Motion Control Of Mobile Robot Research Articles

Control of a three-wheeled omnidirectional mobile robot via a mixed FTB/$$\mathcal H_\infty $$ approach

AbstractIn this paper, the problem of guidance and motion control of mobile robots is addressed and solved within the novel framework of the mixed finite-time/$$\mathcal H_\infty $$ H ∞ control theory of nonlinear quadratic systems (NLQSs). Starting from a NLQS describing the dynamics of omnidirectional mobile platforms, the main tasks performed for controlling in closed loop the motion of omnidirectional robots can be conveniently formulated as a mixed finite-time/$$\mathcal H_\infty $$ H ∞ control problem. A robust motion controller, which can effectively rejects disturbances deviating the robot platform from a planned path, can be designed after choosing a linear state-feedback structure for the controller. The synthesis problem is solved through some sufficient conditions contemplating both norm-bounded disturbances and sets constraining initial and terminal conditions, together with a finite-time bound on the output transient. Therefore, for all the allowable uncertainties, in presence of nonzero initial conditions and exogenous disturbance inputs which are possible within an unstructured environment, the motion control tasks can be accomplished through optimal $$\mathcal H_\infty $$ H ∞ performance by simultaneously guaranteeing that the NLQS, which governs in closed loop the robot platform, is finite-time bounded. Finally, the applicability and control performance of the design approach have been evaluated through numerical simulations.

Trajectory tracking for non-holonomic mobile robots: A comparison of sliding mode control approaches

This paper implements and compares the performance of three controllers based on Sliding Mode Control (SMC) applied to the motion control of mobile robots. The controllers include a standard SMC controller and two variations, namely Dynamic Sliding Mode Control (D-SMC) and Dual Sliding Mode Control (DM-SMC). The three controllers differ from conventional SMC approaches since they are designed based on a generic reduced-order empirical model that can represent any system that exhibits behavior akin to a First-Order Plus Delay Time (FOPDT) system. In this study, the comparison of controllers focuses on the Trajectory Tracking Problem (TTP) of a Nonholonomic Mobile Robot (NMR). Performance indices are employed to quantify the controllers' results across three trajectory types. Simulation results evidence that if a smooth curvature trajectory is tracked, the best performance (in terms of minimizing trajectory tracking error and controller effort) can be obtained by using a DM-SMC. On the other hand, if there are discontinuities in the curvature, the results suggest that a better alternative is the D-SMC since it compensates for abrupt changes as in the case of tracking a square trajectory. Overall, the findings from this work demonstrated that the use of simplified empirical models to design SMC-based motion controllers can be successfully applied to solve the TTP of NMR.

Controller synthesis against omega‐regular specifications: A funnel‐based control approach

AbstractThe paper focuses on the problem of formal synthesis of controllers for control‐affine nonlinear systems against complex properties. Our goal is to design a closed‐form control policy that guarantees the satisfaction of complex properties that are expressed using ()‐regular languages and equivalently recognized by nondeterministic Büchi automata (NBA). We propose leveraging a funnel‐based control approach to provide a closed‐form solution to the problem. Our approach decomposes the specification represented by NBA into a sequence of reachability problems, which we solve using a funnel‐based control approach. Controllers associated with each reachability problem are then combined to design a hybrid control policy enforcing the desired ()‐regular property. We demonstrate the effectiveness of the proposed results on room temperature control and mobile robot motion control case studies.

Modeling of Control System for Tracked Mobile Robot Taking Into Account Kinematic and Dynamic Parameters

The paper examines the problem of constructing a motion control system for autonomous mobile tracked robots in an informal external environment. Based on the proposed mathematical model of the control system for a tracked mobile robot, which takes into account kinematic and dynamic parameters, simulation modeling of a tracked mobile robot was carried out in the dynamic modeling environments of technical systems MATLAB Simulink and SimInTech, which made it possible to control the coordinates of a tracked mobile robot along a predetermined trajectory with a certain accuracy. To increase the stability of the mobile robot motion control system, a PID controller of the armature current and electromagnetic torque was introduced into it. During the simulation study, graphical dependences on time were obtained: supply voltage; rotation angle of the robot body; track speeds; motor armature current; electromagnetic torque of engines; armature current of motors with PID controller; the path traveled by the caterpillars; electromagnetic torque of motors with a PID controller, and also the center of mass of the robot was set when setting a trajectory with a radius of 10 m for 6.2 s. Models were built in the MATLAB Simulink software package: general simulation, kinematic simulation and dynamic simulation of a tracked mobile robot, simulation subsystem of the electric drive control unit. In the SimInTech software environment, a simulation model of the dynamic part of the right electric drive of a tracked mobile robot was obtained. A comparative analysis of the graphical dependencies of the angular velocity of the roller and armature current of the motor of a tracked mobile robot, obtained in the MATLAB Simulink and SimInTech packages, was carried out, which revealed a number of advantages and disadvantages when testing the operation of the control system of a tracked mobile robot in an unformalized external environment.

Multiple mobile robots motion control based on screw theory for aircraft panel assembly

As a crucial component of large aircraft, the assembly efficiency of aircraft skins directly impacts production efficiency. To achieve efficient manufacturing of aircraft skins, this paper proposes a multiple mobile robot control algorithm based on screw theory. The robot arm is integrated into a rail or AGV to increase its motion space, creating a mobile robot assembly system. To address the redundant degrees of freedom problem caused by the mobile manipulator, this paper adopts the screw theory to describe the motion of multiple robots. Furthermore, to ensure the constraint of the motion between multiple robots, this paper proposes a multi-robot control method based on screw constraint. Rigid body constraints are assigned to the end of each mobile manipulator, and the motion is decomposed to the mobile platform and the robot arm. Finally, the cooperative motion control of multiple mobile manipulators is realized. The proposed algorithm is applied in the multi-mobile manipulator cooperative aircraft panel assembly task, achieving efficient assembly of aircraft panel and long truss.

Motion planning and control of an autonomous mobile robot

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.

Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network

Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.

Behavior adaptation for mobile robots via semantic map compositions of constraint-based controllers.

Specifying and solving Constraint-based Optimization Problems (COP) has become a mainstream technology for advanced motion control of mobile robots. COP programming still requires expert knowledge to transform specific application context into the right configuration of the COP parameters (i.e., objective functions and constraints). The research contribution of this paper is a methodology to couple the context knowledge of application developers to the robot knowledge of control engineers, which, to our knowledge, has not yet been carried out. The former is offered a selected set of symbolic descriptions of the robots' capabilities (its so-called "behavior semantics") that are translated in control actions via "templates" in a "semantic map"; the latter contains the parameters that cover contextual dependencies in an application and robot vendor-independent way. The translation from semantics to control templates takes place in an "interaction layer" that contains 1) generic knowledge about robot motion capabilities (e.g., depending on the kinematic type of the robots), 2) spatial queries to extract relevant COP parameters from a semantic map (e.g., what is the impact of entering different types of "collision areas"), and 3) generic application knowledge (e.g., how the robots' behavior is impacted by priorities, emergency, safety, and prudence). This particular design of, and interplay between, the application, interaction, and control layers provides a structured, conceptually simple approach to advance the complexity of mobile robot applications. Eventually, industry-wide cooperation between representatives of the application and control communities should result in an interaction layer with different standardized versions of semantic complexity.

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

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

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

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

Enhanced Reduced-Order Extended State Observer for Motion Control of Differential Driven Mobile Robot.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control.

Due to the complexity of autonomous mobile robot's requirement and drastic technological changes, the safe and efficient path tracking development is becoming complex and requires intensive knowledge and information, thus the demand for advanced algorithm has rapidly increased. Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning and motion control. Among those, motion control is a vital part of a fast, secure operation. Yet, current approaches face problems in managing unstructured gain data and producing accurate local planning due to the lack of formulation in the knowledge on the gain optimization. Therefore, this research aims to design a new gain optimization approach to assist researcher in identifying the value of the gain's product with a qualitative comparative study of the up-to-date controllers. Gains optimization in this context is to classify the near perfect value of the gain's product and processes. For this, a domain controller will be developed based on the attributes of the Fuzzy-PID parameters. The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). It is expected that the domain controller will give a positive impact to the path planning position and angular PID controller algorithm that meet the autonomous demand.

Pedestrian Trajectory Prediction Based on SOPD-GAN Used for the Trajectory Planning and Motion Control of Mobile Robot

Pedestrian Trajectory Prediction Based on SOPD-GAN Used for the Trajectory Planning and Motion Control of Mobile Robot

Robot Navigation in Complex Workspaces Using Conformal Navigation Transformations

Navigation functions provide both path and motion planning, which can be used to ensure obstacle avoidance and convergence in the sphere world. When dealing with complex and realistic scenarios, constructing a transformation to the sphere world is essential and, at the same time, challenging. This work proposes a novel transformation termed the conformal navigation transformation to achieve collision-free navigation of a robot in a workspace populated with obstacles of arbitrary shape. The properties of the conformal navigation transformation, including uniqueness, invariance of navigation properties, and no angular deformation, are investigated, which contribute to the solution of the robot navigation problem in complex environments. Based on navigation functions and the proposed transformations, feedback controllers are derived for the automatic guidance and motion control of kinematic and dynamic mobile robots. Moreover, an iterative method is proposed to construct the conformal navigation transformation in a multiply-connected workspace, which transforms the multiply-connected problem into multiple simply connected problems to achieve fast convergence. In addition to the analytic guarantees, simulation studies verify the effectiveness of the proposed methodology in workspaces with non-trivial obstacles.

Estimation of impulse action parameters using a network of neuronlike oscillators

Aim of the study is to develop a method for estimating the parameters of an external periodic impulse action using a spiking network of neuronlike oscillators. Methods. The spiking activity of a network consisting of coupled nonidentical neuronlike FitzHugh–Nagumo oscillators was studied, depending on the parameters of the periodic impulse action. To estimate the amplitude of the external impulse signal, we detuned the FitzHugh–Nagumo oscillators, which were in a stable state of equilibrium in the absence of an external action, by the threshold parameter responsible for the excitation of the oscillator. To estimate the frequency of excitatory pulses, we detuned the FitzHugh–Nagumo oscillators by the parameter characterizing the ratio of time scales, the value of which determines the natural frequency of oscillators. We also changed the duration of external pulses. Results. It is shown that the number of spikes generated by a network of nonidentical FitzHugh–Nagumo oscillators has a monotonic dependence on the amplitude of the external pulse signal and a nonmonotonic dependence on the frequency of the pulse signal. The number of spikes generated by the network remains constant over a wide range of external pulse durations. A method for estimating the amplitude and frequency of impulse action is proposed. The method efficiency is demonstrated in numerical simulations and in a radio physical experiment. Conclusion. The proposed method allows one to estimate the amplitude of an external pulse signal, knowing its frequency, and estimate the frequency of this signal, knowing its amplitude. The method can be used in robotics when solving the problems of information processing related to the motion control of mobile robots.

A robust intelligent controller-based motion control of a wheeled mobile robot

In this paper, an adaptive intelligent controller is developed for the velocity-tracking problem of a nonholonomic wheeled mobile robot (WMR) in the presence of external disturbances and measurement noises. The whole control system is consisting of two subsystems, where each subsystem has its own control responsibility. In this way, first, a kinematic controller is implemented according to the kinematic model of the robot, and then a dynamic controller is designed based on the characteristic of the robot dynamics. Our focus is designing and developing an adaptive fractional-order fuzzy logic proportional–integral–derivative (FOFPID) controller for the trajectory-tracking task in a two-WMR. Unlike the prevalent works which only designed the scaling factors of FOFPID, a simultaneous optimization of fuzzy membership functions and controller coefficients are realized to improve the efficiency of the WMR dynamic controller. Accordingly, the controller parameters are optimally adjusted by employing a combination of the sin cos algorithm and harmony search, called SCA-HS. To validate the applicability of the suggested framework, experimental studies are also conducted on a real-time platform using a two-WMR prototype. The experimental results confirm the effectiveness of the proposed controller for the exact trajectory-tracking problem in the presence of disturbances and noises.

Global path planning based on a bidirectional alternating search A* algorithm for mobile robots

While the A* algorithm has been widely investigated and applied in path planning problems, it has outstanding issues such as long calculation time, large turning angles, and the unsmoothed path in large task spaces. Aiming at overcoming these problems, an improved A* algorithm is proposed in this work. First, a bidirectional alternating search (BAS) strategy is introduced into the A* algorithm; the forward and backward path lists use the current path node in the opposite list as the target node to alternately search for paths until the paths meet, which improves the search efficiency of the algorithm. Then, the heuristic function is weighted via exponential attenuation, which overcomes the shortcomings of the BAS-A* algorithm. Finally, the filtering function of path nodes is introduced to reduce redundant nodes in the path, thereby effectively reducing the turning angle. Moreover, the use of Bézier curves fulfills the requirements of smooth path planning, which is critical for the motion control of mobile robots. Simulations of path planning on maps of different sizes were performed, and the proposed algorithm was compared with that of the A* algorithm, the genetic algorithm (GA) and the simulated annealing algorithm (SA) in terms of the computation time, path length, and turning angle. The results reveal that the proposed algorithm can solve the robot path planning problem more efficiently and smoothly than the other algorithms mentioned above. Additionally, the practicability of the proposed algorithm is validated on the TurtleBot3 Waffle Pi mobile robot.

Use of Remote Access Technologies and WebLabs as Teaching-Learning Strategy in Engineering

This work presents the use of technological tools and remote laboratories developed in order to teaching process control in engineering courses. The proposal is to demonstrate tools that can be used to allow teaching mediated by technologies, with their benefits and limitations, considering the need to carry out practical activities to complement the theoretical classes aiming at better assimilation of the contents for an effective learning. Furthermore, an overview of projects using remote laboratories developed are presented, such as: “WebLab for Speed ??and Position Control of DC Motor”, “WebLab for Mobile Robot Motion Control”, “WebLab for Temperature Control of a Didactic Oven" and finally a "Level Control System with Remote Web Access". This last remote laboratory will be detailed showing the didactic resources used, the available functionalities, the experiments available and the first steps for those who want to implement a remote laboratory for process control.

An experimental study of two predictive reinforcement learning methods and comparison with model-predictive control

Reinforcement learning (RL) has been successfully used in various simulations and computer games. Industry-related applications, such as autonomous mobile robot motion control, are somewhat challenging for RL up to date though. This paper presents an experimental evaluation of predictive RL controllers for optimal mobile robot motion control. As a baseline for comparison, model-predictive control (MPC) is used. Two RL methods are tested: a rollout Q-learning, which may be considered as MPC with terminal cost being a Q-function approximation, and a so-called stacked Q-learning, which in turn is like MPC with the running cost substituted for a Q-function approximation. The experimental foundation is a mobile robot with a differential drive (Robotis Turtlebot3). Experimental results showed that both RL methods beat the baseline in terms of the accumulated cost, whereas the stacked variant performed best. Provided the series of previous works on stacked Q-learning, this particular study supports the idea that MPC with a running cost adaptation inspired by Q-learning possesses potential of performance boost while retaining the nice properties of MPC.

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.