Browsing by Author "Badar, Tabish"
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Item Estimation of 3D form of the Path for Autonomous Driving in Terrain(Elsevier Science, 2023-11-22) Badar, Tabish; Ouattara, Issouf; Backman, Juha; Visala, Arto; Department of Electrical Engineering and Automation; Autonomous Systems3D path estimation, i.e., determining the height profiles of the path, is crucial in motion control for autonomous driving in terrains. It is essential to prevent rollover from abrupt peaks, dips, and briefly wet ground. In this study, wheel displacement measurements and height estimation of an off-road vehicle are presented. A calibration process is suggested to measure the instantaneous vertical displacement of each wheel. GNSS-pose-based methods are used to estimate the vehicle's geometry parameters yielding the centimeter-level accuracy of the 3D-path estimation method. The accuracy of the vehicle's instrumentation is examined on a test track to create a 3D terrain model of the path. The outcome of the proposed scheme is compared to a reference elevation profile created with structure from motion on the basis of machine vision and GNSS using data collected by an unmanned aerial vehicle. The comparison of the results demonstrates that the 3D path can be estimated with sufficient accuracy in open terrain using ground vehicles.Item Estimation of the height profile of the path for autonomous driving in terrain(Elsevier Science Ltd., 2024-04) Badar, Tabish; Ouattara, Issouf; Backman, Juha; Visala, Arto; Department of Electrical Engineering and Automation; Autonomous SystemsA priori knowledge about the height profile of the path is vital for rollover avoidance in the context of autonomous driving through uneven forest ground. The forest ground is usually covered with either soft vegetation in summertime, or by snow in winter. Thus, the exact solid form of the forest ground cannot be detected by camera or LiDAR. This article, we propose height-odometry and aided height-odometry methods for ground height estimation. The height-odometry method depends solely on interoceptive and proprioceptive sensor data, while the aided height-odometry combines height-odometry output with the existing 3D map information. Thus, the central idea is to build a reference 3D path for autonomous forest machines where the spatial positioning – based on the RTK-GNSS or Forest SLAM method – is fused with the output of (aided) height-odometry method(s). We evaluate the proposed height-odometry methods in two separate environments that are accurately (3D) mapped by a UAV using the advanced machine-vision-based SfM method and the LiDAR-based SLAM algorithms. Through comprehensive data analysis, we demonstrate that the proposed 3D path estimation methods are practical and simple to implement, yet sufficient to estimate the height profile of the path with desired accuracy.Item Implementation of the autonomous functionalities on an electric vehicle platform for research and education(2019-06-17) Badar, Tabish; Vainio, Mika; Sähkötekniikan korkeakoulu; Visala, ArtoSelf-driving cars have recently captured the attention of researchers and car manufacturing markets. Depending upon the level of autonomy, the cars are made capable of traversing from one point to another autonomously. In order to achieve this, sophisticated sensors need to be utilized. A complex set of algorithms is required to use the sensors data in order to navigate the vehicle along the desired trajectory. Polaris is an electric vehicle platform provided for research and education purposes at Aalto University. The primary focus of the thesis was to utilize all the sensors provided in Polaris to their full potential. So that, essential data from each sensor is made available to be further utilized either by a specific automation algorithm or by some mapping routine. For any autonomous robotic system, the first step towards automation is localization. That is to determine the current position of the robot in a given environment. Different sensors mounted over the platform provide such measurements in different frames of reference. The thesis utilizes the GPS based localization solution combined with the LiDAR data and wheel odometry to perform autonomous tasks. Robot Operating System is used as the software development tool in thesis work. Autonomous tasks include the determination of the global as well as the local trajectories. The endpoints of the global trajectories are dictated by the set of predefined GPS waypoints. This is called target-point navigation. A path needs to be planned that avoids all the obstacles. Based on the planned path, a set of velocity commands are issued by the embedded controller. The velocity commands are then fed to the actuators to move the vehicle along the planned trajectory.Item Levels of autonomy in self-driving cars and methods to achieve them(2021-05-06) Seeve, Elias; Badar, Tabish; Sähkötekniikan korkeakoulu; Forsman, PekkaItem Modeling of Tire Lateral Forces in Non-linear 6-DOF Simulations for Off-Road Vehicles(Elsevier Science Publishers, 2022-09-15) Badar, Tabish; Backman, Juha; Visala, Arto; Department of Electrical Engineering and Automation; Autonomous SystemsThis study highlights the use of a linear model to generate lateral forces in a nonlinear vehicle driving simulation. The crucial thing about modeling lateral forces is the centripetal acceleration limit a ground vehicle may experience. One can employ a linear model to simulate lateral forces when the commanded lateral acceleration for an off-road car-like vehicle (such as Polaris - an electric all-terrain vehicle) is limited to 3 m/s2, and for a heavy forest truck (for example, Ponsse's Bison - a forwarder) to about 1 m/s2. Tire construction, which plays a significant role in the load-carrying capability and the cornering of a ground vehicle, is considered in this paper. An estimate of the cornering stiffness for the tires is determined using Hewson's model, which uses only the basic information mentioned in their datasheets. At the maximum rated load, a cornering stiffness coefficient value is obtained. The cornering coefficient is used to simulate lateral forces as the function of vertical load and sideslip angle. The simulation results highlight the advantages and deficiencies of using a linear tire model to generate lateral forces for off-road vehicles. Finally, the simulation data is analyzed, where the results are compared with those obtained from a standard kinematic model.Item Nonlinear 6-DOF Dynamic Simulations for Center-Articulated Vehicles with combined CG(Elsevier Science Publishers, 2022-07-01) Badar, Tabish; Backman, Juha; Tariq, Usama; Vísala, Arto; Department of Electrical Engineering and Automation; Autonomous Systems; Department of Electrical Engineering and AutomationThis study highlights a combined center of gravity (CG) approach to model the comprehensive dynamics of the ground vehicles with articulated steering using solely six degrees of freedom (6-DOF). It is the case with an articulated vehicle that its CG shifts laterally towards the center of rotation during a turn. Thus, the idea is to compute the combined CG position of the multi-body articulated vehicle, which leads to the correction of the moment arms and body inertias about the updated CG position in the dynamic equations. Hence, the body forces and moments are computed with respect to the corrected CG position. In addition, the paper illustrates mathematical modeling of the center-articulated steering mechanism for the ground vehicle while restricting its operation to the primary handling regime. Overall, the draft presents the design of a nonlinear 6-DOF simulation for a load-haul-dump (LHD) type of articulated vehicle with a traveling CG. The simulation data is presented from one simulation run, where the critical results are analyzed. The obtained results signify the simplicity in using a combined CG to represent the vehicle dynamics.Item Nonlinear model predictive control in path tracking and rollover prevention for autonomous forest machines(2023-08-21) Palmén, Mikael; Badar, Tabish; Sähkötekniikan korkeakoulu; Visala, ArtoAn increasing labor shortage is affecting the forestry industry, and the demand for experienced forest machine operators is increasing. A potential solution for this challenge is to increase the efficiency of the harvesting processes by enhancing the level of automation. In this thesis, an autonomous control method for path tracking and rollover prevention for forwarders is proposed. The objective is to ensure that the forwarder is able to accurately follow the desired path in the forest without rolling over, while taking into account the uneven ground profile of the forest floor. The proposed solution utilizes model predictive control, which allows taking into account system constraints and prior knowledge of the elevation changes along the reference path. The performance of the controller was analyzed with simulations and tests using actual hardware. The results of the controller's performance were promising, and the objectives of path tracking and rollover prevention were fulfilled in simulations. However, the testing with real hardware was limited and mainly pointed out that the proposed system setup is capable of controlling the test platform using the developed controller. Despite some challenges related to prediction model inaccuracies, computational requirements, and implementations with real hardware, NMPC can be seen as a potential solution for path tracking and rollover prevention of autonomous forest machines, thanks to the significant advantages of predictive control. Additionally, future developments in modeling and numerical methods, as well as improvements in computational performance, should help overcome these challenges.Item System identification methods for autonomous control of the self-driving car(2020-05-25) Åstrand, Nico; Badar, Tabish; Sähkötekniikan korkeakoulu; Forsman, PekkaItem Vehicle modeling and state estimation for autonomous driving in terrain(Elsevier Ltd, 2024-11) Badar, Tabish; Backman, Juha; Visala, Arto; Department of Electrical Engineering and Automation; Autonomous SystemsThe automobile industry usually ignores the height of the path and uses planar vehicle models to implement automatic vehicle control. In addition, existing literature mostly concerns level terrain or homogeneous road surfaces for estimating vehicle dynamics. However, ground vehicles utilized in forestry, such as forwarders, operate on uneven terrain. The vehicle models built on level terrain assumptions are inadequate to capture the rolling or pitching dynamics of such machines as rollover of such vehicles is a potential risk. Therefore, knowledge about the height profile of the path is crucial for automating such off-road operations and avoiding rollover. We propose the use of a six-degrees-of-freedom (6-DOF) dynamic vehicle model to solve the autonomous forwarder problem. An adaptive linear tire model is used in the 6-DOF model assuming the vehicle operates in a primary handling regime. The force models are modified to include the three-dimensional (3D) map information. The calibration procedures, identifying actuator dynamics, and quantifying sensor delays are also represented. The proposed vehicle modeling contributed to realizing the continuous-discrete extended Kalman filter (CDEKF), which takes into account the 3D path during filtering and fixed-lag smoothing. Polaris (an all-terrain electric car) is used as a case study to experimentally validate the vehicle modeling and performance of the state estimator. Three types of grounds are selected — an asphalt track, a concrete track with a high elevation gradient, and a gravel track inside a forest. Stable state estimates are obtained using CDEKF and sparse 3D maps of terrains despite discontinuities in satellite navigation data inside the forest. The height estimation results are obtained with sufficient accuracy when compared to ground truth obtained by aerial 3D mapping. Finally, the proposed model's applicability for predictive control is demonstrated by utilizing the state estimates to predict future states considering (3D) terrain.
