Constrained Model Predictive Control of a Skid-Steering Mobile Robot

International audienceAbstract—In this paper, a kinematic model of a four-wheelskid-steering mobile robot is presented and a receding horizonstabilizing control law for the system is developed, based onthe optimization of a quadratic cost function on the systemstates and control inputs. Global asymptotic stability of thenominal system with actuator saturation constraints is analyticallyproven and a simple dynamical model is constructed forvalidation purposes. The robustness and performance of thecontroller were tested under simulation on both models andthe results are presented and discussed

Analysis of Tread ICRs for Wheeled Skid-Steer Vehicles on Inclined Terrain

The instantaneous centers of rotation (ICRs) for the two treads of skid-steer vehicles moving with low inertia on hard horizontal terrain almost remain with constant local coordinates, which allows to establish an equivalence with differential-drive locomotion. However, this significant kinematic relationship has not been analyzed yet on sloped ground. One relevant difficulty of studying ICR behavior on inclined terrain, even on a flat surface, is the continuous variation of pitch and roll angles while turning. To overcome this problem, this paper analyzes a dynamic simulation of a skid-steer vehicle on horizontal ground where gravity is substituted by an equivalent external force in such a way that pitch and roll are kept constant. Relevant tread ICR variations on inclined ground have been deduced, which have a significant impact on skid-steer kinematics. These new findings have been corroborated experimentally with a four-wheeled mobile robot that turns on an inclined plane.Spanish Project PID2021-122944OB-I0

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

An Estimator for the Kinematic Behaviour of a Mobile Robot Subject to Large Lateral Slip

In this paper, the effects of wheel slip compensation in trajectory planning for mobile tractor-trailer robot applications are investigated. Firstly, a kinematic model of the proposed robot architecture is marked out, then an experimental campaign is done to identify if it is possible to kinematically compensate trajectories that otherwise would be subject to large lateral slip. Due to the close connection to the experimental data, the results shown are valid only for Epi.q, the prototype that is the main object of this manuscript. Nonetheless, the base concept can be usefully applied to any mobile robot subject to large lateral slip

MODEL PREDICTIVE CONTROL OF SKID-STEERED MOBILE ROBOT WITH DEEP LEARNING SYSTEM DYNAMICS

This thesis project presents several model predictive control (MPC) strategies for control of skid-steered mobile robots (SSMRs) using two different combinations of software environment, optimization tool and machine learning framework. The control strategies are tested in WeBots simulator. Spatial-based path following MPC of SSMR with static obstacle avoidance is developed in MATLAB environment with ACADO optimization toolkit using spatial kinematic model of SSMR. It includes static obstacle and border avoidance strategy based on artificial potential fields. Simulations show that the controller is effective at driving SSMR on a track, while avoiding borders and obstacles. Several more MPCs are developed using Python environment, ACADOS optimisation framework, and Pytorch-Casadi integration framework. Two time-domain controllers are made in Python environment, one based on SSMR kinematic model and another based on data-driven state-space model using Pytorch- Casadi framework. Both are setup to reach a goal point in simulation experiment. Experiments show that both versions reliably reach a target point. Standard and data-driven versions of spatial path following MPC are developed. Standard is a reimplementation of MPC designed in MATLAB with modifications to cost function and border avoidance, without static obstacle avoidance. Data-driven path following MPC is an extension of standard variant with state-space model replaced with a hybrid of spatial kinematics and data-driven model. Simulation of both spatial controllers confirm their effectiveness in following reference path

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

In this article, a controller based on a Robot Operating System (ROS) for a two-wheeled self-balancing robot is designed. The proposed ROS architecture is open, allowing the integration of different sensors, actuators, and processing units. The low-cost robot was designed for educational purposes. It used an ESP32 microcontroller as the central unit, an MPU6050 Inertial Measurement Unit sensor, DC motors with encoders, and an L298N integrated circuit as a power stage. The mathematical model is analyzed through Newton-Euler and linearized around an equilibrium point. The control objective is to self-balance the robot to the vertical axis in the presence of disturbances. The proposed control is based on a bounded saturation, which is lightweight and easy to implement in embedded systems with low computational resources. Experimental results are performed in real-time under regulation, conditions far from the equilibrium point, and rejection of external disturbances. The results show a good performance, thus validating the mechanical design, the embedded system, and the control scheme. The proposed ROS architecture allows the incorporation of different modules, such as mapping, autonomous navigation, and manipulation, which contribute to studying robotics, control, and embedded systems

Design and Motion Planning for a Reconfigurable Robotic Base

A robotic platform for mobile manipulation needs to satisfy two contradicting requirements for many real-world applications: A compact base is required to navigate through cluttered indoor environments, while the support needs to be large enough to prevent tumbling or tip over, especially during fast manipulation operations with heavy payloads or forceful interaction with the environment. This paper proposes a novel robot design that fulfills both requirements through a versatile footprint. It can reconfigure its footprint to a narrow configuration when navigating through tight spaces and to a wide stance when manipulating heavy objects. Furthermore, its triangular configuration allows for high-precision tasks on uneven ground by preventing support switches. A model predictive control strategy is presented that unifies planning and control for simultaneous navigation, reconfiguration, and manipulation. It converts task-space goals into whole-body motion plans for the new robot. The proposed design has been tested extensively with a hardware prototype. The footprint reconfiguration allows to almost completely remove manipulation-induced vibrations. The control strategy proves effective in both lab experiment and during a real-world construction task.Comment: 8 pages, accepted for RA-L and IROS 202