Skid-steered Mobile Robot Research Articles

An incremental unsupervised learning based trajectory controller for a 4 wheeled skid steer mobile robot

This paper proposes a trajectory controller for a 4-wheel skid steering mobile robot designed for use in an oil palm plantation. The nature of the working environment requires adaptive control to eliminate noise and to learn necessary variations on-the-go. The proposed control system is based on the Enhanced Self Organizing Incremental Neural Network (ESOINN), and is able to produce exceptional trajectory control without the use of a kinematic / dynamic model of the mobile robot by training the network with measured trajectory data as well as simulated data by incremental learning. Our simulation results show that the ESOINN is able to adapt to new training samples and errors have been reduced after only a few iterations of incremental learning. The RMSE error of the output of the initial network was reduced by almost 50% after 3 stages of incremental learning. When comparing training times, ESOINN had a much faster computation time with each consecutive incremental learning instance as compared to other non-incremental methods such as self-organizing maps (SOM), K-means clustering and an adaptive Neural Network. In addition, ESOINN produced improved performance after each consecutive stage of learning, proving its reliability, unlike the other mentioned methods, which gave varied performance during each stage.

TOWARDS CONTINUOUS CONTROL FOR MOBILE ROBOT NAVIGATION: A REINFORCEMENT LEARNING AND SLAM BASED APPROACH

Abstract. We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG). Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid-based SLAM with Rao-Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid-steering mobile robot.

Indoor Localization for Skid-Steering Mobile Robot by Fusing Encoder, Gyroscope, and Magnetometer

This paper presents a novel indoor localization method for skid-steering mobile robot by fusing the readings from encoder, gyroscope, and magnetometer which can be read as an enhanced dead-reckoning localization method. Compared with the traditional dead-reckoning localization method implemented by encoder only, the accuracy and reliability can be improved significantly in spite of the price of slightly higher cost in digital devices. The proposed strategy consists mainly of an orientation algorithm and a localization algorithm. First, realizing that gyroscope is barely affected by magnetic field and magnetometer-based orientation has no cumulative error, a novel orientation algorithm, based on the self-tuning Kalman filter coupled with a gross error recognizer, is developed. This orientation algorithm can be applied to determine the robot heading angle in the situation with abundant ferromagnetic materials. Second, based on the orientation algorithm we have proposed, a novel localization algorithm is designed by decomposing the robot motion into uniform linear motion and uniform circular motion. The effectiveness of the proposed indoor localization method is verified via the real-world experiment using a tracked mobile robot developed in our laboratory.

Path following algorithm for skid-steering mobile robot based on adaptive discontinuous posture control

ABSTRACTThe kinematic model of a skid-steering mobile robot (SSMR) is manipulated using signed polar transformation which represents a discontinuous state transformation. The influence of relative position between the instantaneous center of rotation (ICR) and SSMR center of mass is considered. Then, adaptive state feedback controller is designed and stability regions are studied. Subsequently, a point-to-point tracking algorithm is introduced to track a trajectory that is defined by a set of way-points, which is the more realistic case of dangerous exploration or landmine detection purposes. The closed-loop system is simulated using MATLAB environment and experimentally validated using a modified TURTLEBOT3 Burger. Results show that the proposed controller reaches almost zero steady state error with smooth paths for point stabilization, moreover, good tracking capabilities are demonstrated. The proposed control system integrates both posture and tracking algorithm, thus achieve trajectory tacking which is defined by a set of way-points.

Model-Based Coordinated Control of Four-Wheel Independently Driven Skid Steer Mobile Robot with Wheel–Ground Interaction and Wheel Dynamics

Four-wheel independently driven mobile robots are widely used in industrial automation, intelligent inspection, and outdoor exploration. The traditional kinematic control is usually applied for them, where only the chassis kinematics is taken into account and the robot dynamics (especially the wheel dynamics) is normally ignored. It may lead to some performance limitations such as the chattering phenomenon during robot rotating, because of the overactuation characteristic by four driving wheels. To address these problems, the integrated dynamic model is proposed, which includes chassis kinematics, chassis dynamics, wheel–ground interaction, and wheel dynamics. Subsequently, different from kinematic control, a model-based coordinated adaptive robust controller is developed, which generally consists of three-level designs for different parts of robot dynamics, and directly generates the motor driving torque commands for four wheels. The stability and tracking performance are theoretically guaranteed. Comparative experiments are carried out, and the results show the better performance of our proposed scheme.

Active Disturbance Rejection Control for Handling Slip in Tracked Vehicle Locomotion

This paper describes the use of an active disturbance rejection controller (ADRC) to estimate and compensate for the effect of slip in an online manner to improve the path tracking performance of autonomous ground vehicles (AGVs). AGVs with skid-steer locomotion mode are extensively used for robotic applications in the fields of agriculture, transportation, construction, warehouse maintenance, and mining. Majority of these applications such as performing reconnaissance and rescue operations in rough terrain or autonomous package delivery in urban scenarios, require the system to follow a path predetermined by a high-level planner or based on a predefined task. In the absence of effective slip estimation and compensation, the AGVs, especially tracked vehicles, can fail to follow the path as given out by the high-level planner. The proposed ADRC architecture uses a generic mathematical model that can account for the scaling and shift in the states of the system due to the effects of slip through augmented parameters. An extended Kalman filter (EKF) observer is used to estimate the varying slip parameters online. The estimated parameters are then used to compensate for the effects of slip at each iteration by modifying the control actions given by a low-level path tracking controller. The proposed approach is validated through experiments over flat and uneven terrain conditions including asphalt, vinyl flooring, artificial turf, grass, and gravel using a tracked skid-steer mobile robot. A detailed discussion on the results and directions for future research is also presented.

Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

As a type of skid-steering mobile robot, the tracked robot suffers from inevitable slippage, which results in an imprecise kinematics model and a degradation of performance during navigation. Compared with the traditional robot, the kinematics model is able to reflect the influences of slippage through the introduction of instantaneous centres of rotation (ICRs). However, ICRs cannot be measured directly and are time-varying with terrain variation, and thus, here, we aim to develop an online estimation method to acquire the ICRs of a robot by means of data fusion technologies. First, an innovation-based extended Kalman filter (IEKF) is employed to fuse the readings from two incremental encoders and a GPS-compass integrated sensor, to provide a real-time ICR estimation. Second, a decision tree-based learning system is used to classify the terrains that the robot traverses, according to the vibration signals gathered by an accelerometer. The results of this terrain classification are improved via a Bayesian filter, by utilizing temporal correlation in the terrain time series. Third, the performances of the ICR estimation and terrain classification are mutually promoted. On one hand, terrain variation is detected with the aid of the terrain classification, and therefore, the process noise variance of IEKF can be automatically adjusted. Hence, the results of ICR estimation are smooth if the terrain does not change and converge rapidly upon terrain variation. On the other hand, the sudden changes in innovation are used to adjust the state transition probability during the recursive calculation of the Bayesian filter, thus increasing the accuracy of the terrain classification. A real-world experiment was undertaken on a tracked robot to validate the effectiveness of the proposed method. It is also demonstrated that the terrain adaptive odometry outperforms the traditional approach with the knowledge of ICRs.

Linear and Non-Linear Control Design of Skid Steer Mobile Robot on an Embedded

A detailed approach for a linear Proportional-Integral-Derivative (PID) controller and a non-linear controller - Linear Quadratic Regulator (LQR) is discussed in this paper. By analyzing several mathematical designs for the Skid Steer Mobile Robot (SSMR), the controllers are implemented in an embedded microcontroller - Mbed LPC1768. To verify the controllers, MATLAB-Simulink is used for the simulation of both the controllers involving motors - Maxon RE40. This paper compares between PID and LQR controller along with the performance comparison between Homogenous and Non-Homogenous LQR controllers.

An Adaptive-Fuzzy Fractional-Order Sliding Mode Controller Design for an Unmanned Vehicle

In this paper, speed and direction angle of a four wheel skid-steered mobile robot (4 WD SSMR) has been controlled via adaptive-fuzzy fractional-order sliding mode controller (AFFOSMC) under different speed and angle reference signals. The hybrid control method is designed to combine all advantages that controllers have such as flexibility realized by fractional order calculation, robustness to disturbances and parameters variations provided by sliding mode controller (SMC) as well as adaptation of G constant of SMC via fuzzy controller, simultaneously. Also, a fractional-order SMC is applied to the system for the same reference speed and angle references to show the effects of the changes and adaptation of G constant. Experimental results show that the AFFOSMC has better trajectory tracking performance than the FOSMC.DOI: http://dx.doi.org/10.5755/j01.eie.24.2.20630

Discontinuous Stabilizing Control of Skid-Steering Mobile Robot (SSMR)

A discontinuous stabilizing control of Skid-Steering Mobile Robot (SSMR) is proposed using σ transformation introduced in Astolfi (Syst. Control Lett. 27(1), 37–45, 1996). A linear time-invariant system (LTI) is obtained which is driven by state-dependent disturbance. A linear H∞ controller is designed to reduce the effect of this disturbance. Two control transformations are carried out in order to bring the system in a form suitable for σ transformation; one for the case of SSMR orientation around 0 and π and the other around ± π/2. The resulting two controllers for the two cases are blended using fuzzy logic. The closed-loop system is simulated using Matlab environment on point stabilization from different initial conditions. Results show that the proposed controller guarantees asymptotic stability with smooth paths. Experimental results are consistent with simulation and show that the proposed controller succeeded to stabilize the SSMR to the desired point without shuttering.

A Physics-Based Power Model for Skid-Steered Wheeled Mobile Robots

The power consumed by skid-steered mobile robots varies enormously depending on their operating regimes and environments. Therefore, energy optimal planning of field missions can be accomplished only if the operating environment and an accurate model of the robot's power consumption are known in advance. This paper studies influences of location of the center of mass, surface type, terrain relief, platform geometry, radius of curvature, speed, and temperature on the power consumption of a skid-steered mobile platform and derives a physics-based power model that uses friction to accurately model the power consumed for the most relevant parameters inside a wide range of operating conditions. This paper also shows how friction can be measured using power consumed by the motors and how motor constants can be measured in a noninvasive end-to-end manner. Our findings were validated with a Clearpath's Husky A200 platform, driving it indoors and outdoors on different surfaces, varying the speed, the radius of curvature, the mass, and the center of mass location, resulting in a dense dataset on which the derived model as well as the temperature dependence of the power consumption was verified.

Prediction and Experimental Validation of Power Consumption of Skid-Steer Mobile Robots in Manufacturing Environments

Skid-Steer Mobile Robots (SSMRs) provide a robust and simple mechanical drive platform making them useful in many applications. Power consumption is an important consideration in the design of any mobile robot and particularly important for SSMRs because of the slipping and corresponding friction that induce large loads on the drive system. The slipping behavior is generally characterized through Instantaneous Centers of Rotation (ICR) of the contact patches and it has been established that these are functions of the system dynamics. However, the existing SSMR power models generally treat these constraints at the kinematic level by assuming constant slip rates taken from empirical data. This paper demonstrates a method to evaluate SSMR power consumption based on slip parameters that are calculated as differential equations extracted from the equations of motion. The dynamic power model is validated and then implemented on two practical manufacturing applications in which a mobile robot is climbing on steel surfaces with primary power consumption due to turning and overcoming gravity. The applications show that the dynamic ICR model plays a significant role in estimating power requirements. The results further demonstrate that the power and energy requirements for a given task depend on the payload and motion along the task in a non-linear fashion, for example showing that the minimum payload does not necessarily correspond to the minimum energy use. This outcome suggests that dynamic effects can be used to find optimal trajectories to minimize power or energy requirements.

Trajectory tracking control of Skid-Steered Mobile Robot based on adaptive Second Order Sliding Mode Control

This paper presents design and implementation of adaptive Second Order Sliding Mode Control (SOSMC) for a four wheels Skid-Steered Mobile Robot (SSMR). The control objective is to follow a predefined trajectory by regulating the linear and angular velocities, and in presence of external disturbance and parametric uncertainty. Adaptive Super Twisting (AST) algorithm is designed in order to build a robust controller with neglected chattering in steady state. The proposed controller is validated experimentally. The results show that the proposed controller guarantees the performance of the conventional SOSMC under external disturbance and parametric uncertainty with less chattering.

Experimental Speedup and Stability Validation for Multi-Step MPC

In this paper, we propose a multi-step model predictive control (MPC) scheme without stabilizing constraints and/or costs. Within this work, a relaxed Lyapunov inequality is employed to verify asymptotic stability of the MPC closed loop. To this end, prior work is adapted to a trajectory based setting. The approach works for shorter prediction horizons in comparison to single-step MPC, but requires to stay in open loop for longer periods of time. We propose a technique to mitigate this drawback during runtime of the algorithm such that we benefit from the inherent robustness of single-step MPC. Then, we present a prime experimental validation of the proposed control scheme on a skid-steering mobile robot and show that the computational effort is significantly reduced.

Performance-Oriented Coordinated Adaptive Robust Control for Four-Wheel Independently Driven Skid Steer Mobile Robot

Four-wheel independently driven skid-steered mobile robots are widely used in the fields of industrial automation and outdoor exploration. In most of existing controllers of skid-steered mobile robots, the wheel velocities are controlled independently to track the desired velocities from the high-level kinematic controller. However, this kind of control method may lead to chattering phenomenon of skid-steered mobile robots in practice, when the desired velocity commands of four wheels are not matched under different ground conditions. In this paper, the coordinated control problem is investigated for the four-wheel independently driven skid steer mobile robots, so as to solve the chattering phenomenon and also achieve good control performance under different ground conditions. Since the mobile robots are over-actuated and lack of suspension systems, a coordinated adaptive robust control scheme integrated with torque allocation technique is proposed. First, an adaptive robust control law is developed to attenuate the negative effects of load variations and uncertainties. Second, instead of directly giving the desired velocity commands, a torque control and allocation algorithm is developed to regulate the driving torque of each wheel motor. A coordinated control law with considering the wheel slip compensation is also proposed. Comparative experiments are carried out, and the results show the proposed scheme can avoid the chattering problem and achieve the excellent performance under different ground conditions.

Modelling and design of tracked mobile climbing robots on non-planar surfaces

This paper presents an analysis of a tracked-based skid steer mobile robot following a defined path on a class of non-planar surfaces. The analysis will define a method to approximate the position and orientation of each track on a climbing surface in a manner that ensures opposing, symmetric components of the robot are geometrically even in their positioning relative to the base surface. The analysis will assume the robot chassis travels along the path with an orientation defined by the vectors tangent to the path and normal to the climbing surface, and that the centerline distance and longitudinal displacements between the tracks units are fixed. It will be shown that for general paths, three rotational degrees of freedom are required between the tracks to maintain line contact with the surface along the length of the track. This implies that the tracks do not remain parallel while following paths on non-planar surfaces. It will further be shown that relative lateral slipping between the left and right tracks result when traversing non-planar surfaces. Two subsets of paths are shown to require one rotational degree of freedom only and avoid lateral slipping. The model will then be used to define the required relative motion between the tracks which can be used to design a kinematic arrangement for connecting track modules to a central chassis in a manner to minimise slip and maximise surface contact when climbing on non-planar surfaces.

Caster-leg aided maneuver for negotiating surface discontinuities with a wheeled skid-steer mobile robot

Crossing surface discontinuities, such as ditches or curbs in urban environments, is a relevant navigation issue for ground mobile robots. This paper proposes a maneuver for negotiating surface discontinuities for a hybrid locomotion system consisting on a skid-steer wheeled vehicle with a single caster-leg mechanism. This maneuver is appropriate for gaps, steps, and ramps, both upwards and downwards. Furthermore, an analysis of tipover stability and steerability through the different states of the maneuver is performed, resulting in sensor-based stability margins that can be computed online. Experimental results have been obtained by means of realistic Simulink/ADAMS simulations and actual tests with the Lazaro mobile robot. The results confirm the effectiveness of the proposed maneuver as well as the applicability of stability margins.

Output feedback control of a skid-steered mobile robot based on the super-twisting algorithm

This paper presents the design and implementation of an output feedback controller based on the super twisting algorithm (STA) that stabilizes the trajectory tracking error of a skid steered mobile robot (SSMR). The control scheme introduces a diffeomorphism based on the mathematical model of the SSMR to transform the original problem into a third order chain of integrators. In this study, the available measurements are the position and orientation of the SSMR. A modified STA working as a step by step differentiator estimates the velocity and acceleration of the mobile robot. Then, a second STA enforces the tracking of a predefined trajectory. Numerical and experimental results comparing the STA with a state feedback controller (SFC) and a first order sliding mode controller (FOSM) justify the control proposal.

Speed and Direction Angle Control of Four Wheel Drive Skid-Steered Mobile Robot by Using Fractional Order PI Controller

In this paper, speed and direction angle control of four-wheel drive skid-steered mobile robot (4WD SSMR) is realized by Fractional-Order Proportional Integral (FOPI) controller. Speed and direction angle of the mobile robot are calculated by using angular velocity of each motors. FOPI controller produces the torques of each motor of mobile robot for trajectory tracking and stabilization in the desired position. A well-tuned conventional PI controller is also applied to mobile robot for comparison with the FOPI. Experimental results prove that the FOPI shows better trajectory tracking performance than PI controller in terms of trajectory tracking accuracy and error levels.DOI: http://dx.doi.org/10.5755/j01.eie.22.5.16337

Waypoint Following for Differentially Driven Wheeled Robots with Limited Velocity Perturbations

In this paper a unified motion control strategy dedicated for the waypoint following task realized by a differentially driven robot is presented. It is assumed that the vehicle moves with limited velocities and accelerations in order to reduce excessive slip and skid effects. In order to include operational constraints, a motion planner is combined with a universal stabilizer taking advantage of transverse functions. To improve tracking precision translated transverse functions are deployed and a new adaptive technique for the controller tuning is proposed. During the motion planning stage an auxiliary trajectory connecting points in the configuration space and satisfying assumed phase constraints is generated. The resulting motion execution system has been implemented on a laboratory-scale skid-steering mobile robot, which served as platform for experimental validation of presented algorithms.