Autonomous Motion Control Research Articles

Vehicle dynamics analysis and adaptive control scheme design under coupled slope for trajectory tracking of four-wheel independent drive autonomous vehicles

Trajectory tracking is the core function of autonomous vehicle motion control. On variable road slopes, vehicles will encounter extra forces due to gravity, distinct from driving on flat roads. These forces will cause significant deviations in trajectory tracking and even instability, especially for four-wheel independent drive autonomous vehicles (4WIDAVs) with closely coupled longitudinal-lateral dynamics. To address above issues, first, a modified dynamics model considering coupled slopes is built, and the slope effect on vehicle motion and stability is analysed. Secondly, treating coupled slopes as augmented states, a square root cubature Kalman filter (SRCKF) is designed for real-time slope estimation. Then, a slope-adaptive model separate predictive trajectory tracking control strategy is proposed, decoupling the slope-adaptive tracking system into longitudinal and lateral subsystems. Predictive control is performed separately to ensure real-time performance, and prediction information is exchanged between subsystems to preserve dynamics coupled characteristics, enhancing tracking accuracy. Finally, the effectiveness of the proposed scheme is validated by simulations and real-vehicle experiment under the varying slope condition. Results show that the proposed scheme improves tracking accuracy by 17.59% and reduces computation time by 55.02% compared to existing scheme in simulations, and in experiment, the tracking error is reduced by 17.89%.

Observer‐based adaptive neural networks optimal control for spacecraft proximity maneuver with state constraints

AbstractThis article proposes an adaptive neural network (NN) optimal control approach for autonomous relative motion control of non‐cooperative spacecraft in proximity. The proposed method aims to minimize fuel consumption under various challenges including model uncertainty, state constraints, external disturbances, and input saturation. To account for uncertain parameters of non‐cooperative target and external disturbances, we start by designing a NN disturbance observer. Subsequently, a novel optimal control index function is presented. An adaptive NN based on the actor‐critic (A‐C) framework and backstepping theory is then utilized to approximate the solution of Hamilton–Jacobi–Bellman (HJB) equation and obtain an optimal control law. The Lyapunov framework is leveraged to establish the stability of the closed‐loop control system. Finally, numerical simulations are conducted to assess the feasibility and effectiveness of the proposed control scheme in comparison with an existing approach.

Ship Autonomous Berthing Strategy Based on Improved Linear-Quadratic Regulator

There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−Quadratic Regulator (LQR) control approach, reinforced by the Covariance Matrix Adaptation Evolution Strategy (CMA−ES), along with an adaptive berthing strategy decision model. This integrated framework encompasses ship motion control, path planning, and berthing strategy selection to facilitate adaptive and autonomous ship berthing. Initially, a dynamic mathematical model of ship motion is established, taking into account wind and current interference effects. Subsequently, an adaptive environment−aware berthing strategy model is introduced to enable automatic selection of berthing strategies based on spatial relationships between environmental factors and the berth. By utilizing the refined LQR method, autonomous motion control for ship berthing is achieved. To validate the effectiveness of our controller, comprehensive simulation analyses are conducted under varying operating conditions to encompass crucial factors such as large drift angle characteristics of ships, shallow water effects, and bank effects across seven diverse working conditions. The simulation results underscore the robustness of our proposed method in responding to environmental interference while demonstrating its capability to select appropriate berthing strategies based on varying operational scenarios.

R²NMPC: A Real-Time Reduced Robustified Nonlinear Model Predictive Control with Ellipsoidal Uncertainty Sets for Autonomous Vehicle Motion Control

In this paper, we present a novel Reduced Robustified NMPC (R²NMPC) algorithm that has the same complexity as an equivalent nominal NMPC while enhancing it with robustified constraints based on the dynamics of ellipsoidal uncertainty sets. This promises both a closed-loop- and constraint satisfaction performance equivalent to common Robustified NMPC approaches while drastically reducing the computational complexity. The main idea lies in approximating the ellipsoidal uncertainty sets propagation over the prediction horizon with the system dynamics’ sensitivities inferred from the last optimal control problem (OCP) solution and similarly for the gradients to robustify the constraints. Thus, we do not require the decision variables related to the uncertainty propagation within the OCP, rendering it computationally tractable. Next, we illustrate the real-time control capabilities of our algorithm in handling a complex, high-dimensional, and highly nonlinear system, namely the trajectory following of an autonomous passenger vehicle modeled with a dynamic nonlinear single-track model. Our experimental findings, alongside a comparative assessment against other Robust NMPC approaches, affirm the robustness of our method in effectively tracking an optimal racetrack trajectory while satisfying the nonlinear constraints. This performance is achieved while fully utilizing the vehicle's interface limits, even at high speeds of up to 37.5m s−1, and successfully managing state estimation disturbances. Our approach maintains a mean solving frequency of 140Hz. The code used in this research is publicly accessible as open-source software:https://github.com/bzarr/TUM-CONTROL

A task level fusion autonomous switching mechanism.

Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.

PID control based on machine learning algorithm in robot path optimization

Robot path optimization in the robotic arm, robot movement of autonomous navigation and motion control plays a great role, PID control is a traditional, simple and widely used control algorithm. in recent years, machine learning has become increasingly popular, and combining machine learning with PID control has become a potential way to optimize PID control deficiencies. However, the results of the research are scattered in different research teams and different research fields, and in order to integrate the existing research results, we have integrated and analyzed articles from different research teams and research fields. This paper aims to discuss the recent perspectives of robot path optimization based on machine learning algorithms and PID control, summarize the existing problems, research progress and challenges, and put forward the research directions and prospects that can be carried out in the future. In addition, the systematic analysis of research studies contained in this paper helps to consolidate the research results that are still in the final stage and to set the research agenda for future development. This article focuses on robotic arms.

Autonomous underwater vehicle motion state recognition and control pattern mining

Self-awareness of its own state during autonomous operation is critical for Autonomous Underwater Vehicles (AUVs) to execute tasks and monitor their health. Automated data analysis methods appear to be the most appropriate tool for attempting to understand and elucidate the motion states of AUVs. However, research scholars often focus on the state recognition of small AUVs used for shallow water detection, and the relevant conclusions cannot be directly applied to deep-sea AUVs. As a representative deep-sea AUV, Qianlong-3 is studied in this paper. In this study, a data-driven approach was employed to recognize the motion states and control modes of the Qianlong-3 AUV while retaining the original data noise. First, the class imbalance problem in the data was addressed, and then the data features were enhanced through a sliding window statistical method. By comparing the differences in the accuracy of motion state recognition after class imbalance processing using different supervised learning methods, the Random Forest (RF) algorithm was found to be the most suitable classification algorithm. In addition, the Toeplitz Inverse Covariance-Based Clustering (TICC) algorithm was used to recognize and mine the motion states and control modes of the AUV. The TICC algorithm was effective in extracting latent information from the actual data. Finally, through correlation analysis, the three motion states of the AUV were discussed, and the control modes for each of these states were determined. Overall, this study demonstrates the feasibility of relevant algorithms in practical deep-sea applications for state recognition and control mode mining. This provides valuable reference and experience for the development of future deep-sea AUV.

Manoeuvre decision‐making of unmanned aerial vehicles in air combat based on an expert actor‐based soft actor critic algorithm

AbstractThe demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial. Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy; however, limits their combat capabilities, which proves to be very challenging. To meet the challenge, this article proposes an autonomous manoeuvre decision model using an expert actor‐based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience. Specifically, the algorithm uses a small amount of expert experience to increase the diversity of the samples, which can largely improve the exploration and utilisation efficiency of deep reinforcement learning. And to simulate the complex battlefield environment, a one‐to‐one air combat model is established and the concept of missile's attack region is introduced. The model enables the one‐to‐one air combat to be simulated under different initial battlefield situations. Simulation results show that the expert actor‐based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster, and converge more quickly, compared with the soft actor critic algorithm.

Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

In this study, an autonomous driving system of a patient-transfer robot is developed. The developed autonomous driving system has a path-planning module and a motion-control module. Since the developed autonomous driving system is applied to medical robots, such as patient-transfer robots, the main purpose of this study is to generate an optimal path for the robot’s movement and to ensure the patient on board moves comfortably in the PTR. In particular, for the patient’s comfortable movement, a lower controller is needed to minimize the sway angle of the patient. In this paper, we propose a hybrid path-planning algorithm that combines the A-STAR algorithm as a global path-planning method and the AHP (Analytic Hierarchy Process)-based path-planning algorithm as a local path-planning method. In addition, model-based controllers are designed to move patient-transport robots along planned paths. In particular, the LQR controller with the Kalman filter is designed to be robust to the uncertainty and disturbance of the model including the patient. The optimal path generation and patient shaking angle reduction performance of the proposed autonomous driving system have been demonstrated via a simulation on a map that mimics a hospital environment.

Autonomous Motion Control of a Mobile Robot Using Marker Recognition via Deep Learning in GPS-Denied Environments

In this study, an autonomous traveling control system for a mobile robot was developed using the results for calculation of the relative positions and angles between the mobile robot and markers, based on the image information obtained from a camera mounted on the mobile robot. The mobile robot runs autonomously based on the path of the marker. However, as the conventional method uses OpenCV to identify the shape of the marker using the color information of the marker, the marker may be misrecognized owing to the influence of light. Furthermore, the specifications of the camera limit the detection distance of the marker placed opposite it, which inevitably limits the straight traveling distance of the mobile robot in the proposed method. The proposed method improves the accuracy of marker recognition by using deep learning, and also devises the method of placing markers that allows the user to move straight ahead over a longer distance. It can also easily achieve autonomous path travel control, including long-distance straight lines, for a mobile robot in an environment where global positioning systems (GPS) cannot be received. In addition, the system can be easily operated by an actual user, who need not have any knowledge of programming, because the travel path of the mobile robot can be set up simply by placing markers. The effectiveness of the proposed system was demonstrated through several experiments.

Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments

Unmanned aerial vehicles (UAVs) have been widely used in military warfare, and realizing safely autonomous motion control (AMC) in complex unknown environments is a challenge to face. In this paper, we formulate the AMC problem as a Markov decision process (MDP) and propose an advanced deep reinforcement learning (DRL) method that allows UAVs to execute complex tasks in different environments. Aiming to overcome the limitations of the prioritized experience replay (PER), the proposed asynchronous curriculum experience replay (ACER) uses multithreads to asynchronously update the priorities and assigns the true priorities to increase the diversity of experiences. It also applies a temporary pool to enhance learning from new experiences and changes the fashion of experience pool to first-in-useless-out (FIUO) to make better use of old experiences. In addition, combined with curriculum learning (CL), a more reasonable training paradigm is designed for ACER to train UAV agents smoothly. By training in a large-scale dynamic environment constructed based on the parameters of a real UAV, ACER improves the convergence speed by 24.66% and the convergence result by 5.59% compared to the twin delayed deep deterministic policy gradient (TD3) algorithm. The testing experiments carried out in environments with different complexities further demonstrate the strong robustness and generalization ability of the ACER agents.

Autonomous Localization and Motion Control of Under-Vehicle Inspection Robot

This paper focuses on the train under-vehicle inspection robot equipped with 3D solid-state LiDAR and laser distance sensor. Dedicated 3D Simultaneous Localization and Mapping (SLAM) framework and motion control algorithm are proposed for the robot operating in the hazardous environment of the inspection pit. The constrained working environment includes repetitive and monotonous spatial features makes autonomous localization and navigation task a challenging issue. The proposed SLAM localization framework has a complete architecture that includes frontend laser odometry and loop closure detection. The method optimized for 3D solid-state LiDAR is utilized for feature extraction. The pose optimization problem is initialized with the motion estimation provided by wheel odometry. The wheel odometry interpolation is utilized to increase the pose update frequency. Moreover, the features of the vehicle wheels in the measurements of laser distance sensor are utilized for loop closure detection. The proposed motion control algorithm extracts the side features of the inspection pit for feedback of angular velocity closed-loop control. The experimental results from simulation and real-world show that the robot can be driven accurately along a straight trajectory in the inspection pit with a maximum integrated localization and navigation error of less than 0.015m in movement over 200m, which demonstrates the effectiveness of the proposed algorithms.

Розроблення схеми та удосконалення методу управління рухом групи мобільних робототехнічних платформ

When managing a group of mobile robotic platforms, there are specific tasks of ensuring operational analysis and taking into account changes in the functioning parameters of each individual platform and the impact of the surrounding environment on it and the group as a whole. It is necessary to realize not only the coordinated management of a separate robotic platform but also to ensure the interaction of separate platforms in order to fulfill the task as a whole. At the same time, it is necessary to analyze the navigational state of the surrounding environment, the composition and coordinates of the platforms in the group, to keep track of the available resources necessary for the performed task. When performing complex tasks by a group of robots, it is necessary to take into account the possibility of losing individual robot during the execution of the task and the fact that each individual robot can perform relatively simple operations, which are determined by its characteristics (radius of action, energy resource, set of executive devices). Groups of mobile robotic platforms can be homogeneous or heterogeneous, which determines the peculiarities of their management. The hybrid management method, which is a combination of centralized and distributed, in the case of heterogeneous platforms, which is most often encountered in practice, should be considered the most adequate. Under the conditions of heterogeneity of platforms in the group, control algorithms should be implemented with unconditional consideration of the features and characteristics of each individual platform. The main requirements for the hybrid management of the robots group are to ensure: effective management of the robots group in real time; respond to changes in working conditions and the surrounding environment; implement various scenarios for achieving a common goal and fulfilling a common task; scaling the number of robots that need to be managed in the group; increasing the accuracy of movement control of each robot in the group. To implement the specified tasks, the method of controlling the movement of a group of mobile robotic platforms has been improved, which, by taking into account the changing parameters of the platforms and the changing state of the surrounding environment, provides effective management of the group of platforms in real time. A generalized scheme of the group management process has been developed, which ensures the adaptation of the group management process to the changing conditions of the surrounding environment. A block diagram of the autonomous motion control algorithm of a separate mobile robotic platform has been developed, which ensures its effective functioning taking into account the variable characteristics of the platform and the state of the environment.

The Autonomous Pipeline Navigation of a Cockroach Bio-Robot with Enhanced Walking Stimuli.

Tens of crawling bio-robots with cockroaches as the mobile platform have been developed with various functions. Compared with artificial crawling robots of the same size, they revealed better flexibility, larger payload, and stronger endurance. These features made bio-robots ideal for pipeline inspection scenarios because the advancements in locomotion mechanisms and efficient power systems are still hurdles for current artificial systems. In this study, we controlled the bio-robot to crawl in the confined dark pipeline and achieved autonomous motion control with the help of an onboard sensing system. Specifically, a micro-camera was mounted on the electronic backpack of the cockroach for image collection, and an IMU sensor was used to compute its body orientation. The electronic backpack transmitted images to the host computer for junction recognition and distance estimation. Meanwhile, the insect's habituation to electrical stimulation has long been an uncertain factor in the control of bio-robots. Here, a synergistic stimulation strategy was proposed to markedly reduce the habituation and increase the number of effective turning controls to over 100 times. It is also found that both the increase of payload and the application of stimulations could promote the metabolic rate by monitoring carbon dioxide release. With the integration of synergistic stimulation and autonomous control, we demonstrated the fully autonomous pipeline navigation with our cockroach bio-robot, which realized the cycle number of approximately 10 in a roll. This research provides a novel technology that has the potential for practical applications in the future.

Safe navigation of a quadrotor UAV with uncertain dynamics and guaranteed collision avoidance using barrier Lyapunov function

In this paper, the safe autonomous motion control of a quadrotor Unmanned Aerial Vehicle (UAV) is considered, in the presence of disturbance, stationary and moving obstacles. In this regard, we directly combine an analytical control design approach, within the backstepping framework, with obstacle avoidance to solve the navigation problem. A Barrier Lyapunov Function (BLF) is incorporated into the translational control to keep the vehicle out of a safety sphere, constructed around the obstacles, while steering it toward a desired position. BLF allows the direct inclusion of the obstacle position in the control design. This is achieved for both cases of known and unknown obstacle velocities. Furthermore, the issue of arbitrary initial conditions is analytically addressed, with a preassigned exit time from the safety sphere. We also consider the case of chance-constrained collision avoidance. The proposed approach leads to a computationally efficient design, since a closed-form of the control is obtained with no need for real-time optimization. More importantly, the analytical stability of closed-loop system is guaranteed. A hierarchical control structure is designed with an adaptive model-free control for unknown attitude dynamics in the presence of disturbances. A number of numerical simulations are performed to evaluate the effectiveness of the proposed approach.

The modularization design and autonomous motion control of a new baby stroller.

The increasing number of newborns has stimulated the infant market. In particular, the baby stroller, serving as an important life partner for both babies and parents, has attracted more attention from society. Stroller design and functionality are of vital importance to babies' physiological and psychological health as well as brain development. Therefore, in this paper, we propose a modularization design method for the novel four-wheeled baby stroller based on the KANO model to ensure the mechanical safety and involve more functionalities. Manual control of the baby stroller requires the rapid response of human motor systems in a completely controlled manner, which could be a potential risk. To enhance the safety and stability of the stroller motion, especially in situations where manual control is hard to achieve (e.g., sharp turns), we propose an autonomous motion control scheme based on model predictive control. Both the modularization design and the motion controller are verified in the MATLAB simulation environment through path tracking tasks. The feasibility is validated by the satisfactory experimental results with lateral position error in a reasonable range and good trajectory smoothness.

Measurement and comparison of data rate and time delay of end-devices in licensed sub-6 GHz 5G standalone non-public networks

The fifth mobile communications generation (5G) offers the deployment scenario of licensed 5G standalone non-public networks (NPNs). Standalone NPNs are locally restricted 5G networks based on 5G New Radio technology which are fully isolated from public networks. NPNs operate on their dedicated core network and offer organizations high data security and customizability for intrinsic network control. Especially in networked and cloud manufacturing, 5G is seen as a promising enabler for delay-sensitive applications such as autonomous mobile robots and robot motion control based on the tactile internet that requires wireless communication with deterministic traffic and strict cycling times. However, currently available industrial standalone NPNs do not meet the performance parameters defined in the 5G specification and standardization process. Current research lacks in performance measurements of download, upload, and time delays of 5G standalone-capable end-devices in NPNs with currently available software and hardware in industrial settings. Therefore, this paper presents initial measurements of the data rate and the round-trip delay in standalone NPNs with various end-devices to generate a first performance benchmark for 5G-based applications. In addition, five end-devices are compared to gain insights into the performance of currently available standalone-capable 5G chipsets. To validate the data rate, three locally hosted measurement methods, namely iPerf3, LibreSpeed and OpenSpeedTest, are used. Locally hosted Ping and LibreSpeed have been executed to validate the time delay. The 5G standalone NPN of Reutlingen University uses licensed frequencies between 3.7-3.8 GHz and serves as the testbed for this study.

A learning-based flexible autonomous motion control method for UAV in dynamic unknown environments

This paper presents a deep reinforcement learning (DRL)-based motion control method to provide unmanned aerial vehicles (UAVs) with additional flexibility while flying across dynamic unknown environments autonomously. This method is applicable in both military and civilian fields such as penetration and rescue. The autonomous motion control problem is addressed through motion planning, action interpretation, trajectory tracking, and vehicle movement within the DRL framework. Novel DRL algorithms are presented by combining two difference-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem. An improved Lyapunov guidance vector field (LGVF) method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV. In contrast to conventional motion-control approaches, the proposed methods directly map the sensorbased detections and measurements into control signals for the inner loop of the UAV, i.e., an end-to-end control. The training experiment results show that the novel DRL algorithms provide more than a 20% performance improvement over the state-of-the-art DRL algorithms. The testing experiment results demonstrate that the controller based on the novel DRL and LGVF, which is only trained once in a static environment, enables the UAV to fly autonomously in various dynamic unknown environments. Thus, the proposed technique provides strong flexibility for the controller.

A homogenization-planning-tracking method to solve cooperative autonomous motion control for heterogeneous carrier dispatch systems

Taxiing aircraft and towed aircraft with drawbar are two typical dispatch modes on the flight deck of aircraft carriers. In this paper, a novel hierarchical solution strategy, named as the Homogenization-Planning-Tracking (HPT) method, to solve cooperative autonomous motion control for heterogeneous carrier dispatch systems is developed. In the homogenization layer, any towed aircraft system involved in the sortie task is abstracted into a virtual taxiing aircraft. This layer transforms the heterogeneous systems into a homogeneous configuration. Then in the planning layer, a centralized optimal control problem is formulated for the homogeneous system. Compared with conducting the path planning directly with the original heterogeneous system, the homogenization layer contributes to reduce the dimension and nonlinearity of the formulated optimal control problem in the planning layer and consequently improves the robustness and efficiency of the solution process. Finally, in the tracking layer, a receding horizon controller is developed to track the reference trajectory obtained in the planning layer. To improve the tracking performance, multi-objective optimization techniques are implemented offline in advance to determine optimal weight parameters used in the tracking layer. Simulations demonstrate that smooth and collision-free cooperative trajectory can be generated efficiently in the planning phase. And robust trajectory tracking can be realized in the presence of external disturbances in the tracking phase. The developed HPT method provides a promising solution to the autonomous deck dispatch for unmanned carrier aircraft in the future.

Unmanned Powerboat Motion Terminal Control in an Environment with Moving Obstacles

The major point for consideration throughout this paper is controlling the motion of an unmanned powerboat in an obstructed environment with stationary and moving objects. It offers a procedure for the terminal control law development based on the powerboat programmed motion trajectory in a polynomial form and proposes position-trajectory-based control algorithms. A hybrid method based on virtual fields and unstable driving modes, taking into account powerboat speeds and obstacles, is used to plan motion trajectories for obstacle avoidance. There were experiments carried out to test the developed methods and algorithms meanwhile estimating the energy consumption for control, the length of the trajectory and the safety indicator for obstacle avoidance. The novelty of the proposed approach lies in the method used to develop a local movement trajectory in the field with obstacles and in the hybridization of trajectory scheduling methods. This approach allows us to achieve a given safe distance when avoiding obstacles and virtually eliminate the chances of an emergency collision. The presented results can be used in systems of boats autonomous motion control and allow safe stationary and dynamic obstacles avoidance.