UAV Navigation System for Prescribed Fires

Since the beginning of mankind, a lot of fires have happened and have taken millions of lives, whether they were human or animal lives. On average, there are about twenty thousand forest fires annually in the world and the burnt area is one per thousand of the total forest area on Earth. In the last years, there were a lot of big fires such as the fires in Pedrogão Grande, Portugal, the SoCal fires in the US coast, the big fire in the Amazon Forest in Brazil and the bush fires in Australia, later 2019. When fires take such dimensions, they can also cause several environmental and health problems. These problems can be damage to millions of hectares of forest resources, the evacuation of thousands of people, burning of homes and devastation of infrastructures. When a big fire starts, the priority is the rapid rescue of lives and then, the attempt to control the fire. In these scenarios, autonomous robots are a very good assistance because they can help in the rescue missions and monitoring the fire. These autonomous robots include the unmanned aerial vehicle, or commonly called the UAV. This dissertation begins with an intensive research on the work that has already been done relative to this subject. It will then continue with the testing of different simulators and see which better fits for this type of work. With this, it will be implemented a simulation that can represent fires and has physics for test purposes, in order to test without causing any material damage in the real world. After the simulation part is done, algorithm testing and bench marking are expected, in order to compare different algorithms and see which are the best for this type of applications. If everything goes according to plan, in the end, it is expected to have an autonomous navigation system for UAVs to navigate through burnt areas and wildfires to monitor the development of these.Desde o início da humanidade muitos incêndios têm acontecido e têm levado milhões de vidas, quer estas sejam humanas ou animais. Em média, no planeta, existem cerca de vinte mil incêndios florestais anualmente e a área queimada é um por mil da área total de florestas do mundo e na última década, houveram grandes incêndios. Alguns destes são os de Pedrogrão Grande, em Portugal, os incêndios no sul da Califórnia, na costa dos EUA, o incêndio que deflagrou na floresta Amazónia, no Brasil e os incêndios na Austrália, no final de 2019. Quando os incêndios assumem estas dimensões, podem vir a causar vários problemas ambientais e de saúde. Estes problemas podem ser danos a milhões de hectares de recursos florestais, a evacuação de milhares de pessoas e podem haver habitações e infraestruturas ardidas. Quando um grande incêndio começa, a primeira prioridade é o resgate rápido e de seguida a tentativa de controlar o incêndio. Nestes cenários, robôs autónomos são uma excelente assistência. Estes robôs incluem o veículo aéreo não tripulado, o UAV. Esta dissertação começa com uma intensa pesquisa sobre o trabalho já realizado em relação a este tema. De seguida, vários testes irão ser realizados para testar diferentes simuladores e ver qual melhor se adapta ao trabalho que se irá realizar. Com isto, será implementada uma simulação que consiga representar um incêndio e suporte várias fisícas do mundo real. Após a secção da simulação estar concluída, espera-se vários testes de algoritmos e comparação entre eles, para ver qual o que se adequa melhor a este tipo de situações. Se tudo correr conforme planeado, é esperado no final desta dissertação ter-se um sistema de navegação autónoma para UAVs percorrem áreas florestais e ser possível monitorizar incêndios

EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy

Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terrain properties directly from data via self-supervision, but challenges remain to properly quantify and mitigate risks due to uncertainties in learned models. This work efficiently quantifies both aleatoric and epistemic uncertainties by learning discrete traction distributions and probability densities of the traction predictor's latent features. Leveraging evidential deep learning, we parameterize Dirichlet distributions with the network outputs and propose a novel uncertainty-aware squared Earth Mover's distance loss with a closed-form expression that improves learning accuracy and navigation performance. The proposed risk-aware planner simulates state trajectories with the worst-case expected traction to handle aleatoric uncertainty, and penalizes trajectories moving through terrain with high epistemic uncertainty. Our approach is extensively validated in simulation and on wheeled and quadruped robots, showing improved navigation performance compared to methods that assume no slip, assume the expected traction, or optimize for the worst-case expected cost.Comment: Under review. Journal extension for arXiv:2210.00153. Project website: https://xiaoyi-cai.github.io/evora

Intention prediction for interactive navigation in distributed robotic systems

Modern applications of mobile robots require them to have the ability to safely and effectively navigate in human environments. New challenges arise when these robots must plan their motion in a human-aware fashion. Current methods addressing this problem have focused mainly on the activity forecasting aspect, aiming at improving predictions without considering the active nature of the interaction, i.e. the robot’s effect on the environment and consequent issues such as reciprocity. Furthermore, many methods rely on computationally expensive offline training of predictive models that may not be well suited to rapidly evolving dynamic environments. This thesis presents a novel approach for enabling autonomous robots to navigate socially in environments with humans. Following formulations of the inverse planning problem, agents reason about the intentions of other agents and make predictions about their future interactive motion. A technique is proposed to implement counterfactual reasoning over a parametrised set of light-weight reciprocal motion models, thus making it more tractable to maintain beliefs over the future trajectories of other agents towards plausible goals. The speed of inference and the effectiveness of the algorithms is demonstrated via physical robot experiments, where computationally constrained robots navigate amongst humans in a distributed multi-sensor setup, able to infer other agents’ intentions as fast as 100ms after the first observation. While intention inference is a key aspect of successful human-robot interaction, executing any task requires planning that takes into account the predicted goals and trajectories of other agents, e.g., pedestrians. It is well known that robots demonstrate unwanted behaviours, such as freezing or becoming sluggishly responsive, when placed in dynamic and cluttered environments, due to the way in which safety margins according to simple heuristics end up covering the entire feasible space of motion. The presented approach makes more refined predictions about future movement, which enables robots to find collision-free paths quickly and efficiently. This thesis describes a novel technique for generating "interactive costmaps", a representation of the planner’s costs and rewards across time and space, providing an autonomous robot with the information required to navigate socially given the estimate of other agents’ intentions. This multi-layered costmap deters the robot from obstructing while encouraging social navigation respectful of other agents’ activity. Results show that this approach minimises collisions and near-collisions, minimises travel times for agents, and importantly offers the same computational cost as the most common costmap alternatives for navigation. A key part of the practical deployment of such technologies is their ease of implementation and configuration. Since every use case and environment is different and distinct, the presented methods use online adaptation to learn parameters of the navigating agents during runtime. Furthermore, this thesis includes a novel technique for allocating tasks in distributed robotics systems, where a tool is provided to maximise the performance on any distributed setup by automatic parameter tuning. All of these methods are implemented in ROS and distributed as open-source. The ultimate aim is to provide an accessible and efficient framework that may be seamlessly deployed on modern robots, enabling widespread use of intention prediction for interactive navigation in distributed robotic systems

Path Planning Framework for Unmanned Ground Vehicles on Uneven Terrain

In this thesis, I address the problem of long-range path planning on uneven terrain for non-holonomic wheeled mobile robots (WMR). Uneven terrain path planning is essential for search-and-rescue, surveillance, military, humanitarian, agricultural, constructing missions, etc. These missions necessitate the generation of a feasible sequence of waypoints, or reference states, to navigate a WMR from the initial location to the final target location through the uneven terrain. The feasibility of navigating through a given path over uneven terrain can be undermined by various terrain features. Examples of such features are loose soil, vegetation, boulders, steeply sloped terrain, or a combination of all of these elements. I propose a three-stage framework to solve the problem of rapid long-range path planning. In the first stage, RRT-Connect provides a rapid discovery of the feasible solution. Afterward, Informed RRT* improves the feasible solution. Finally, Shortcut heuristics improves the solution locally. To improve the computational speed of path planning algorithms, we developed an accelerated version of the traversability estimation on point clouds based on Principal Component Analysis. The benchmarks demonstrate the efficacy of the path planning approach

Assessment of local path planners in a indoor and outdoor robot

Modern mobile robotics are entering commercial use in a variety of non-controlled environments. One such robot is the Roboguide service and guide robot for the visually impaired. For the smooth operation of a service robot in the daily life of its users, it is imperative that the paths along which the robot travels are intuitive, comfortable, and above all, safe. The goal of this thesis is to assess the viability of the Elastic Band, Timed Elastic Band and Dynamic Window Approach path-planners in a dynamic environment. This is accomplished through testing various scenarios typical in dynamic environments, including outright collisions and near-miss scenarios. The testing is done on a simulated platform. In addition to assessing the current viability of the path-planners in question, this thesis also aims to identify challenges and problems caused by the dynamic nature of the environment. The results suggest the Timed Elastic Band is the superior path-planner. Dynamic obstacles create problems for all the tested path-planners, and a future approach to cost-efficient dynamic prediction is suggested. The tests within this thesis are implemented using Robotic Operating System(ROS) and the robot simulation environment Gazebo. Implementations are based on real products and software modules.Nykyaikaista autonomista robotiikkaa on alettu soveltaa kaupallisessa käytössä erilaisissa kontrolloimattomissa toimintaympäristöissä. Yksi näistä sovelluskohteista on Roboguide, näkövammaisille suunnattu opasrobotti. Jotta robottia olisi intuitiivista ja turvallista käyttää, on oleellista, että robotti pystyy toimimaan arkipäivän eri tilanteissa helppokäyttöisesti, mukavasti ja ennen kaikkea turvallisesti. Tämän diplomityön tavoite on arvioida Elastic Band, Timed Elastic Band ja Dynamic Window Approach reittisuunnitelualgoritmien soveltuvuutta dynaamisessa ympäristössä. Tätä varten on toteutettu testisarja, jossa simuloidaan tyypillisiä dynaamisen ympäristön haasteita, kuten törmäys- ja läheltä-ohitustilanteita. Testaus toteutettiin simuloidulla alustalla. Eri reittisuunnittelualgoritmien soveltuvuuden arvioinnin lisäksi diplomityö pyrkii tunnistamaan dynaamisessa ympäristössä liikkumiseen liittyviä haasteita ja uhkakuvia. Testatuista algoritmeista Timed Elastic Band soveltuu selvästi parhaiten dynaamiseen ympäristöön. Lisäksi työssä ehdotetaan lähestymistapaa dynaamisten esteiden sijainnin ennustamiseen laskennallisesti tehokkaasti. Testaus on toteutettu ROS-pohjaisella robotilla ja testit on suoritettu Gazebo-simulointiympäristössä. Testaus ja simuloitu robotti perustuu aitoon tuotteeseen ja sen komponentteihin

PERFORMANCE EVALUATION AND REVIEW FRAMEWORK OF ROBOTIC MISSIONS (PERFORM): AUTONOMOUS PATH PLANNING AND AUTONOMY PERFORMANCE EVALUATION

The scope of this work spans two main areas of autonomy research 1) autonomous path planning and 2) test and evaluation of autonomous systems. Path planning is an integral part of autonomous decision-making, and a deep understanding in this area provides valuable perspective on approaching the problem of how to effectively evaluate vehicle behavior. Autonomous decision-making capabilities must include reliability, robustness, and trustworthiness in a real-world environment. A major component of robot decision-making lies in intelligent path-planning. Serving as the brains of an autonomous system, an efficient and reliable path planner is crucial to mission success and overall safety. A hybrid global and local planner is implemented using a combination of the Potential Field Method (PFM) and A-star (A*) algorithms. Created using a layered vector field strategy, this allows for flexibility along with the ability to add and remove layers to take into account other parameters such as currents, wind, dynamics, and the International Regulations for Preventing Collisions at Sea (COLGREGS). Different weights can be attributed to each layer based on the determined level of importance in a hierarchical manner. Different obstacle scenarios are shown in simulation, and proof-of-concept validation of the path-planning algorithms on an actual ASV is accomplished in an indoor environment. Results show that the combination of PFM and A* complement each other to generate a successfully planned path to goal that alleviates local minima and entrapment issues. Additionally, the planner demonstrates the ability to update for new obstacles in real time using an obstacle detection sensor. Regarding test and evaluation of autonomous vehicles, trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The author introduces the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework for which to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e. providing direct comparisons and quantitative evaluations of varying autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g. for surveying, surveillance, search and rescue). Several case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission

Machine Learning in Robotic Navigation:Deep Visual Localization and Adaptive Control

The work conducted in this thesis contributes to the robotic navigation field by focusing on different machine learning solutions: supervised learning with (deep) neural networks, unsupervised learning, and reinforcement learning.First, we propose a semi-supervised machine learning approach that can dynamically update the robot controller's parameters using situational analysis through feature extraction and unsupervised clustering. The results show that the robot can adapt to the changes in its surroundings, resulting in a thirty percent improvement in navigation speed and stability.Then, we train multiple deep neural networks for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. We prepare two image-based localization datasets in 3D simulation and compare the results of a traditional multilayer perceptron, a stacked denoising autoencoder, and a convolutional neural network (CNN). The experiment results show that our proposed inception based CNNs without pooling layers perform very well in all the environments. Finally, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. The multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep CNNs. The results show a significant improvement when multi-goal reinforcement learning is used