Spencer Folk, Ph.D.

This thesis presents a solution that enables aerial robots to reason about surrounding wind flow fields in real time using on board sensors and embedded flight hardware. The core novelty of this research is the fusion of range measurements with sparse in situ wind measurements to predict surrounding flow fields. We aim to address two fundamental questions: first, the sufficiency of topographical data for accurate wind prediction in dense urban environments; and second, the utility of learned wind models for motion planning with an emphasis on energy efficiency and obstacle avoidance. Drawing on tools from deep learning, fluid mechanics, and optimal control, we establish a framework for local wind prediction using navigational LiDAR, and then incorporate local wind model priors into a receding-horizon optimal controller to study how local wind knowledge affects energy use and robustness during autonomous navigation. Through simulated demonstrations in diverse urban wind scenarios we evaluate the predictive capabilities of the wind predictor, and quantify improvements to autonomous urban navigation in terms of crash rates and energy consumption when local wind information is integrated into the motion planning. Sub-scale free flight experiments in an open-air wind tunnel demonstrate that these algorithms can run in real time on an embedded flight computer with sufficient bandwidth for stable control of a small aerial robot. Philosophically, this thesis contributes a new paradigm for localized wind inference and motion planning in unknown windy environments. By enabling robots to rapidly assess local wind conditions without prior environmental knowledge, this research accelerates the introduction of aerial robots into increasingly challenging environments.

Urban winds are a serious hazard for low-altitude autonomous aerial operations in urban airspaces. Previous methods for motion planning in urban winds require global knowledge of the obstacles and flow field and do not lend themselves to real-time application. In this paper, a planning and control framework is proposed for safe and energy-efficient navigation through urban flow fields that strictly relies on onboard sensing. The algorithm incorporates predictions of local wind flow fields into a receding horizon optimal controller, balancing energy consumption with obstacle avoidance on the fly to reach a goal destination. Simulation studies on a procedurally generated urban map with diverse wind conditions demonstrate that the energy-aware motion planner reduces energy consumption by as much as 30% and results in 32% fewer crashes on average compared to the wind-agnostic baseline.Comparisons to a global wind-aware planner indicate only minor trade-offs associated with planning on a local horizon.

Obtaining accurate and timely predictions of the wind through an urban environment is a challenging task, but has wide-ranging implications for the safety and efficiency of autonomous aerial vehicles in future urban airspaces. Prior work relies strongly on global information about the environment, such as a precise map of the city and in-situ wind measurements at various locations, to run expensive computational fluid dynamics solvers to predict the entire wind flow field. In contrast, this letter introduces a new method to estimate the wind flow field in a region around the robot in real time, utilizing on-board range measurements to sense nearby buildings and sparse wind measurements to infer windspeed and direction. We propose that this information sufficiently characterizes the structure of the wind flow field in the local region of interest. To that end, we introduce a deep learning-based approach to predict local flow fields from range measurements. Our results indicate that a neural network trained on numerous simulated winds through small randomized maps is capable of reconstructing local wind flows while generalizing to larger environments with over 200 buildings. This contribution empowers computationally-constrained aerial robots to reason about the structure of local wind flow fields, thereby enabling new planning, control, and estimation strategies in windy urban environments without a priori knowledge of the map.

Simulators play a critical role in aerial robotics both in and out of the classroom. We present RotorPy, a simulation environment written entirely in Python intentionally designed to be a lightweight and accessible tool for robotics students and researchers alike to probe concepts in estimation, planning, and control for aerial robots. RotorPy simulates the 6-DoF dynamics of a multirotor robot including aerodynamic wrenches, obstacles, actuator dynamics and saturation, realistic sensors, and wind models. This work describes the modeling choices for RotorPy, benchmark testing against real data, and a case study using the simulator to design and evaluate a model-based wind estimator.

Aerial vehicle missions require navigating trade-offs during design, such as the range, speed, maneuverability, and size. Multi-modal aerial vehicles enable this trade-off to be negotiated during flight. This paper presents a Bistable Aerial Transformer (BAT) robot, a novel morphing hybrid aerial vehicle that switches between quadrotor and fixed-wing modes via rapid acceleration and without any additional actuation beyond those required for normal flight. The design features a compliant bistable mechanism made of thermoplastic polyurethane (TPU) that bears a large mass at the center of the robot’s body. When accelerating, inertial forces transition the vehicle between its stable modes, and a four-bar linkage connected to the bistable mechanism folds the vehicle’s wings in and out. The paper includes the full robot design and a comparison of the fabricated system to the elastodynamic simulation. Successful transitions between the two modes in mid-flight, as well as sustained flight in each mode indicate that the vehicle experiences higher agility in the quadrotor mode and higher flight efficiency in the fixed-wing mode, at an energy equivalent cost of only 2 s of flight time per pair of transitions. The vehicle demonstrates how compliant and bistable mechanisms can be integrated into future aerial vehicles for controllable self-reconfiguration for tasks such as surveillance and sampling that require a combination of maneuverability and long-distance flight.

Control tuning and adaptation present a significant challenge to the usage of robots in diverse environments. It is often nontrivial to find a single set of control parameters by hand that work well across the broad array of environments and conditions that a robot might encounter. Automated adaptation approaches must utilize prior knowledge about the system while adapting to significant domain shifts to find new control parameters quickly. In this work, we present a general framework for online controller adaptation that deals with these challenges. We combine meta-learning with Bayesian recursive estimation to learn prior predictive models of system performance that quickly adapt to online data, even when there is significant domain shift. These predictive models can be used as cost functions within efficient sampling-based optimization routines to find new control parameters online that maximize system performance. Our framework is powerful and flexible enough to adapt controllers for four diverse systems: a simulated race car, a simulated quadrupedal robot, and a simulated and physical quadrotor. The video and code can be found at this https URL.