Browsing by Author "Cheein, Fernando Auat"

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    GNSS-Based Estimation of Average Instantaneous Power Consumption in Electric Vehicles
    (2023) Bustos, Javier A. Torres; Orchard, Marcos E.; Torres-Torriti, Miguel; Cheein, Fernando Auat
    Several countries are currently leading the challenge of replacing internal combustion engines (ICE) in vehicles by electrically powered ones, mainly motivated by the goal of reducing the dependence on oil and the carbon footprint. However, the autonomy of electric vehicles (EVs) remains significantly lower than their ICE counterpart: it depends on the amount of energy stored and its rate of utilization, characterized by the instantaneous power consumption (IPC). Although IPC can be measured from on-board voltage and current sensors, in route planning applications it is of paramount importance to generate prior estimates from other sources of information, such as the mass of the vehicle, its velocity, acceleration, aerodynamic, and rolling resistances (among other vehicle and terrain parameters). In this work, we propose the estimation of average IPC for a given path, using only localization information provided by a global navigation satellite system antenna, at different levels of errors. Our work is experimentally validated with an EV on the road, achieving on average a 99.7% of power estimation accuracy for a localization error of +/- 1.5 m-as best case-and 73.6% accuracy for +/- 8.6 m of localization error-as worst case. These results encourages us to use only position data to estimate average IPC of EVs, avoiding the need of placing extra sensors into the vehicles.
  • No Thumbnail Available
    Item
    Markov Chain Monte Carlo Parameter Estimation for Nonzero Slip Models of Wheeled Mobile Robots: A Skid-Steer Case Study
    (2021) Yandun, Francisco; Torres-Torriti, Miguel; Cheein, Fernando Auat
    An accurate modeling, simulation, and estimation of the wheel-terrain interaction and its effects on a robot movement plays a key role in control and navigation tasks, specially in constantly changing environments. We study the calibration of wheel slip models using Particle Markov Chain Monte Carlo methods to approximate the posterior distributions of their parameters. In contrast to classic identification approaches, considering the parameters as random variables allows to obtain a probability measure of the parameter estimations and subsequently propagate their uncertainty to wheel slip-related variables. Extensive simulation and experimental results showed that the proposed methodology can effectively get reliable posterior approximations from noisy sensor measurements in changing terrains. Validation tests also include the applicability assessment of the proposed methodology by comparing it with the integrated prediction error minimization methodology. Field results presented up to 66% of improvement in the robot motion prediction with the proposed calibration approach.
  • No Thumbnail Available
    Item
    Model predictive path-following controller for Generalised N-Trailer vehicles with noisy sensors and disturbances
    (2024) Deniz, Nestor; Jorquera, Franco; Torres-Torriti, Miguel; Cheein, Fernando Auat
    Generalised N-Trailer (GNT) vehicles, commonly used in agriculture, mining, and industry, consist of a tractor pulling multiple passive trailers. This paper presents a nonlinear moving horizon estimator (NMHE) and nonlinear model predictive controller (NMPC) for GNT robotic vehicles. The NMHE accurately estimates the vehicle's state under challenging conditions such as noisy measurements, disturbances, and different soil conditions, without relying on assumptions about disturbances, making it more effective than techniques like the Extended Kalman Filter (EKF). Similarly, the NMPC successfully steers the GNT along a predefined path with lower control effort compared to existing algorithms, thanks to smoother tractor manoeuvres that minimise energy in the objective function. Moreover, unlike other methods, this approach computes the tractors' velocities instead of those of the last trailer, eliminating the need for kinematic inversion and making it suitable for various vehicle configurations. Efficient solvers for the NMHE and NMPC problems were generated using two different open-source frameworks. The proposed framework was evaluated through challenging simulated studies and field experiments. Additional resources, including code implementation and field experiment videos, can be found at https://drive.google.com/drive/folders/1n-qVWJA40cZn-WiDpwXhhF9AdK54LTfX?usp=sharing.
  • Thumbnail Image
    Item
    Point cloud-based estimation of effective payload volume for earthmoving loaders
    (2020) Guevara, Javier; Arévalo Ramírez, Tito; Yandun, Francisco; Torres Torriti, Miguel Attilio; Cheein, Fernando Auat