@conference {331, title = {Cone Detection with Convolutional Neural Networks for an Autonomous Formula Student Race Car}, booktitle = {26th ABCM International Congress of Mechanical Engineering (COBEM 2021)}, year = {2021}, abstract = {

Formula Student Driverless is a competition that provides an opportunity to design and test a wide set of technologies that are key for achieving autonomy in vehicle navigation: object detection and segmentation, obstacle avoidance, trajectory planning and following, etc.\ 
In this context, the objective of this work is to employ and analyse state-of-the art Convolutional Neural Networks (CNN) architectures known in the literature for robust\  detection of cones whose function is to delimit tracks for an autonomous race car in the Formula Student Driverless Competition.
Here, fast and reliable cone detection is important for achieving a robust and efficient perception pipeline for a Formula race car.
To that end, we employ the known YOLO (You Only Look Once) architecture that makes use of CNNs and several optimizations to achieve fast and accurate enough object detection to be suitable for the current application.
To train the CNN/YOLO, different datasets are considered: one is provided by MIT and another is the FSOCO dataset (Formula Student Objects in Context), which is a more complete dataset that considers four different classes of cones in the track: blue cone, yellow cone, and small orange and large orange cones.
The main metrics for measuring the performance of the trained network in cone detection tasks are the Mean Average Precision (mAP) and network inference time.\ 
Using these metrics and different versions of YOLO (the original and the tiny version), we analyze:\ 
(1) the influence of the image resolution for the input layer of the CNN;\ 
(2) the influence of different image conditions by varying brightness, exposure, blur, noise and other perturbations;\ 
(3) the impact of manual augmentations in the training dataset and its performance under different image conditions.
In general, YOLO has shown to be a strong candidate for real-time cone detection for race cars.
Results shows that mAP performance increases as the image resolution for the input layer in the YOLO net gets higher, at a cost of increasing the CNN\&$\#$39;s inference time though.\ 
Besides, manual augmentations of the training set were found to be effective for recovering the lost mAP when perturbations (of brightness, blur and noise) were added to the images.\ 
As future work, the current cone detection solution can be extended to be part of the perception pipeline of the race car from Ampera Racing, which is an electric Formula SAE (Society of Automotive Engineers) team from Federal University of Santa Catarina.

}, author = {La{\'\i}za Milena Scheid Parizotto and Eric A Antonelo} } @proceedings {334, title = {Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distribution}, journal = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, year = {2021}, pages = {1-8}, abstract = {

Reinforcement learning methods for continuous control tasks have evolved in recent years generating a family of policy gradient methods that rely primarily on a Gaussian distribution for modeling a stochastic policy. However, the Gaussian distribution has an infinite support, whereas real world applications usually have a bounded action space. This dissonance causes an estimation bias that can be eliminated if the Beta distribution is used for the policy instead, as it presents a finite support. In this work, we investigate how this Beta policy performs when it is trained by the Proximal Policy Optimization (PPO) algorithm on two continuous control tasks from OpenAI gym. For both tasks, the Beta policy is superior to the Gaussian policy in terms of agent\&$\#$39;s final expected reward, also showing more stability and faster convergence of the training process. For the CarRacing environment with high-dimensional image input, the agent\&$\#$39;s success rate was improved by 63\% over the Gaussian policy.

}, doi = {10.1109/SSCI50451.2021.9660123}, url = {https://ieeexplore.ieee.org/abstract/document/9660123}, author = {Irving G. B. Petrazzini and Eric A Antonelo} } @article {Antonelo2015, title = {System Identification of a Vertical Riser Model with Echo State Networks}, journal = {IFAC-PapersOnLine (2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production)}, volume = {48}, year = {2015}, pages = {304-310}, abstract = {

System identification of highly nonlinear dynamical systems, important for reducing time complexity in long simulations, is not trivial using more traditional methods such as recurrent neural networks (RNNs) trained with back-propagation through time. The recently introduced Reservoir Computing (RC)\∗ approach to training RNNs is a viable and powerful alternative which renders fast training and high performance. In this work, a single Echo State Network (ESN), a flavor of RC, is employed for system identification of a vertical riser model which has stationary and oscillatory signal behaviors depending of the production choke opening input variable. It is shown experimentally that these different behaviors are learned by constraining the high-dimensional reservoir states to attractor subspaces in which the specific behavior is represented. Further experiments show the stability of the identified system.

}, issn = {2405-8963}, doi = {10.1016/j.ifacol.2015.08.048}, url = {http://www.sciencedirect.com/science/article/pii/S2405896315009155}, author = {Eric A Antonelo and Eduardo Camponogara and Agustinho Plucenio} }