Automated Layout Design and Control of Robust Cooperative Grasped-Load Aerial Transportation Systems

We present a novel approach to cooperative aerial transportation through a team of drones, using optimal control theory and a hierarchical control strategy. We assume the drones are connected to the payload through rigid attachments, essentially transforming the whole system into a larger flying object with "thrust modules" at the attachment locations of the drones. We investigate the optimal arrangement of the thrust modules around the payload, so that the resulting system is robust to disturbances. We choose the $\mathcal{H}_2$ norm as a measure of robustness, and propose an iterative optimization routine to compute the optimal layout of the vehicles around the object. We experimentally validate our approach using four drones and comparing the disturbance rejection performances achieved by two different layouts (the optimal one and a sub-optimal one), and observe that the results match our predictions.

Design and control of a collision-resilient aerial vehicle with an icosahedron tensegrity structure

We present the tensegrity aerial vehicle, a design of collision-resilient rotor robots with icosahedron tensegrity structures. The tensegrity aerial vehicles can withstand high-speed impacts and resume operation after collisions. To guide the design process of these aerial vehicles, we propose a model-based methodology that predicts the stresses in the structure with a dynamics simulation and selects components that can withstand the predicted stresses. Meanwhile, an autonomous re-orientation controller is created to help the tensegrity aerial vehicles resume flight after collisions. The re-orientation controller can rotate the vehicles from arbitrary orientations on the ground to ones easy for takeoff. With collision resilience and re-orientation ability, the tensegrity aerial vehicles can operate in cluttered environments without complex collision-avoidance strategies. Moreover, by adopting an inertial navigation strategy of replacing flight with short hops to mitigate the growth of state estimation error, the tensegrity aerial vehicles can conduct short-range operations without external sensors. These capabilities are validated by a test of an experimental tensegrity aerial vehicle operating with only onboard inertial sensors in a previously-unknown forest.

A Zero-Shot Adaptive Quadcopter Controller

This paper proposes a universal adaptive controller for quadcopters, which can be deployed zero-shot to quadcopters of very different mass, arm lengths and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework. We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network. We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground. We directly deploy the same controller trained in the simulation without any modifications on two quadcopters with differences in mass, inertia, and maximum motor speed of up to 4 times. In addition, we show rapid adaptation to sudden and large disturbances (up to 35.7%) in the mass and inertia of the quadcopters. We perform an extensive evaluation in both simulation and the physical world, where we outperform a state-of-the-art learning-based adaptive controller and a traditional PID controller specifically tuned to each platform individually. Video results can be found at https://dz298.github.io/universal-drone-controller/.

Perception-aware receding horizon trajectory planning for multicopters with visual-inertial odometry

Visual inertial odometry (VIO) is widely used for the state estimation of multicopters, but it may function poorly in environments with few visual features or in overly aggressive flights. In this work, we propose a perception-aware collision avoidance local planner for multicopters. Our approach is able to fly the vehicle to a goal position at high speed, avoiding obstacles in the environment while achieving good VIO state estimation accuracy. The proposed planner samples a group of minimum jerk trajectories and finds collision-free trajectories among them, which are then evaluated based on their speed to the goal and perception quality. Both the features' motion blur and their locations are considered for the perception quality. The best trajectory from the evaluation is tracked by the vehicle and is updated in a receding horizon manner when new images are received from the camera. All the sampled trajectories have zero speed and acceleration at the end, and the planner assumes no other visual features except those already found by the VIO. As a result, the vehicle will follow the current trajectory to the end and stop safely if no new trajectories are found, avoiding collision or flying into areas without features. The proposed method can run in real time on a small embedded computer on board. We validated the effectiveness of our proposed approach through experiments in indoor and outdoor environments. Compared to a perception-agnostic planner, the proposed planner kept more features in the camera's view and made the flight less aggressive, making the VIO more accurate. It also reduced VIO failures, which occurred for the perception-agnostic planner but not for the proposed planner. The experiment video can be found at https://youtu.be/LjZju4KEH9Q.

Tethered Power Supply for Quadcopters: Architecture, Analysis and Experiments

Tethered quadcopters are used for extended flight operations where the necessary power to the system is provided through the tether from an external power source on the ground. In this work, we study the design factors such as the tether mass, electrical resistance, voltage conversion efficiency, etc. that influence the power requirements. We present analytical formulations to predict the power requirement for a given setup. Additionally, we show the existence of a critical hover height for a single-quadcopter tether system, beyond which it is physically (electrically) impossible for the quadcopter to hover. We then present experimental results for single and two-quadcopter tethered systems. Power supply readings from the experiments in various configurations are consistent with the predictions from the analysis (within 5%), which experimentally validates the presented analysis. A two-quadcopter system powered via a single tether is flown through a corridor to demonstrate one of the applications of having multiple quadcopters on the same tether.

Learning Torque Control for Quadrupedal Locomotion

Reinforcement learning (RL) is a promising tool for developing controllers for quadrupedal locomotion. The design of most learning-based locomotion controllers adopts the joint position-based paradigm, wherein a low-frequency RL policy outputs target joint positions that are then tracked by a high-frequency proportional-derivative (PD) controller that outputs joint torques. However, the low frequency of such a policy hinders the advancement of highly dynamic locomotion behaviors. Moreover, determining the PD gains for optimal tracking performance is laborious and dependent on the task at hand. In this paper, we introduce a learning torque control framework for quadrupedal locomotion, which trains an RL policy that directly predicts joint torques at a high frequency, thus circumventing the use of PD controllers. We validate the proposed framework with extensive experiments where the robot is able to both traverse various terrains and resist external pushes, given user-specified commands. To our knowledge, this is the first attempt of learning torque control for quadrupedal locomotion with an end-to-end single neural network that has led to successful real-world experiments among recent research on learning-based quadrupedal locomotion which is mostly position-based.

Model-free online motion adaptation for energy efficient flights of multicopters

Limited flight distance and time is a common problem for multicopters. We propose a method for finding the optimal speed and heading of multicopters while flying a given path to achieve the longest flight distance or time. Since flight speed and heading are often free variables in multicopter path planning, they can be changed without changing the mission. The proposed method is based on a novel multivariable extremum seeking controller with adaptive step size. It (a) does not require any power consumption model of the vehicle, (b) can be executed online, (c) is computationally efficient and runs on low-cost embedded computers in real-time, and (d) converges faster than the standard extremum seeking controller with constant step size. We prove the stability of this proposed extremum seeking controller, and conduct outdoor experiments to validate the effectiveness of this method with different initial conditions, with and without payload. This method could be especially useful for applications such as package delivery, where the weight, size and shape of the payload vary between deliveries and the power consumption of the vehicle is hard to model. Experiments show that compared to flying at the maximum speed with a bad heading angle, flying at the optimal range speed and heading reduces the energy consumed per distance by 24.9% without payload and 33.5% with a box payload. In addition, compared to hovering, flying at the optimal endurance speed and heading reduces the the power consumption by 7.0% without payload and 12.6% with a box payload.

Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles

The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times. However, basic problems such as fast and robust geo-localization in GPS-denied environments still remain unsolved. Existing research has primarily concentrated on improving the accuracy of localization at the cost of long and varying computation time in various situations, which often necessitates the use of powerful ground station machines. In order to make image-based geo-localization online and pragmatic for lightweight embedded systems on UAVs, we propose a framework that is reliable in changing scenes, flexible about computing resource allocation and adaptable to common camera placements. The framework is comprised of two stages: offline database preparation and online inference. At the first stage, color images and depth maps are rendered as seen from potential vehicle poses quantized over the satellite and topography maps of anticipated flying areas. A database is then populated with the global and local descriptors of the rendered images. At the second stage, for each captured real-world query image, top global matches are retrieved from the database and the vehicle pose is further refined via local descriptor matching. We present field experiments of image-based localization on two different UAV platforms to validate our results.

Design and Control of a Midair Reconfigurable Quadcopter using Unactuated Hinges

This paper presents the design and control of a novel quadcopter capable of changing shape mid-flight, allowing for operation in four configurations with the capability of sustained hover in three. The normally rigid connections between the arms of the quadcopter and the central body are replaced by free-rotating hinges that allow the arms to fold downward; no additional actuators beyond the four motors that drive the propellers are used. Configuration transitions are accomplished by either reducing or reversing the thrust forces produced by specific propellers during flight. Constraints placed on the control inputs of the vehicle prevent the arms from folding or unfolding unexpectedly, allowing for the use of existing quadcopter controllers and trajectory generation algorithms. For our experimental vehicle at hover, we find that these constraints result in a 36% reduction of the maximum yaw torque the vehicle can produce, but do not result in a reduction of the maximum thrust or roll and pitch torques. Furthermore, the ability to change configurations is shown to enable the vehicle to traverse small passages, perch on hanging wires, and perform simple grasping tasks.

Autonomous Flight through Cluttered Outdoor Environments Using a Memoryless Planner

This paper introduces a collision avoidance system for navigating a multicopter in cluttered outdoor environments based on the recent memory-less motion planner, rectangular pyramid partitioning using integrated depth sensors (RAPPIDS). The RAPPIDS motion planner generates collision-free flight trajectories at high speed with low computational cost using only the latest depth image. In this work we extend it to improve the performance of the planner by taking the following issues into account. (a) Changes in the dynamic characteristics of the multicopter that occur during flight, such as changes in motor input/output characteristics due to battery voltage drop. (b) The noise of the flight sensor, which can cause unwanted control input components. (c) Planner utility function which may not be suitable for the cluttered environment. Therefore, in this paper we introduce solutions to each of the above problems and propose a system for the successful operation of the RAPPIDS planner in an outdoor cluttered flight environment. At the end of the paper, we validate the proposed method's effectiveness by presenting the flight experiment results in a forest environment. A video can be found at www.youtube.com/channel/UCK-gErmvZlBODN5gQpNcpsg