622 research outputs found
PAMPC: Perception-Aware Model Predictive Control for Quadrotors
We present the first perception-aware model predictive control framework for
quadrotors that unifies control and planning with respect to action and
perception objectives. Our framework leverages numerical optimization to
compute trajectories that satisfy the system dynamics and require control
inputs within the limits of the platform. Simultaneously, it optimizes
perception objectives for robust and reliable sens- ing by maximizing the
visibility of a point of interest and minimizing its velocity in the image
plane. Considering both perception and action objectives for motion planning
and control is challenging due to the possible conflicts arising from their
respective requirements. For example, for a quadrotor to track a reference
trajectory, it needs to rotate to align its thrust with the direction of the
desired acceleration. However, the perception objective might require to
minimize such rotation to maximize the visibility of a point of interest. A
model-based optimization framework, able to consider both perception and action
objectives and couple them through the system dynamics, is therefore necessary.
Our perception-aware model predictive control framework works in a
receding-horizon fashion by iteratively solving a non-linear optimization
problem. It is capable of running in real-time, fully onboard our lightweight,
small-scale quadrotor using a low-power ARM computer, to- gether with a
visual-inertial odometry pipeline. We validate our approach in experiments
demonstrating (I) the contradiction between perception and action objectives,
and (II) improved behavior in extremely challenging lighting conditions
Towards Long-endurance Flight: Design and Implementation of a Variable-pitch Gasoline-engine Quadrotor
Majority of today's fixed-pitch, electric-power quadrotors have short flight
endurance ( 1 hour) which greatly limits their applications. This paper
presents a design methodology for the construction of a long-endurance
quadrotor using variable-pitch rotors and a gasoline-engine. The methodology
consists of three aspects. Firstly, the rotor blades and gasoline engine are
selected as a pair, so that sufficient lift can be comfortably provided by the
engine. Secondly, drivetrain and airframe are designed. Major challenges
include airframe vibration minimization and power transmission from one engine
to four rotors while keeping alternate rotors contra-rotating. Lastly, a PD
controller is tuned to facilitate preliminary flight tests. The methodology has
been verified by the construction and successful flight of our gasoline
quadrotor prototype, which is designed to have a flight time of 2 to 3 hours
and a maximum take-off weight of 10 kg.Comment: 6 page
Perception-aware time optimal path parameterization for quadrotors
The increasing popularity of quadrotors has given rise to a class of
predominantly vision-driven vehicles. This paper addresses the problem of
perception-aware time optimal path parametrization for quadrotors. Although
many different choices of perceptual modalities are available, the low weight
and power budgets of quadrotor systems makes a camera ideal for on-board
navigation and estimation algorithms. However, this does come with a set of
challenges. The limited field of view of the camera can restrict the visibility
of salient regions in the environment, which dictates the necessity to consider
perception and planning jointly. The main contribution of this paper is an
efficient time optimal path parametrization algorithm for quadrotors with
limited field of view constraints. We show in a simulation study that a
state-of-the-art controller can track planned trajectories, and we validate the
proposed algorithm on a quadrotor platform in experiments.Comment: Accepted to appear at ICRA 202
Agilicious: Open-source and open-hardware agile quadrotor for vision-based flight
Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in terms of perception, planning, learning, and control. A versatile and standardized platform is needed to accelerate research and let practitioners focus on the core problems. To this end, we present Agilicious, a codesigned hardware and software framework tailored to autonomous, agile quadrotor flight. It is completely open source and open hardware and supports both model-based and neural network–based controllers. Also, it provides high thrust-to-weight and torque-to-inertia ratios for agility, onboard vision sensors, graphics processing unit (GPU)–accelerated compute hardware for real-time perception and neural network inference, a real-time flight controller, and a versatile software stack. In contrast to existing frameworks, Agilicious offers a unique combination of flexible software stack and high-performance hardware. We compare Agilicious with prior works and demonstrate it on different agile tasks, using both model-based and neural network–based controllers. Our demonstrators include trajectory tracking at up to 5g and 70 kilometers per hour in a motion capture system, and vision-based acrobatic flight and obstacle avoidance in both structured and unstructured environments using solely onboard perception. Last, we demonstrate its use for hardware-in-the-loop simulation in virtual reality environments. Because of its versatility, we believe that Agilicious supports the next generation of scientific and industrial quadrotor research
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Developing and testing algorithms for autonomous vehicles in real world is an
expensive and time consuming process. Also, in order to utilize recent advances
in machine intelligence and deep learning we need to collect a large amount of
annotated training data in a variety of conditions and environments. We present
a new simulator built on Unreal Engine that offers physically and visually
realistic simulations for both of these goals. Our simulator includes a physics
engine that can operate at a high frequency for real-time hardware-in-the-loop
(HITL) simulations with support for popular protocols (e.g. MavLink). The
simulator is designed from the ground up to be extensible to accommodate new
types of vehicles, hardware platforms and software protocols. In addition, the
modular design enables various components to be easily usable independently in
other projects. We demonstrate the simulator by first implementing a quadrotor
as an autonomous vehicle and then experimentally comparing the software
components with real-world flights.Comment: Accepted for Field and Service Robotics conference 2017 (FSR 2017
Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms
Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal Rapidly-exploring Random Tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.Unión Europea H2020-644271Ministerio de Ciencia, Innovación y Universidades DPI2014-59383-C2-1-
Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this letter, we propose L1 -NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline
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