Tracked Mobile Robot Research Articles

RESEARCH ON IMPACT LOADING OF A MOBILE TRACKED PLATFORM WITH AN AUTONOMOUS STABILIZATION SYSTEM

The work presents a developed 3D model of a mobile robotic platform with an autonomous stabilization system for reconnaissance operations. Mobile robotic platforms have been found to be widely used in intelligence because they can help gather information from dangerous and hard-to-reach places and reduce the risk to the lives of intelligence personnel. Unmanned aerial vehicles and mobile robotic platforms with tracks are mainly used in reconnaissance. An important distinguishing quality of tracked mobile robots is their maneuverability. Having an independent drive for each of the tracks separately, the mobile robot can easily change the direction of its own movement. A study on shock loads (external influences on the system that can cause deformation, damage or destruction) of a mobile tracked platform with an autonomous stabilization system was conducted and the obtained results were described. The SOLIDWORKS software environment was used to model the design of the mobile platform. For numerical studies on impact loads, it is advisable to use linear elastic isotropic materials and bilinear Mises plastic materials. The results of the simulation of the movement of the mobile platform of the developed structure and the stresses in the collision with different speeds of 1, 5, 10, 25 m/s and the platform material carbon steel and rubber are presented. It was established that when a mobile platform collides with speeds of up to 25 m/s, the maximum stresses do not exceed 1.85 MPa, which gives a sufficient margin of strength in the event of impact loads for the selected material of the mobile platform. The use of steel inserts allows you to reduce the value of the maximum contact stresses under impact load, but increases the cost of the mobile platform, increases the total weight and, accordingly, reduces the operating time from the installed power batteries.

A novel adaptive robust control for trajectory tracking of mobile robot with uncertainties

The main objective of this paper is to handle the trajectory tracking control for a class of three-wheel mobile robot by using a novel adaptive robust control. The mobile robot consists of nonholonomic constraints and uncertainties. The uncertainties include initial condition offset, mass, and moment of inertia of the system, which are time-varying and bounded (unknown). The control force in analytical form is obtained by UK theory, and the adaptive robust control is formulated to handle the uncertainties. For estimating the unknown bounds of uncertainties, the leakage-type adaptive law is designed, which can automatically adjust its own value according to the trajectory tracking errors. Then we verify the uniform boundedness and uniform ultimate boundedness of the proposed control by Lyapunov method. Finally, numerical simulations are conducted to show the trajectory tracking effectiveness of the designed control method.

English

This paper presents the research and construction of a motion tracing control system for omnidirectional mobile robots based on reinforcement learning techniques in automatic control. The process of controlling a mobile robot in a flat environment with definite and unknown obstacles, taking into account the nonlinear factor of interference. Research and application of programming tools are operating systems for mobile robots (Robot Operating System - ROS). From updated information on maps, operating environment, robot control position, and obstacle identification (SLAM) to calculate the movement trajectory of a three-wheeled omnidirectional mobile robot. The positioning system calculates the orbital tracking for the robot based on the Q-learning algorithm. The results of simulation research in the Gazebo environment and running tests on real Turtlebot mobile robots have shown the practical effectiveness of the research problem of tracking motion tracking and intelligent navigation for mobile robots.

Motion Tracking of Four-Wheeled Mobile Robots in Outdoor Environments Using Bayes’ Filters

Motion Tracking of Four-Wheeled Mobile Robots in Outdoor Environments Using Bayes’ Filters

Explicit Identification of Pointwise Terrain Gradients for Speed Compensation of Four Driving Tracks in Passively Articulated Tracked Mobile Robot

Tracked mobile robots can overcome the limitations of wheeled and legged robots in environments, such as construction and mining, but there are still significant challenges to be addressed in terms of trajectory tracking. This study proposes a kinematic strategy to improve the trajectory-tracking performance of a PASTRo (Passively Articulated Suspension based Track-typed mobile robot), which comprises four tracks, two rockers, a differential gear, and a main body. Due to the difficulties in explicitly identifying track-terrain contact angles, suspension kinematics is used to identify track-terrain contact angles (TTCA) in arbitrarily rough terrains. Thus, the TTCA-based driving velocity projection method is proposed in this study to improve the maneuverability of PASTRo in arbitrarily rough terrains. The RecurDyn-Simulink co-simulator is used to examine the improvement of PASTRo compared to a tracked mobile robot non-suspension version. The results indicate that PASTRo has a 33.3% lower RMS(Root Mean Square) distance error, 56.3% lower RMS directional error, and 43.2% lower RMS offset error than the four-track skid-steer mobile robot (SSMR), even with planar SSMR kinematics. To improve the maneuverability of PASTRo without any information on the rough terrain, the TTCA is calculated from the suspension kinematics, and the TTCA obtained is used for both TTCA-based driving velocity projection methods. The results show that PASTRo, with the TTCA-based driving velocity projection method, has a 39.2% lower RMS distance error, 57.9% lower RMS directional error, and 51.9% lower RMS offset error than the four-track SSMR.

Non-Linear Prediction-Based Trajectory Tracking for Non-Holonomic Mobile Robots

Non-Linear Prediction-Based Trajectory Tracking for Non-Holonomic Mobile Robots

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Mobile Robot Tracking with Deep Learning Models under the Specific Environments

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.

Trajectory Tracking of Mobile Robot Based on Improved Hierarchical Sliding Mode Control

A control algorithm based on PD control and non-singular terminal hierarchical sliding mode control is proposed for tracking control of mobile robot systems. First establish a dynamic model of mobile robot under cartesian coordinate system, the system can be divided into subsystems of posture and position, attitude subsystem by using PD control, location subsystem using non-singular terminal hierarchical sliding mode control, the introduction of the system state higher order term, improve the system in the convergence speed and stability of the sliding surface, the system reaches a steady state in a limited time; Finally, the stability of the system is proved theoretically, and the effectiveness of the control method is verified by simulation.

A Study on Object Detection and Tracking of a Mobile Robot Using CIE L^* a^* b^* Color Space

Autonomous vehicles are increasingly used in daily life and industrial applications. Mobile robot technologies lead to autonomous architectures in these areas. The path planning methods of mobile robots contain differences in the purpose they realize. This trajectory planning from a determined starting point to the target point brings many techniques from image processing to artificial intelligence. In the study, an application with a unique design has been carried out on the tracking of circular objects with different diameters and colors by a mobile robot. Moving object is detected with CIE L^* a^* b^* color space with RGB-D camera by utilizing the ROS server-client architecture. The mobile robot tracks the detected object at a certain distance at a constant speed. Image filtering parameters are processed by the mobile robot in the Matlab environment together with the publisher-subscriber parameters. Thus, two circular objects with different colors, detected because of image processing and determined beforehand, are continuously followed by the mobile robot at a certain speed. Experiments were carried out using different diameter, size tolerance and color parameters in the image depending on the CIE L^* a^* b^* color space.

Air–ground trajectory tracking for autonomous mobile robot based on model predictive hybrid tracking control and multiple harmonics time‐varying disturbance observer

AbstractThis paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time‐varying disturbance observer for a discrete‐time dynamics nonholonomic autonomous mobile robot (AMR) with disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air–ground cooperative, a hybrid tracking control strategy combined with improved model predictive control (MPC) method is presented. First, a time‐varying air‐ground cooperative tracking control model based on the nonholonomic constraints AMR and UAV is established by polar coordinate transformation. Second, to estimate disturbances of the time‐varying model, a multiple harmonics disturbance observer with time‐varying gains is designed. A hybrid tracking control scheme for the AMR based on the estimated states and MPC method with relaxing factor and kinematics constraints is proposed. Finally, experimental results show the effectiveness of the proposed control strategy.

Leader-Following Formation Tracking Control of Nonholonomic Mobile Robots Considering Collision Avoidance: A System Transformation Approach

In this paper, the obstacle avoidance problem-based leader–following formation tracking of nonholonomic wheeled mobile robots with unknown parameters of desired trajectory is investigated. First, the under-actuated system is transformed into a fully-actuated system by obtaining an auxiliary control variable using the transverse function. Second, by introducing a potential function for each obstacle, the influence of obstacles is considered in trajectory tracking, and the effect of the potential field on mobile robots is taken into account in the system tracking error. Third, the adaptive laws are designed to estimate the unknown parameters of the desired trajectory. Fourth, the results show that the formation error with respect to the actual position and orientation can be arbitrarily small by selecting appropriate design parameters. Finally, simulation examples are used to demonstrate that the proposed control scheme is effective.

Research on Obstacle Avoidance and Environment Adaptability of Snake Robot Based on Deep Learning

With the development and popularization of computer artificial intelligence technology, more and more intelligent machines are gradually produced. These intelligent machines have brought great convenience to people's lives.This paper studies the control method of snake robots based on environment adaptability, which mainly explains the construction and stability of multi-modal CPG model. In addition, this paper also studies the trajectory tracking and dynamic obstacle avoidance of mobile robot based on deep learning.

Trajectory Tracking and Obstacle Avoidance for Wheeled Mobile Robots Based on EMPC With an Adaptive Prediction Horizon.

This article develops an event-triggered model-predictive control (EMPC) strategy to realize trajectory tracking and obstacle avoidance for a wheeled mobile robot (WMR) subject to input constraints and external disturbances. In the EMPC strategy, a potential field is introduced in the cost function to guarantee a smooth path for the WMR. An event-triggered mechanism is designed to reduce the computational load of solving an optimal control problem (OCP). Moreover, an adaptive prediction horizon is utilized to further achieve computation reduction. Both recursive feasibility of the OCP and practical stability of the resulting closed-loop system are analyzed for the WMR with the input constraints and the external disturbances. Simulation results are provided to demonstrate the effectiveness and superiority of the proposed EMPC strategy.

Optimal trajectory tracking control for a wheeled mobile robot using backstepping technique

<span lang="EN-US">This work studies an optimal trajectory tracking of a wheeled mobile robot with the objective of minimizing energy consumption. First, the mathematical model, which takes into account the kinematic model of the mobile robot and the dynamic model of the actuators is presented. Then, a backstepping controller is designed and its parameters are tuned to satisfy several strict criteria such as rapid convergence, matching desired trajectory, and minimizing energy. For that, two cost functions were investigated and the best one has been selected. The significant reduction in energy losses achieved for all the proposed motion scenarios proves the effectiveness of our approach.</span>

Discrete open-closed-loop PID-type iterative learning control for trajectory tracking of tracked mobile robots

In this article, a robust discrete-time open-closed-loop proportion integral differential (PID) -type iteration learning control (ILC) algorithm is developed for the high-precision trajectory tracking control of tracked mobile robots (TMRs) with external disturbances and noises. The proposed ILC algorithm adopts the past, current, and predictive learning error items of the former and current iterations to correct the current control input variables, which finally converges to the desired trajectory through continuous iterative learning. The convergence characterization of the algorithm for TMRs under both external disturbances and noises is carried on rigorous mathematical proof. Numerical simulations and physical experiments are provided to verify the feasibility and effectiveness of the algorithm. The comparative results of two ILC algorithms indicate that the tracking performance of the proposed ILC algorithm is superior to the traditional PID-type ILC algorithm in terms of tracking accuracy and convergence rate.

Adaptive Tracking and Stabilization of Nonholonomic Mobile Robots With Input Saturation

This article investigates the control problems of tracking and stabilization simultaneously for nonholonomic mobile robots subjected to input constraints and parameter uncertainties. A saturated time-varying controller is developed by applying a novel error state modification with bounded auxiliary variables. To improve the estimation performance of unknown kinematic and dynamic parameters, two projection-type adaptation laws are presented, while radial basis function approximations are used to estimate uncertain dynamics. Simulations are conducted to verify the effectiveness of the proposed controller.

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety.

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collision-avoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collision-avoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.

Trajectory Tracking of Swing-Arm Type Omnidirectional Mobile Robot

In order to enhance the omnidirectional maneuverability of outdoor wheeled mobile robots, a mobile robot driven by a common wheel is proposed, which realizes four-wheel steering (4WS), oblique motion mode, and spot turn modes according to different power distribution and angle of leg rotation. A dual-mode hybrid controller is designed based on the oblique and 4WS modes, in which multipoint prescanning trajectory tracking control is used for the oblique mode, and incremental linear time-varying model predictive control (ILTV-MPC) is used for the 4WS mode. The constraints required for the dual-mode controller are obtained by experimenting with the test prototype’s transverse and longitudinal tracking performance. A joint MATLAB/CARSIM simulation platform is used to compare and verify the performance of each mode controller under two operating conditions. In the first condition, the tracking accuracy of the 4WS mode can be achieved by using the oblique motion mode, and the transverse sway angle variation is significantly reduced. In the second working condition, the oblique motion mode is better than the tracking effect of the 4WS mode overall. The results show that the oblique motion mode can be used instead of the 4WS mode for trajectory tracking under a small-curvature reference trajectory.