7 research outputs found
AutoTune: Controller Tuning for High-Speed Flight
Due to noisy actuation and external disturbances, tuning controllers for
high-speed flight is very challenging. In this paper, we ask the following
questions: How sensitive are controllers to tuning when tracking high-speed
maneuvers? What algorithms can we use to automatically tune them? To answer the
first question, we study the relationship between parameters and performance
and find out that the faster the maneuver, the more sensitive a controller
becomes to its parameters. To answer the second question, we review existing
methods for controller tuning and discover that prior works often perform
poorly on the task of high-speed flight. Therefore, we propose AutoTune, a
sampling-based tuning algorithm specifically tailored to high-speed flight. In
contrast to previous work, our algorithm does not assume any prior knowledge of
the drone or its optimization function and can deal with the multi-modal
characteristics of the parameters' optimization space. We thoroughly evaluate
AutoTune both in simulation and in the physical world. In our experiments, we
outperform existing tuning algorithms by up to 90\% in trajectory completion.
The resulting controllers are tested in the AirSim Game of Drones competition,
where we outperform the winner by up to 25\% in lap-time. Finally, we show that
AutoTune improves tracking error when flying a physical platform with respect
to parameters tuned by a human expert.Comment: Video: https://youtu.be/m2q_y7C01So; Code:
https://github.com/uzh-rpg/mh_autotun
Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking
Accurately modeling quadrotor's system dynamics is critical for guaranteeing
agile, safe, and stable navigation. The model needs to capture the system
behavior in multiple flight regimes and operating conditions, including those
producing highly nonlinear effects such as aerodynamic forces and torques,
rotor interactions, or possible system configuration modifications. Classical
approaches rely on handcrafted models and struggle to generalize and scale to
capture these effects. In this paper, we present a novel Physics-Inspired
Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system
dynamics purely from robot experience. Our approach combines the expressive
power of sparse temporal convolutions and dense feed-forward connections to
make accurate system predictions. In addition, physics constraints are embedded
in the training process to facilitate the network's generalization capabilities
to data outside the training distribution. Finally, we design a model
predictive control approach that incorporates the learned dynamics for accurate
closed-loop trajectory tracking fully exploiting the learned model predictions
in a receding horizon fashion. Experimental results demonstrate that our
approach accurately extracts the structure of the quadrotor's dynamics from
data, capturing effects that would remain hidden to classical approaches. To
the best of our knowledge, this is the first time physics-inspired deep
learning is successfully applied to temporal convolutional networks and to the
system identification task, while concurrently enabling predictive control.Comment: Video: https://youtu.be/dsOtKfuRjE
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
Model-based control requires an accurate model of the system dynamics for
precisely and safely controlling the robot in complex and dynamic environments.
Moreover, in the presence of variations in the operating conditions, the model
should be continuously refined to compensate for dynamics changes. In this
paper, we present a self-supervised learning approach that actively models the
dynamics of nonlinear robotic systems. We combine offline learning from past
experience and online learning from current robot interaction with the unknown
environment. These two ingredients enable a highly sample-efficient and
adaptive learning process, capable of accurately inferring model dynamics in
real-time even in operating regimes that greatly differ from the training
distribution. Moreover, we design an uncertainty-aware model predictive
controller that is heuristically conditioned to the aleatoric (data)
uncertainty of the learned dynamics. This controller actively chooses the
optimal control actions that (i) optimize the control performance and (ii)
improve the efficiency of online learning sample collection. We demonstrate the
effectiveness of our method through a series of challenging real-world
experiments using a quadrotor system. Our approach showcases high resilience
and generalization capabilities by consistently adapting to unseen flight
conditions, while it significantly outperforms classical and adaptive control
baselines
Unifying Foundation Models with Quadrotor Control for Visual Tracking Beyond Object Categories
Visual control enables quadrotors to adaptively navigate using real-time
sensory data, bridging perception with action. Yet, challenges persist,
including generalization across scenarios, maintaining reliability, and
ensuring real-time responsiveness. This paper introduces a perception framework
grounded in foundation models for universal object detection and tracking,
moving beyond specific training categories. Integral to our approach is a
multi-layered tracker integrated with the foundation detector, ensuring
continuous target visibility, even when faced with motion blur, abrupt light
shifts, and occlusions. Complementing this, we introduce a model-free
controller tailored for resilient quadrotor visual tracking. Our system
operates efficiently on limited hardware, relying solely on an onboard camera
and an inertial measurement unit. Through extensive validation in diverse
challenging indoor and outdoor environments, we demonstrate our system's
effectiveness and adaptability. In conclusion, our research represents a step
forward in quadrotor visual tracking, moving from task-specific methods to more
versatile and adaptable operations
AutoCharge: Autonomous Charging for Perpetual Quadrotor Missions
Battery endurance represents a key challenge for long-term autonomy and
long-range operations, especially in the case of aerial robots. In this paper,
we propose AutoCharge, an autonomous charging solution for quadrotors that
combines a portable ground station with a flexible, lightweight charging tether
and is capable of universal, highly efficient, and robust charging. We design
and manufacture a pair of circular magnetic connectors to ensure a precise
orientation-agnostic electrical connection between the ground station and the
charging tether. Moreover, we supply the ground station with an electromagnet
that largely increases the tolerance to localization and control errors during
the docking maneuver, while still guaranteeing smooth un-docking once the
charging process is completed. We demonstrate AutoCharge on a perpetual 10
hours quadrotor flight experiment and show that the docking and un-docking
performance is solidly repeatable, enabling perpetual quadrotor flight
missions
Effetti a livello ipogeo di erbicidi di pre-emergenza in brassicaceae alto erucico.
In recent years, oilseed rape together with no-food oilcrops is rapidly spreading in Italy and even more at european level, as a response to the intense genetic breeding that has improvedthese crops
Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks
This paper presents a new framework for human body part segmentation based on
Deep Convolutional Neural Networks trained using only synthetic data. The
proposed approach achieves cutting-edge results without the need of training
the models with real annotated data of human body parts. Our contributions
include a data generation pipeline, that exploits a game engine for the
creation of the synthetic data used for training the network, and a novel
pre-processing module, that combines edge response maps and adaptive histogram
equalization to guide the network to learn the shape of the human body parts
ensuring robustness to changes in the illumination conditions. For selecting
the best candidate architecture, we perform exhaustive tests on manually
annotated images of real human body limbs. We further compare our method
against several high-end commercial segmentation tools on the body parts
segmentation task. The results show that our method outperforms the other
models by a significant margin. Finally, we present an ablation study to
validate our pre-processing module. With this paper, we release an
implementation of the proposed approach along with the acquired datasets.Comment: Submitted to the 16th International Conference on Intelligent
Autonomous System (IAS