Holonomic Mobile Robot Research Articles

An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses.

Smooth and collision-free path planning for holonomic mobile robot based RRT-Connect

Robot navigation is a crucial aspect of any mobile robot system. Many path planners generate a path that is zigzag in nature and contains many sharp or angular turns. Such courses are not adequate for a mobile robot to follow as it has to slow down and then speed up at these sharp turns. Thus, the integration of the smoothing function is a vital aspect to generate smooth and continuous paths. Moreover, a collision check after path smoothing needs to be accounted for a possible collision with obstacles surrounding. Herein, a path pruning algorithm is utilized to reduce the total path waypoints. Then a piecewise cubic B-spline smoothing function is applied to ensure the path is smooth and continuous. In addition, a post-smoothing collision checking and improvement algorithm based on mid-point insertion for the collide segments is proposed. Experiments and comparisons with the geometric RRT-Connect path planner have shown that the inclusion of a path pruning algorithm, smoothing function and post-smoothing collision check generates better results with reduced path length.

Learning Observation Model for Factor Graph Based-State Estimation Using Intrinsic Sensors

The navigation system of autonomous mobile robots has appeared challenging when using exteroceptive sensors such as cameras, LiDARs, and radars in textureless and structureless environments. This paper presents a robust state estimation system for holonomic mobile robots using intrinsic sensors based on adaptive factor graph optimization in the degradation scenarios. In particular, the neural networks are employed to learn the observation and noise model using only IMU sensor and wheel encoder data. Investigating the learning model for the holonomic mobile robot is discussed with various neural network architectures. We also explore the neural networks that are far more powerful and have cheaper computing power when using the inertial-wheel encoder sensors. Furthermore, we employ an industrial holonomic robot platform equipped with multiple LiDARs, cameras, IMU, and wheel encoders to conduct the experiments and create the ground truth without a bulky motion capture system. The collected datasets are then implemented to train the neural networks. Finally, the experimental evaluation presents that our solution provides better accuracy and real-time performance than other solutions. <i>Note to Practitioners</i>&#x2014;Autonomous mobile robots need to serve in challenging environments robustly that deny extrinsic sensors such as cameras, LiDARs, and radars. In order to operate in this situation, the navigation system shall rely on the intrinsic sensor as inertial sensor and wheel encoders. Existing conventional methods have combined the intrinsic sensors in the form of the recursive Bayesian filtering technique without adapting these models. Besides, deep learning-based solutions have adopted extensive networks like LSTM or CNN to handle the estimation problem. This work aims to develop a state estimation subsystem of the navigation system for the holonomic mobile robots that utilize the intrinsic sensors in adaptive factor graph optimization. In particular, we present how to join the factor graph efficiently with the learning observation model of IMU and wheel encoder factor. Moreover, the neural networks are introduced to learn the observation model with an IMU and wheel encoder data inputs. We recognize that lightweight neural networks can achieve more power than deep learning techniques using the IMU sensor and wheel encoders. Finally, the neural networks are embedded in a factor graph to handle the smoothing state estimation. The proposed system could operate with high accuracy in real time.

Operational control with set-points tuning - application to mobile robots

This paper proposes a novel method for the optimal tuning of set points for multiple-layered control system structure widely seen in robotics and other complex industrial processes composed of a number of subsystems. The terminal sliding mode control (SMC) is used as the low-level control strategy to ensure the stability of subsystems. When uncertainties exist, it can be shown that the deteriorated system performance will be improved by the outer loop with set points tuning. For this purpose, the learning of the new set point is designed to compensate for the effects caused by uncertainties during the system operation. At the same time, the system is proven to stay with the original set point when the compensation is introduced. A practical application to a holonomic mobile robot system is given to illustrate the presented method. Desired results have been obtained.

Model Predictive Path Following Control without terminal constraints for holonomic mobile robots

In this paper, a model predictive path following control (MPFC) for holonomic mobile robots is considered. The MPFC is aimed to control a mobile robot to follow a geometric path, where the time evolution of the path parameterization is not fixed a priori. Instead, the time evolution is considered as an extra degree of freedom of the controller. Contrary to previous works, in this study, the closed-loop asymptotic stability of holonomic mobile robots under MPFC without terminal constraints or costs is rigorously proven and a stabilizing-horizon length is calculated. The analysis is based on verifying the cost-controllability assumption by deriving an upper bound of the MPFC value function with a finite prediction horizon. Then, using this bound, the length of a stabilizing prediction horizon is calculated. The analysis is performed in the discrete time settings and theoretical results are verified with numerical simulations as well as implementations on an actual mobile robot.

Drift compensation of a holonomic mobile robot using recurrent neural networks

Drift compensation of a holonomic mobile robot using recurrent neural networks

Model-based detection and isolation of the wheel slippage and actuator faults of a holonomic mobile robot

PurposeMobile robots may perform very critical tasks under difficult operating conditions. Faults encountered during their tasks may cause the task to be interrupted or failed completely. In the active fault tolerant control methods, it is very important not only to detect the faults that occur in the robot, but also to isolate these faults to develop a fault recovery strategy that is suitable for that specific type of fault. This study aims to develop a model-based fault detection and isolation method for wheel slippage and motor performance degradation that may occur in wheeled mobile robots.Design/methodology/approachIn the proposed method, wheel speeds can be estimated via the dynamic model of the mobile robot, which includes a friction model between the wheel and the ground. Four residual signals are obtained from the differences between the estimated states and the measured states of the mobile robot. Mobile robot’s faults are detected by using these signals. Also, two different residual signals are generated from the calculation of the traction forces with two different procedures. These six residual signals are then used to isolate possible wheel slippage and performance degradation in a motor.FindingsThe proposed method for diagnosing wheel slip and performance degradation in motors are tested by moving the robot in various directions. According to the data obtained from the test results, a logic table is created to isolate these two faults from each other. Thanks to the created logic table, slippage in any wheel and performance degradation in any motor can be detected and isolated.Originality/valueTwo different recovery strategies are needed to recover temporary wheel slippage and permanent motor faults. Therefore, it is important to isolate these two faults that create similar symptoms in robot’s general movement. Thanks to the method proposed in this study, it is not only possible to isolate the slipping wheel with respect to the non-slipping wheels or to isolate the faulty motor from the non-faulty ones, but also to isolate these two different fault types from each other.

Nonuniform Dual-Rate Extended Kalman-Filter-Based Sensor Fusion for Path-Following Control of a Holonomic Mobile Robot with Four Mecanum Wheels

This paper presents an extended Kalman-filter-based sensor fusion approach, which enables path-following control of a holonomic mobile robot with four mecanum wheels. Output measurements of the mobile platform may be sensed at different rates: odometry and orientation data can be obtained at a fast rate, whereas position information may be generated at a slower rate. In addition, as a consequence of possible sensor failures or the use of lossy wireless sensor networks, the presence of the measurements may be nonuniform. These issues may degrade the path-following control performance. The consideration of a nonuniform dual-rate extended Kalman filter (NUDREKF) enables us to estimate fast-rate robot states from nonuniform, slow-rate measurements. Providing these estimations to the motion controller, a fast-rate control signal can be generated, reaching a satisfactory path-following behavior. The proposed NUDREKF is stated to represent any possible sampling pattern by means of a diagonal matrix, which is updated at a fast rate from the current, existing measurements. This fact results in a flexible formulation and a straightforward algorithmic implementation. A modified Pure Pursuit path-tracking algorithm is used, where the reference linear velocity is decomposed into Cartesian components, which are parameterized by a variable gain that depends on the distance to the target point. The proposed solution was evaluated using a realistic simulation model, developed with Simscape Multibody (Matlab/Simulink), of the four-mecanum-wheeled mobile platform. This model includes some of the nonlinearities present in a real vehicle, such as dead-zone, saturation, encoder resolution, and wheel sliding, and was validated by comparing real and simulated behavior. Comparison results reveal the superiority of the sensor fusion proposal under the presence of nonuniform, slow-rate measurements.

Dynamic Consensus With Prescribed Convergence Time for Multileader Formation Tracking

This work addresses the problem of distributed formation tracking for a group of follower holonomic mobile robots around a reference signal. The reference signal is comprised of the geometric center of the positions of multiple leaders. This work&#x2019;s main contribution is a novel Modulated Distributed Virtual Observer (MDVO) for the reference signal. Moreover, the proposed MDVO is based on an exact dynamic consensus algorithm with a prescribed convergence time. In addition, we provide simulation examples showcasing two different application scenarios for the proposal.

Experimental study of path planning problem using EMCOA for a holonomic mobile robot

In this paper, a comparative study of the path planning problem using evolutionary algorithms, in comparison with classical methods such as the A&#x2217; algorithm, is presented for a holonomic mobile robot. The configured navigation system, which consists of the integration of sensors sources, map formatting, global and local path planners, and the base controller, aims to enable the robot to follow the shortest smooth path delicately. Grid-based mapping is used for scoring paths efficiently, allowing the determination of collision-free trajectories from the initial to the target position. This work considers the evolutionary algorithms, the mutated cuckoo optimization algorithm (MCOA) and the genetic algorithm (GA), as a global planner to find the shortest safe path among others. A non-uniform motion coefficient is introduced for MCOA in order to increase the performance of this algorithm. A series of experiments are accomplished and analyzed to confirm the performance of the global planner implemented on a holonomic mobile robot. The results of the experiments show the capacity of the planner framework with respect to the path planning problem under various obstacle layouts.

A New Algorithm for Calibration of an Omni-Directional Wheeled Mobile Robot Based on Effective Kinematic Parameters Estimation

Calibration is a strategy to compensate for the systematic errors of a mobile robot hence, to increase the accuracy of the robot localization. So far, various methodologies have been proposed for the calibration of wheeled robots, but the majority have focused on non-holonomic mobile robots, e.g., differential type, and holonomic ones have been less studied from this point of view. This paper presents an innovative approach for the calibration of a holonomic robot by introducing “Effective Kinematic Parameters” (EKPs). To estimate the EKPs of a holonomic mobile robot, some tests are proposed, and the variables of the inverse Jacobian equations are modified by defining and minimizing an appropriate cost function. In the following, a virtual holonomic robot is modeled with some unknown intentional errors in its parameters, which cause some error in its path tracking. Then, the methodology is applied to estimate its EKPs, and the simulation is fulfilled again. The results show incredible improvement in path tracking. The methodology is applied to a 3-wheeled Omni-directional mobile robot, and some experiments are performed in a laboratory environment to experimentally evaluate the approach. The outcomes approved the algorithm and showed great enhancement in the calibration of the robot compared to the previous researches. The methodology can be employed to calibrate other robotic systems as well.

Path Tracking Simulation of a Wheeled Mobile Robot with Three Mecanum Wheels

This paper presents a study of wheeled mobile robot WMR path tracking by presenting the kinematic model of holonomic wheeled mobile robot with three mecanum controlled wheels. The advantage of mecanum wheels usage is to make the mobile robot movement in lateral and longitudinal directions smooth and to improve the tracking ability to travel in every direction under any orientation. This proposed type of WMR has three actuated input coordinates (angular velocities of the mecanum wheels) and three generalized output coordinates (robot linear translation along x, y axes and the robot orientation). The kinematic models of the WMR are based on a functional relation among configuration variables and their derivatives. It is a known fact that the WMR poses can be achieved in the configuration space but the path to reach these poses has complex step in the consideration. The mobility capability in the proposed design of WMR makes it suitable for different types of applications. The contribution in this work is the initiative to obtain the mathematical models of inverse and forward kinematics in order to analyze a new design of WMR with three mecanum wheels to simplify the task implementation of the robot by obtaining the suitable path to reach the mobile robot target. It has been taken in consideration that, the WMR motion is assumed as a pure rolling without any slipping. Simulation example results presented in this work show the proposed control algorithm applied on two different cases of path tracking (circular path, infinity path) and the effect of WMR wheels angular velocities on the robot body velocities and robot position errors. The results show a good performance in minimizing the positioning errors during the implementation of its task.

Autonomous omnidirectional mobile robot navigation based on hierarchical fuzzy systems

PurposeThis paper aims to propose a one-layer Mamdani hierarchical fuzzy system (HFS) to navigate autonomously an omnidirectional mobile robot to a target with a desired angle in unstructured environment. To avoid collision with unknown obstacles, Mamdani limpid hierarchical fuzzy systems (LHFS) are developed based on infrared sensors information and providing the appropriate linear speed controls.Design/methodology/approachThe one-layer Mamdani HFS scheme consists of three fuzzy logic units corresponding to each degree of freedom of the holonomic mobile robot. This structure makes it possible to navigate with an optimized number of rules. Mamdani LHFS for obstacle avoidance consists of a number of fuzzy logic units of low dimension connected in a hierarchical structure. Hence, Mamdani LHFS has the advantage of optimizing the number of fuzzy rules compared to a standard fuzzy controller. Based on sensors information inputs of the Mamdani LHFS, appropriate linear speed controls are generated to avoid collision with static obstacles.FindingsSimulation results are performed with MATLAB software in interaction with the environment test tool “Robotino Sim.” Experiments have been done on an omnidirectional mobile robot “Robotino.” Simulation results show that the proposed approaches lead to satisfied performances in navigation between static obstacles to reach the target with a desired angle and have the advantage that the total number of fuzzy rules is greatly reduced. Experimental results prove the efficiency and the validity of the proposed approaches for the navigation problem and obstacle avoidance collisions.Originality/valueBy comparing simulation results of the proposed Mamdani HFS to another navigational controller, it was found that it provides better results in terms of path length in the same environment. Moreover, it has the advantage that the number of fuzzy rules is greatly reduced compared to a standard Mamdani fuzzy controller. The use of Mamdani LHFS in obstacle avoidance greatly reduces the number of involved fuzzy rules and overcomes the complexity of high dimensionality of the infrared sensors data information.

Geometric Landmark-based Pose Tracking for Holonomic Mobile Robots

Geometric Landmark-based Pose Tracking for Holonomic Mobile Robots

Model Predictive Control without terminal constraints or costs for holonomic mobile robots

We investigate Model Predictive Control (MPC) schemes without stabilizing constraints or costs for the set-point stabilization of holonomic mobile robots. Herein, we ensure closed-loop asymptotic stability using the concept of cost controllability. To this end, we derive a growth bound on the finite-horizon value function in terms of the running costs evaluated at the current state, which is then used to determine a stabilizing prediction horizon. In the discrete-time setting, we additionally show that asymptotic stability holds for the shortest possible prediction horizon. Moreover, we deduce a lower bound on the MPC performance on the infinite horizon. Theoretical results are verified by numerical simulations as well as laboratory experiments of stabilizing a holonomic mobile robot to a reference set point.

Online Bidirectional Trajectory Planning for Mobile Robots in State-Time Space

In this paper, we propose a computationally efficient online local motion planning algorithm for mobile robots in unknown cluttered dynamic environments. The algorithm plans a trajectory incrementally up to the finite horizon in state-time space. Incremental planning method is capable of fast computation but has poor obstacle avoidance performance. To compensate for the drawbacks of incremental planning, a partial trajectory modification scheme is used that sets an interim goal and then plans a trajectory to pass through the interim goal. The interim goal is a temporary desired goal to prevent the robot from falling into the inevitable collision state. By using incremental planning and partial trajectory modification, it is possible to plan collision-free trajectory with small computation even in cluttered dynamic environment. To generate smooth trajectories around given waypoints and goal, we systematize bidirectional trajectory planning with three kinds of trajectories: 1) a forward trajectory from the current robot state; 2) a backward trajectory from the state of current target waypoint; 3) and a connecting trajectory between the forward and backward trajectories. A smooth trajectory is generated around the way point and goal by setting the state of the current target waypoint (or goal), while taking into account the positional relationship among the planned forward trajectory, the target waypoint and the next waypoint. Performances of the proposed algorithm are validated through extensive simulations and experiment with two types of mobile robots: 1) a holonomic mobile robot and 2) a differential drive mobile robot.

Fuzzy Greedy RRT Path Planning Algorithm in a Complex Configuration Space

A randomized sampling-based path planning algorithm for holonomic mobile robots in complex configuration spaces is proposed in this article. A complex configuration space for path planning algorithms may cause different environmental constraints including the convex/concave obstacles, narrow passages, maze-like spaces and cluttered obstacles. The number of vertices and edges of a search tree for path planning in these configuration spaces would increase through the conventional randomized sampling-based algorithm leading to exacerbation of computational complexity and required runtime. The proposed path planning algorithm is named fuzzy greedy rapidly-exploring random tree (FG-RRT). The FG-RRT is equipped with a fuzzy inference system (FIS) consisting of two inputs, one output and nine rules. The first input is a Euclidean function applied in evaluating the quantity of selected parent vertex. The second input is a metaheuristic function applied in evaluating the quality of selected parent vertex. The output indicates the competency of the selected parent vertex for generating a random offspring vertex. This algorithm controls the tree edges growth direction and density in different places of the configuration space concurrently. The proposed method is implemented on a Single Board Computer (SBC) through the xPC Target to evaluate this algorithm. For this purpose four test-cases are designed with different complexity. The results of the Processor-in-the-Loop (PIL) tests indicate that FG-RRT algorithm reduces the required runtime and computational complexity in comparison with the conventional and greedy RRT through fewer number of vertices in planning an initial path in significant manner.

Design, Simulation, and Control of an Omnidirectional Mobile Robot

Nowadays, the design phase of any mechatronic system is mandatory before the implementation and the construction of a prototype. In the design phase, it is crucial to minimize failures, without under-dimensioning, any drive system in the desired prototype, using different simulation tools to test the proposed design under varied conditions.In this paper, the design, the simulation, and the control of a holonomic or omnidirectional mobile robot are presented. The design approach consists of a mathematically based method. The obtained model is an approach of the studied mobile robot, that allows an approach to the system’s behaviour, to a testing platform, and to a simulator for the desired robot, including most of the innerrobot’s interactions. The main contributions of this paper are the presentation and the study case of a proposed structured methodology for the study of any mechatronic-robotic system.

Model-Free Detection and Following of Moving Objects by an Omnidirectional Mobile Robot using 2D Range Data

Abstract The ability of mobile robots to detect and track moving targets is frequently considered as an important behavior that is a necessary pre-requisite while developing autonomous robots intended to coexist with humans or other dynamic objects in a shared environment. If the robot can track dynamic objects this is further simplifying the development of autonomous navigation and obstacle avoidance behaviors as well as the robot ability to operate within multi-agent formations. The approach proposed in this investigation allows a KUKA youBot omnidirectional mobile platform, equipped with a 2D LiDAR sensor to detect and follow a moving target (i.e. another mobile robot) in a shared indoor environment. It uses a nonparametric representation of the dynamic objects that is independent of their classes and shapes. The target following holonomic mobile robot is also maintaining a pre-specified relative position and orientation with respect to the tracked dynamic object by controlling its own linear and angular velocity.

κ-PMP: Enhancing Physics-based Motion Planners with Knowledge-Based Reasoning

Physics-based motion planning is a challenging task, since it requires the computation of the robot motions while allowing possible interactions with (some of) the obstacles in the environment. Kinodynamic motion planners equipped with a dynamic engine acting as state propagator are usually used for that purpose. The difficulties arise in the setting of the adequate forces for the interactions and because these interactions may change the pose of the manipulatable obstacles, thus either facilitating or preventing the finding of a solution path. The use of knowledge can alleviate the stated difficulties. This paper proposes the use of an enhanced state propagator composed of a dynamic engine and a low-level geometric reasoning process that is used to determine how to interact with the objects, i.e. from where and with which forces. The proposal, called \k{appa}-PMP can be used with any kinodynamic planner, thus giving rise to e.g. \k{appa}-RRT. The approach also includes a preprocessing step that infers from a semantic abstract knowledge described in terms of an ontology the manipulation knowledge required by the reasoning process. The proposed approach has been validated with several examples involving an holonomic mobile robot, a robot with differential constraints and a serial manipulator, and benchmarked using several state-of-the art kinodynamic planners. The results showed a significant difference in the power consumption with respect to simple physics-based planning, an improvement in the success rate and in the quality of the solution paths.