Autonomous vehicles leverage the data provided by a suite of sensors, combining measurements in order to provide precise and robust position estimation to localization and navigation systems. In this paper, an Adaptive Monte Carlo Localization algorithm is applied to an autonomous golf car, where data from wheel odometry, an inertial measurement unit, a Global Positioning System (GPS) and laser scanning is combined to estimate the pose of a vehicle in an outdoor environment. Monte Carlo Localization techniques allow the compensation of the technical flaws of different sensors by fusing the information delivered by each one. However, one of the main problems of fusing GPS data are sudden decreases of accuracy and sudden jumps on positions due to phenomenons like multi–path signal reception. In this paper, a particle weighting MCL model which integrates GPS measurements is proposed, and its performance is compared in several experiments with a particle generation approach when a GPS sensor suddenly provides erroneous data.

In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters. This model is updated, including a real time wheel diameter measurement improving the accuracy of the results. A neural network model is used in order to learn the odometric model from data. Tests are made using this neural network in several configurations and the results are compared to the mathematical model, showing that the neural network can outperform the first proposed model.

To achieve optimal mobility, visually impaired people have to deal with obstacle detection and avoidance challenges. Aside from the broadly adopted white cane, electronic aids have been developed. However, available electronic devices are not extensively used due to their complexity and price. As an effort to improve the existing ones, this work presents the design of a low-cost aid for blind people. A standard low-cost HC-SRF04 ultrasonic range is modified by adding phase modulation in the ultrasonic pulses, allowing it to detect the origin of emission, thus discriminating if the echo pulses come from the same device and avoiding false echoes due to interference from other sources. This improves accuracy and security in areas where different ultrasonic sensors are working simultaneously. The final device, based on users and trainers feedback for the design, works with the user’s own mobile phone, easing utilization and lowering manufacturing costs. The device was tested with a set of twenty blind persons carrying out a travel experiment and satisfaction survey. The main results showed a change in total involuntary contacts with unknown obstacles and high user satisfaction. Hence, we conclude that the device can fill a gap in the mobility aids and reduce feelings of insecurity amongst the blind.

An Ant colony optimisation approach is proposed to address the problem of non-structured roads real-time detection and tracking by a self-guided electrical vehicle. The solution adopted tries to find some properties describing the probability that a pixel belongs to the boundaries of the road, and then formalises the road detection problem as an optimisation one.

This paper describes a localization module for an autonomous wheelchair. This module includes a combination of various sensors such as odometers, laser scanners, IMU and Doppler speed sensors. Every sensor used in the module features variable covariance estimation in order to yield a final accurate localization. The main problem of a localization module composed of different sensors is the accuracy estimation of each sensor. Average static values are normally used, but these can lead to failure in some situations. In this paper, all the sensors have a variable covariance estimation that depends on the data quality. A Doppler speed sensor is used to estimate the covariance of the encoder odometric localization. Lidar is also used as a scan matching localization algorithm, comparing the difference between two consecutive scans to obtain the change in position. Matching quality gives the accuracy of the scan matcher localization. This structure yields a better position than a traditional odometric static covariance method. This is tested in a real prototype and compared to a standard fusion technique.

The lifting methodology can be used for simplifying the closed-loop stability study of a system in which the measurement data are acquired at different frequencies. This is because the lifting operators allow treating this kind of systems as if there is only one control frequency, which reduces the complexity of the problem in a very important way. The four-rotor microhelicopter designed by the authors, whose control is faced, is a nonlinear plant with sensors operating at two different frequencies. First, the system is linearised cancelling the rotors dynamics. Then, the authors implement a model reference adaptive control structure for identifying the gains of the linear reference plant and performing the adaptive control of the microhelicopter. At this point, the system stability study is carried out by just computing the eigenvalues of the closed-loop states matrix thanks to the lifting operators.