313 research outputs found
Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning
Slip and skid compensation is crucial for mobile robots' navigation in
outdoor environments and uneven terrains. In addition to the general slipping
and skidding hazards for mobile robots in outdoor environments, slip and skid
cause uncertainty for the trajectory tracking system and put the validity of
stability analysis at risk. Despite research in this field, having a real-world
feasible online slip and skid compensation is still challenging due to the
complexity of wheel-terrain interaction in outdoor environments. This paper
presents a novel trajectory tracking technique with real-world feasible online
slip and skid compensation at the vehicle-level for skid-steering mobile robots
in outdoor environments. The sliding mode control technique is utilized to
design a robust trajectory tracking system to be able to consider the parameter
uncertainty of this type of robot. Two previously developed deep learning
models [1], [2] are integrated into the control feedback loop to estimate the
robot's slipping and undesired skidding and feed the compensator in a real-time
manner. The main advantages of the proposed technique are (1) considering two
slip-related parameters rather than the conventional three slip parameters at
the wheel-level, and (2) having an online real-world feasible slip and skid
compensator to be able to reduce the tracking errors in unforeseen
environments. The experimental results show that the proposed controller with
the slip and skid compensator improves the performance of the trajectory
tracking system by more than 27%
Disturbance Rejection Control for Autonomous Trolley Collection Robots with Prescribed Performance
Trajectory tracking control of autonomous trolley collection robots (ATCR) is
an ambitious work due to the complex environment, serious noise and external
disturbances. This work investigates a control scheme for ATCR subjecting to
severe environmental interference. A kinematics model based adaptive sliding
mode disturbance observer with fast convergence is first proposed to estimate
the lumped disturbances. On this basis, a robust controller with prescribed
performance is proposed using a backstepping technique, which improves the
transient performance and guarantees fast convergence. Simulation outcomes have
been provided to illustrate the effectiveness of the proposed control scheme
Control of Outdoor Robots at Higher Speeds on Challenging Terrain
This thesis studies the motion control of wheeled mobile robots. Its focus is set on high speed control on challenging terrain. Additionally, it deals with the general problem of path following, as well as path planning and obstacle avoidance in difficult conditions.
First, it proposes a heuristic longitudinal control for any wheeled mobile robot, and evaluates it on different kinematic configurations and in different conditions, including laboratory experiments and participation in a robotic competition.
Being the focus of the thesis, high speed control on uneven terrain is thoroughly studied, and a novel control law is proposed, based on a new model representation of skid-steered vehicles, and comprising of nonlinear lateral and longitudinal control. The lateral control part is based on the Lyapunov theory, and the convergence of the vehicle to the geometric reference path is proven. The longitudinal control is designed for high speeds, taking actuator saturation and the vehicle properties into account. The complete solution is experimentally tested on two different vehicles on several different terrain types, reaching the speeds of ca. 6 m/s, and compared against two state-of-the-art algorithms.
Furthermore, a novel path planning and obstacle avoidance system is proposed, together with an extension of the proposed high speed control, which builds up a navigation system capable of autonomous outdoor person following. This system is experimentally compared against two classical obstacle avoidance methods, and evaluated by following a human jogger in outdoor environments, with both static and dynamic obstacles.
All the proposed methods, together with various different state-of-the-art control approaches, are unified into one framework. The proposed framework can be used to control any wheeled mobile robot, both indoors and outdoors, at low or high speeds, avoiding all the obstacles on the way. The entire work is released as open-source software
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
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
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