24 research outputs found
Fast Simulation of Vehicles with Non-deformable Tracks
This paper presents a novel technique that allows for both computationally
fast and sufficiently plausible simulation of vehicles with non-deformable
tracks. The method is based on an effect we have called Contact Surface Motion.
A comparison with several other methods for simulation of tracked vehicle
dynamics is presented with the aim to evaluate methods that are available
off-the-shelf or with minimum effort in general-purpose robotics simulators.
The proposed method is implemented as a plugin for the open-source
physics-based simulator Gazebo using the Open Dynamics Engine.Comment: Submitted to IROS 201
Conference Paper Number: 28878 DEVELOPING A KINEMATIC ESTIMATION MODEL FOR A CLIMBING MOBILE ROBOTIC WELDING SYSTEM
Abstract: Skid steer tracked-based robots are popular due to their mechanical simplicity, zero-turning radius and greater traction. This architecture also has several advantages when employed by mobile platforms designed to climb and navigate ferrous surfaces, such as increased magnet density and low profile (center of gravity). However, creating a kinematic model for localization and motion control of this architecture is complicated due to the fact that tracks necessarily slip and do not roll. Such a model could be based on a heuristic representation, an experimentally-based characterization or a probabilistic form. This paper will extend an experimentallybased kinematic equivalence model to a climbing, track-based robot platform. The model will be adapted to account for the unique mobility characteristics associated with climbing. The accuracy of the model will be evaluated in several representative tasks. Application of this model to a climbing mobile robotic welding system (MRWS) is presented
Kinematics Based Visual Localization for Skid-Steering Robots: Algorithm and Theory
To build commercial robots, skid-steering mechanical design is of increased
popularity due to its manufacturing simplicity and unique mechanism. However,
these also cause significant challenges on software and algorithm design,
especially for pose estimation (i.e., determining the robot's rotation and
position), which is the prerequisite of autonomous navigation. While the
general localization algorithms have been extensively studied in research
communities, there are still fundamental problems that need to be resolved for
localizing skid-steering robots that change their orientation with a skid. To
tackle this problem, we propose a probabilistic sliding-window estimator
dedicated to skid-steering robots, using measurements from a monocular camera,
the wheel encoders, and optionally an inertial measurement unit (IMU).
Specifically, we explicitly model the kinematics of skid-steering robots by
both track instantaneous centers of rotation (ICRs) and correction factors,
which are capable of compensating for the complexity of track-to-terrain
interaction, the imperfectness of mechanical design, terrain conditions and
smoothness, and so on. To prevent performance reduction in robots' lifelong
missions, the time- and location- varying kinematic parameters are estimated
online along with pose estimation states in a tightly-coupled manner. More
importantly, we conduct in-depth observability analysis for different sensors
and design configurations in this paper, which provides us with theoretical
tools in making the correct choice when building real commercial robots. In our
experiments, we validate the proposed method by both simulation tests and
real-world experiments, which demonstrate that our method outperforms competing
methods by wide margins.Comment: 18 pages in tota
On-line learning and updating unmanned tracked vehicle dynamics
Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle mission
DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT
In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller
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
A design and analysis of an autonomous ground vehicle to automate the process of transplanting rice
Precision agriculture brought with it the implementation of new digital technologies, mainly autonomous vehicles, satellite images, IoT and artificial intelligence, to provide economic, productive, and environmental benefits in the agricultural field. However, the main applications are focused on data management and monitoring of crop fields, so agricultural processes such as planting and harvesting are not yet fully automated. A clear example of this occurs in the cultivation of rice, which despite being one of the most important agricultural products in the world, the manual production method continues to predominate in developing countries. This work presents a design of an autonomous terrestrial vehicle capable of carrying out the rice transplantation process, having as its main characteristics its ability to move in the field of cultivation at a speed of 0.75m/s, transport a payload of up to 20kg and possess an autonomy of 1 hour. Which translates into an effective field capacity (EFC) of 0.21 ha/h, an operational equivalence of 7 workers/hour and an increase in the productivity of the transplant process of 200% with respect to the manual process. It seeks to provide farmers in developing countries with an affordable option, supported by numerical simulations, with which they can obtain the benefits of precision agriculture in the process of transplanting rice. In such a way, that the manual production of rice and its disadvantages such as low productivity, the physical consequences for the farmers and the limitations against expensive machinery are replaced by the automation proposal.Association of Field Ornithologist
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