Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles

In order to enable Micro-Aerial Vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem, or solves it by using an optimistic global planner. We present a conservative trajectory optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.Comment: Accepted to ICRA 2018 and RA-L 201

Sensor Fusion and Non-linear MPC controller development studies for Intelligent Autonomous vehicular systems

The demand for safety and fuel efficiency on ground vehicles and advancement in embedded systems created the opportunity to develop Autonomous controller. The present thesis work is three fold and it encompasses all elements that are required to prototype the autonomous intelligent system including simulation, state handling and real time implementation. The Autonomous vehicle operation is mainly dependent upon accurate state estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The initial work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. The previous studies covers the handling of sensor noise data for vehicle yaw estimations and the same approach can be applied for additional sensors used in the work. However, it is important to develop simulations to analyze the autonomous navigation for various road, obstacles and grade conditions. These simulations serve base platform for real time implementation and provide the opportunity to implement it on real road vehicular application and leads to prototype the controller. Therefore, the next section deals with simulations that focuses on developing Non-linear Model Predictive controller for high speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in MATLAB environment. As mentioned, the above two sections provide base platform for real time implementation and the final section uses these techniques for developing intelligent autonomous vehicular system that would track the given path and avoid static obstacles by rejecting the considerable environmental disturbance in the given path. The Linear Quadratic Gaussian (LQG) is developed for the present application, The model developed in the LQG controller is a kinematic bicycle model, that mimics 1/5th scale truck and cubic spline has been used to connect and generate the continuous target path

3D modeling of ultra-high-resolution UAV imagery using low-cost photogrammetric software and structure from motion

The availability of advanced low-cost unmanned aerial systems (UASs), aftermarket applications, and a competitive market for processing software have provided researchers new opportunities for employing high resolution remote sensing in research. The UAS allows for the capture of close range aerial imagery that can then be used to generate dense point clouds using structure from motion (SfM). A variety of digital products can be created form these dense point clouds such as three-dimensional models, digital elevation models (DEMs), digital surface models (DSMs) and orthomosaics. This dissertation looks at methods and accuracies associated with the creation of digital mapping products from dense point clouds generated from imagery captured by two low-cost off the shelf UASs. The UASs were used to capture imagery over a 2-hectare vineyard in the Uwharrie mountains of North Carolina. Aspects of imagery collection, such as altitude, ground control, camera types, flight paths, and target styles, were investigated for their impacts on accuracy. Thirty-one ground control points were created in the vineyard using a survey grade GNSS receiver and total station for use in georeferencing. The number of ground control points used for georeferencing were reduced until a significant difference in accuracy was found using t-tests. Five ground control points were shown to be the least amount of ground control needed before accuracy began to change significantly. Four flight altitudes were tested with 80-meters generating the least level of error. Orthomosaics created from structure from motion and imagery collected using a global shutter 20-megapixel had total RMS errors between 2-4 cm

How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability

Estimating terrain traversability in off-road environments requires reasoning about complex interaction dynamics between the robot and these terrains. However, it is challenging to build an accurate physics model, or create informative labels to learn a model in a supervised manner, for these interactions. We propose a method that learns to predict traversability costmaps by combining exteroceptive environmental information with proprioceptive terrain interaction feedback in a self-supervised manner. Additionally, we propose a novel way of incorporating robot velocity in the costmap prediction pipeline. We validate our method in multiple short and large-scale navigation tasks on a large, autonomous all-terrain vehicle (ATV) on challenging off-road terrains, and demonstrate ease of integration on a separate large ground robot. Our short-scale navigation results show that using our learned costmaps leads to overall smoother navigation, and provides the robot with a more fine-grained understanding of the interactions between the robot and different terrain types, such as grass and gravel. Our large-scale navigation trials show that we can reduce the number of interventions by up to 57% compared to an occupancy-based navigation baseline in challenging off-road courses ranging from 400 m to 3150 m

Motion Planning For Micro Aerial Vehicles

A Micro Aerial Vehicle (MAV) is capable of agile motion in 3D making it an ideal platform for developments of planning and control algorithms. For fully autonomous MAV systems, it is essential to plan motions that are both dynamically feasible and collision-free in cluttered environments. Recent work demonstrates precise control of MAVs using time-parameterized trajectories that satisfy feasibility and safety requirements. However, planning such trajectories is non-trivial, especially when considering constraints, such as optimality and completeness. For navigating in unknown environments, the capability for fast re-planning is also critical. Considering all of these requirements, motion planning for MAVs is a challenging problem. In this thesis, we examine trajectory planning algorithms for MAVs and present methodologies that solve a wide range of planning problems. We first introduce path planning and geometric control methods, which produce spatial paths that are inadequate for high speed flight, but can be used to guide trajectory optimization. We then describe optimization-based trajectory planning and demonstrate this method for solving navigation problems in complex 3D environments. When the initial state is not fixed, an optimization-based method is prone to generate sub-optimal trajectories. To address this challenge, we propose a search-based approach using motion primitives to plan resolution complete and sub-optimal trajectories. This algorithm can also be used to solve planning problems with constraints such as motion uncertainty, limited field-of-view and moving obstacles. The proposed methods can run in real time and are applicable for real-world autonomous navigation, even with limited on-board computational resources. This thesis includes a carefully analysis of the strengths and weaknesses of our planning paradigm and algorithms, and demonstration of their performance through simulation and experiments

Multilevel Motion Planning: A Fiber Bundle Formulation

Motion planning problems involving high-dimensional state spaces can often be solved significantly faster by using multilevel abstractions. While there are various ways to formally capture multilevel abstractions, we formulate them in terms of fiber bundles, which allows us to concisely describe and derive novel algorithms in terms of bundle restrictions and bundle sections. Fiber bundles essentially describe lower-dimensional projections of the state space using local product spaces. Given such a structure and a corresponding admissible constraint function, we can develop highly efficient and optimal search-based motion planning methods for high-dimensional state spaces. Our contributions are the following: We first introduce the terminology of fiber bundles, in particular the notion of restrictions and sections. Second, we use the notion of restrictions and sections to develop novel multilevel motion planning algorithms, which we call QRRT* and QMP*. We show these algorithms to be probabilistically complete and almost-surely asymptotically optimal. Third, we develop a novel recursive path section method based on an L1 interpolation over path restrictions, which we use to quickly find feasible path sections. And fourth, we evaluate all novel algorithms against all available OMPL algorithms on benchmarks of eight challenging environments ranging from 21 to 100 degrees of freedom, including multiple robots and nonholonomic constraints. Our findings support the efficiency of our novel algorithms and the benefit of exploiting multilevel abstractions using the terminology of fiber bundles.Comment: Submitted to IJR

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments

Hybrid approaches for mobile robot navigation

The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point

Optimal Multi-UAV Trajectory Planning for Filming Applications

Teams of multiple Unmanned Aerial Vehicles (UAVs) can be used to record large-scale outdoor scenarios and complementary views of several action points as a promising system for cinematic video recording. Generating the trajectories of the UAVs plays a key role, as it should be ensured that they comply with requirements for system dynamics, smoothness, and safety. The rise of numerical methods for nonlinear optimization is finding a ourishing field in optimization-based approaches to multi- UAV trajectory planning. In particular, these methods are rather promising for video recording applications, as they enable multiple constraints and objectives to be formulated, such as trajectory smoothness, compliance with UAV and camera dynamics, avoidance of obstacles and inter-UAV con icts, and mutual UAV visibility. The main objective of this thesis is to plan online trajectories for multi-UAV teams in video applications, formulating novel optimization problems and solving them in real time. The thesis begins by presenting a framework for carrying out autonomous cinematography missions with a team of UAVs. This framework enables media directors to design missions involving different types of shots with one or multiple cameras, running sequentially or concurrently. Second, the thesis proposes a novel non-linear formulation for the challenging problem of computing optimal multi-UAV trajectories for cinematography, integrating UAV dynamics and collision avoidance constraints, together with cinematographic aspects such as smoothness, gimbal mechanical limits, and mutual camera visibility. Lastly, the thesis describes a method for autonomous aerial recording with distributed lighting by a team of UAVs. The multi-UAV trajectory optimization problem is decoupled into two steps in order to tackle non-linear cinematographic aspects and obstacle avoidance at separate stages. This allows the trajectory planner to perform in real time and to react online to changes in dynamic environments. It is important to note that all the methods in the thesis have been validated by means of extensive simulations and field experiments. Moreover, all the software components have been developed as open source.Los equipos de vehículos aéreos no tripulados (UAV) son sistemas prometedores para grabar eventos cinematográficos, en escenarios exteriores de grandes dimensiones difíciles de cubrir o para tomar vistas complementarias de diferentes puntos de acción. La generación de trayectorias para este tipo de vehículos desempeña un papel fundamental, ya que debe garantizarse que se cumplan requisitos dinámicos, de suavidad y de seguridad. Los enfoques basados en la optimización para la planificación de trayectorias de múltiples UAVs se pueden ver beneficiados por el auge de los métodos numéricos para la resolución de problemas de optimización no lineales. En particular, estos métodos son bastante prometedores para las aplicaciones de grabación de vídeo, ya que permiten formular múltiples restricciones y objetivos, como la suavidad de la trayectoria, el cumplimiento de la dinámica del UAV y de la cámara, la evitación de obstáculos y de conflictos entre UAVs, y la visibilidad mutua. El objetivo principal de esta tesis es planificar trayectorias para equipos multi-UAV en aplicaciones de vídeo, formulando novedosos problemas de optimización y resolviéndolos en tiempo real. La tesis comienza presentando un marco de trabajo para la realización de misiones cinematográficas autónomas con un equipo de UAVs. Este marco permite a los directores de medios de comunicación diseñar misiones que incluyan diferentes tipos de tomas con una o varias cámaras, ejecutadas de forma secuencial o concurrente. En segundo lugar, la tesis propone una novedosa formulación no lineal para el difícil problema de calcular las trayectorias óptimas de los vehículos aéreos no tripulados en cinematografía, integrando en el problema la dinámica de los UAVs y las restricciones para evitar colisiones, junto con aspectos cinematográficos como la suavidad, los límites mecánicos del cardán y la visibilidad mutua de las cámaras. Por último, la tesis describe un método de grabación aérea autónoma con iluminación distribuida por un equipo de UAVs. El problema de optimización de trayectorias se desacopla en dos pasos para abordar los aspectos cinematográficos no lineales y la evitación de obstáculos en etapas separadas. Esto permite al planificador de trayectorias actuar en tiempo real y reaccionar en línea a los cambios en los entornos dinámicos. Es importante señalar que todos los métodos de la tesis han sido validados mediante extensas simulaciones y experimentos de campo. Además, todos los componentes del software se han desarrollado como código abierto