dynamic tracking of coded targets is proposed. This method includes a lightweight feature tracker (CBAM-Slim-Net) for adaptive localization of coded circles and a large-capacity coded target solver (CSN-BSSCT). The CBAM-Slim-Net feature tracker achieves a detection accuracy of 0.987 with only 6.030 M parameters. In actual measurement environments with complex background interference, CSN-BSSCT can decode quickly and accurately, with a mean error of 0.036 mm in the three-dimensional Euclidean distance between coded circles in static measurement scenarios. Additionally, this method can analyze the motion trajectory of the target and perform dynamic stitching for 3D measurement from multiple perspectives, making it highly significant for applications in robot motion control and large-field-of-view 3D measurement.

Motion control of legged robots based on gradient central pattern generators

Motion control of legged robots based on gradient central pattern generators

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

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

3D Model Prototype of Robotic Arm and Motion Control Simulation on 4-DOF Based on Virtual Reality

Penggunaan robotika dalam industri modern telah menjadi krusial, terutama dalam otomatisasi dan manufaktur. Robot lengan, sebagai elemen kunci, memerlukan pengembangan dan pengujian prototipe yang teliti untuk mencapai performa optimal. Teknologi Virtual Reality (VR) muncul sebagai solusi inovatif untuk mensimulasikan gerakan robot lengan di lingkungan virtual, memungkinkan visualisasi dan interaksi mendalam dalam berbagai derajat kebebasan (DOF). Penelitian ini bertujuan untuk membangun prototype model robot lengan 3D dan mengerjakan eksplorasi virtualisasi gerakan dari pengembangan prototipe robot lengan berbasis VR. Hasil penelitian ini diantaranya telah dilakukan integrasi teknologi VR dengan sistem kontrol robot lengan dalam lingkungan Unity, memungkinkan pengguna mengendalikan gerakan robot lengan dalam ruang 3D secara real-time. Studi ini menunjukkan bahwa robot lengan dengan 4 DOF dapat mencapai fleksibilitas dan presisi yang diinginkan pada skenario uji yang telah dirancang. Dari hasil pengujian antara parameter simulasi dari lingkungan unity dan perhitungan secara teoritis gerakan robot lengan yang dikendalikan menggunakan controller, diperoleh rentang koordinat yang dijangkau adalah x = -19,85 cm hingga 28,53 cm, y = 21,69 cm hingga 52,04 cm, z = -15,35 cm hingga -24,87 cm. Setelah dilakukan perhitungan perbandingan antara hasil pengujian di Unity dan hasil perhitungan berdasarkan rumus, didapatkan selisih koordinat sebesar x = 0,080, y = 0,038, dan z = 0,034

Conductive hydrogels‐based self‐sensing soft robot state perception and trajectory tracking

AbstractSoft robots face significant challenges in proprioceptive sensing and precise control due to their highly deformable and compliant nature. This paper addresses these challenges by developing a conductive hydrogel sensor and integrating it into a soft robot for bending angle measurement and motion control. A quantitative mapping between the hydrogel resistance and the robot's bending gesture is formulated. Furthermore, a nonlinear differentiator is proposed to estimate the angular velocity for closed‐loop control, eliminating the reliance on conventional sensors. Meanwhile, a controller is designed to track both structural and nonstructural trajectories. The proposed approach integrates advanced soft sensing materials and intelligent control algorithms, significantly improving the proprioception and motion accuracy of soft robots. This work bridges the gap between novel material design and practical control applications, opening up new possibilities for soft robots to perform delicate tasks in various fields. The experimental results demonstrate the effectiveness of the proposed sensing and control approach in achieving precise and robust motion control of the soft robot.

Human-redundant robot interaction and redundancy utilization based on three-dimensional force sensor

To make full use of redundant characteristics, the inverse kinematics algorithm based on augmented Jacobian matrix with the goal of avoiding joint limit and obstacle is adopted. Then, admittance control system is established for human-robot interaction, and force control is converted into motion control of robot. Through the expansion of Jacobian matrix, the model of joint limit avoidance is established. Regarding obstacle avoidance in human-robot interaction process, the potential function gradient is calculated and augmented Jacobian matrix is formed. Thus, comprehensive control model of human-robot interaction and obstacle avoidance is established. When the external force is applied, the robot can move smoothly along the human hand. The robot's motion process is smooth and compliant. At the same time, when performing task of avoiding joint limit, the values of the joint angle are within the set range. The average angle margin of the four joints is over 60 %. So the goal of avoiding the joint limit is achieved. When adding obstacle avoidance in the interaction control, the difference value between the minimum distance and safe distance is always positive. The safety margin of the distance is over 20 %. So the robot does not contact with the obstacle. So human-robot interaction and redundancy utilization can be realized simultaneously.

Research of Bio-Inspired Motion Control in Robotics

Research of Bio-Inspired Motion Control in Robotics

An improved preconditioned conjugate gradient method for unconstrained optimization problem with application in Robot arm control

AbstractThis work suggests improved conjugate gradient methods for enhancing the efficiency and robustness of the classical conjugate gradient methods. The study modifies the diagonal of the inverse Hessian approximation of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi‐Newton update in order to build a preconditioner for nonlinear conjugate gradient (NCG) methods applied to large‐scale unconstrained optimization problems. Damping techniques were embedded into the algorithm to impose the positive definiteness of the diagonal approximation. This made the methods easy to use and presented a viable way to increase the effectiveness of unconstrained optimization techniques. Experimental findings from a collection of benchmark problems demonstrate the efficiency and robustness of the proposed method when compared to five other existing NCG algorithms. Moreover, the successful application of the algorithm to manipulate robotic planar motion control systems with 3 degrees of freedom has been demonstrated, highlighting the practicality of the proposed approach.

Design and Motion Control of Master-Slave Control Endotracheal Intubation Robot.

Master-slave remote control technology allows patients to be treated promptly during transport and also reduces the risk of contagious infections. Endotracheal intubation, guided by endoscopy and a master-slave system, enables doctors to perform the procedure efficiently and accurately. In this paper, we present the development of a master-slave controlled endotracheal intubation robot (EIR). It is based on operation incremental mapping, a weighted recursive average filtering method to reduce vibration, and a virtual fixture designed to reduce mishandling in minimally invasive surgery. Simulation analysis of the master-slave control demonstrates that the weighted recursive average filtering method effectively reduces vibration, while the virtual fixture assists in confining the operator's movement within a delimited area. Experimental validation confirms the validity of the robot's structural design and control method. The developed robot successfully achieves the necessary motion for endotracheal intubation surgery through master-slave control.

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

Experimental Study on the Longitudinal Motion Performance of a Spherical Robot Rolling on Sandy Terrain

To provide the necessary theoretical models of sphere–soil interaction for the structural design, motion control, and simulation of spherical robots, this paper derives analytical expressions for traction force and driving torque when spherical robots slide and sink into sandy terrain, based on terramechanics and multibody dynamics. Furthermore, orthogonal experimental analysis identifies the load, joint angular acceleration, and maximum joint angular velocity of spherical robots as influencing factors, highlighting that the load significantly affects their longitudinal motion performance. Experimental results indicate that rolling friction and additional resistance on sandy terrain cannot be ignored. The corrected theoretical model effectively replicates the temporal variation of driving torque exerted by spherical robots on sandy terrain. Numerical computations and experimental analyses demonstrate that increasing the radius of the sphere shell, the load, and the slip ratio all lead to increased traction force and driving torque. However, traction force and driving torque begin to decrease once the slip ratio reaches approximately 0.5. Therefore, in the design of spherical robot structures and control laws, appropriate parameters such as load and slip ratio should be chosen based on the established sphere–soil interaction theoretical model to achieve high-quality longitudinal motion performance on sandy terrain.

Interactive Path Editing and Simulation System for Motion Planning and Control of a Collaborative Robot

Robots in hazardous environments demand precise and advanced motion control, making extensive simulations crucial for verifying the safety of motion planning. This paper presents a simulation system that enables interactive path editing, allowing for motion planning in a simulated collaborative robot environment and its real-world application. The system includes a simulation host, a control board, and a robot. Unity 3D on a Windows platform provides the simulation environment, while a virtual Linux environment runs ROS2 for execution. Unity sends edited motion paths to ROS2 using the Unity ROS TCP Connector package. The ROS2 MoveIt framework generates trajectories, which are synchronized back to Unity for simulation and real-world validation. To control the six-axis Indy7 collaborative robot, we used the MIO5272 embedded board as an EtherCAT master. Verified trajectories are sent to the target board, synchronizing the robot with the simulation in position and speed. Data are relayed from the host to the MIO5272 using ROS2 and the Data Distribution Service (DDS) to control the robot via EtherCAT communication. The system enables direct simulation and control of various trajectories for robots in hazardous environments. It represents a major advancement by providing safe and optimized trajectories through efficient motion planning and repeated simulations, offering a clear improvement over traditional time-consuming and error-prone teach pendant methods.

PyIgH : A unified architecture of IgH EtherCAT Master based on Python considering hard real-time constraints

The increasing demand for rapid application development tools, especially those employing high-level languages such as Python, has underscored the importance of utilizing a wide array of popular libraries while addressing real-time constraints in distributed hardware systems. This paper introduces PyIgH, a unified architecture of an IgH EtherCAT master based on Python, specifically designed to satisfy hard real-time requirements in an EtherCAT network. Implemented as a Python module, PyIgH exposes the functionalities and capabilities of an open-source EtherCAT master, facilitating seamless configuration and control of EtherCAT slave devices within the Python runtime environment. Real-time adaptation of the POSIX library, encapsulated within Python, is also utilized to satisfy the timing requirements of EtherCAT. The feasibility of the proposed approach is verified by analyzing the real-time performance in terms of periodicity and in-controller delay of the EtherCAT control task with a 1 kHz cycle. Experimental results demonstrate that PyIgH is suitable for hard real-time applications and serves as a valid alternative to conventional low-level EtherCAT masters. Additionally, a practical application involving motion control of a six-axis collaborative robot showcases consistent performance of PyIgH within a real-time multi-tasking environment.

Virtual ecological landscape design of theme parks based on entertainment robots and VR devices

Currently, the design of virtual ecological landscapes in theme parks has become an innovative form of entertainment. This study aims to explore how to construct virtual ecological landscapes in theme parks through entertainment robots and VR devices, in order to provide an interactive entertainment experience between tourists and nature. We have built an outdoor sensing system and collected environmental data through sensors. Implement motion control of entertainment robots through programming, enabling them to interact according to environmental changes. Then, through information control and feedback technology, interaction with tourists is achieved. Analyze the motion trajectory of robots to optimize their performance, use virtual reality technology for design and rendering, achieve interactive effects through VR development platforms, and optimize VR devices through controlled simulation technology to enhance the virtual experience of tourists. Finally, combining the principles of ecological landscape design with landscape design techniques, applying VR design technology to achieve the design of virtual ecological landscapes in theme parks, considering the layout and combination of natural elements, in order to create virtual landscapes that are similar to real ecology. Through the application of VR technology, tourists can experience the landscape changes under different seasons and weather conditions, increasing the fun and realism of interaction. The results indicate that the virtual ecological landscape design of the theme park has been achieved through the combination of entertainment robots and VR devices. Tourists can obtain an immersive entertainment experience through interaction with robots and the application of virtual reality technology. Through robot trajectory analysis and ecological landscape recognition technology, the landscape design effect is optimized.