2,772 research outputs found
Vision-based Learning for Drones: A Survey
Drones as advanced cyber-physical systems are undergoing a transformative
shift with the advent of vision-based learning, a field that is rapidly gaining
prominence due to its profound impact on drone autonomy and functionality.
Different from existing task-specific surveys, this review offers a
comprehensive overview of vision-based learning in drones, emphasizing its
pivotal role in enhancing their operational capabilities under various
scenarios. We start by elucidating the fundamental principles of vision-based
learning, highlighting how it significantly improves drones' visual perception
and decision-making processes. We then categorize vision-based control methods
into indirect, semi-direct, and end-to-end approaches from the
perception-control perspective. We further explore various applications of
vision-based drones with learning capabilities, ranging from single-agent
systems to more complex multi-agent and heterogeneous system scenarios, and
underscore the challenges and innovations characterizing each area. Finally, we
explore open questions and potential solutions, paving the way for ongoing
research and development in this dynamic and rapidly evolving field. With
growing large language models (LLMs) and embodied intelligence, vision-based
learning for drones provides a promising but challenging road towards
artificial general intelligence (AGI) in 3D physical world
Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios
Perching on the moving platforms is a promising solution to enhance the
endurance and operational range of quadrotors, which could benefit the
efficiency of a variety of air-ground cooperative tasks. To ensure robust
perching, tracking with a steady relative state and reliable perception is a
prerequisite. This paper presents an adaptive dynamic tracking and perching
scheme for autonomous quadrotors to achieve tight integration with moving
platforms. For reliable perception of dynamic targets, we introduce elastic
visibility-aware planning to actively avoid occlusion and target loss.
Additionally, we propose a flexible terminal adjustment method that adapts the
changes in flight duration and the coupled terminal states, ensuring full-state
synchronization with the time-varying perching surface at various angles. A
relaxation strategy is developed by optimizing the tangential relative speed to
address the dynamics and safety violations brought by hard boundary conditions.
Moreover, we take SE(3) motion planning into account to ensure no collision
between the quadrotor and the platform until the contact moment. Furthermore,
we propose an efficient spatiotemporal trajectory optimization framework
considering full state dynamics for tracking and perching. The proposed method
is extensively tested through benchmark comparisons and ablation studies. To
facilitate the application of academic research to industry and to validate the
efficiency of our scheme under strictly limited computational resources, we
deploy our system on a commercial drone (DJI-MAVIC3) with a full-size
sport-utility vehicle (SUV). We conduct extensive real-world experiments, where
the drone successfully tracks and perches at 30~km/h (8.3~m/s) on the top of
the SUV, and at 3.5~m/s with 60{\deg} inclined into the trunk of the SUV
Vision and Learning for Deliberative Monocular Cluttered Flight
Cameras provide a rich source of information while being passive, cheap and
lightweight for small and medium Unmanned Aerial Vehicles (UAVs). In this work
we present the first implementation of receding horizon control, which is
widely used in ground vehicles, with monocular vision as the only sensing mode
for autonomous UAV flight in dense clutter. We make it feasible on UAVs via a
number of contributions: novel coupling of perception and control via relevant
and diverse, multiple interpretations of the scene around the robot, leveraging
recent advances in machine learning to showcase anytime budgeted cost-sensitive
feature selection, and fast non-linear regression for monocular depth
prediction. We empirically demonstrate the efficacy of our novel pipeline via
real world experiments of more than 2 kms through dense trees with a quadrotor
built from off-the-shelf parts. Moreover our pipeline is designed to combine
information from other modalities like stereo and lidar as well if available
Adaptive Airborne Separation to Enable UAM Autonomy in Mixed Airspace
The excitement and promise generated by Urban Air Mobility (UAM) concepts have inspired both new entrants and large aerospace companies throughout the world to invest hundreds of millions in research and development of air vehicles, both piloted and unpiloted, to fulfill these dreams. The management and separation of all these new aircraft have received much less attention, however, and even though NASAs lead is advancing some promising concepts for Unmanned Aircraft Systems (UAS) Traffic Management (UTM), most operations today are limited to line of sight with the vehicle, airspace reservation and geofencing of individual flights. Various schemes have been proposed to control this new traffic, some modeled after conventional air traffic control and some proposing fully automatic management, either from a ground-based entity or carried out on board among the vehicles themselves. Previous work has examined vehicle-based traffic management in the very low altitude airspace within a metroplex called UTM airspace in which piloted traffic is rare. A management scheme was proposed in that work that takes advantage of the homogeneous nature of the traffic operating in UTM airspace. This paper expands that concept to include a traffic management plan usable at all altitudes desired for electric Vertical Takeoff and Landing urban and short-distance, inter-city transportation. The interactions with piloted aircraft operating under both visual and instrument flight rules are analyzed, and the role of Air Traffic Control services in the postulated mixed traffic environment is covered. Separation values that adapt to each type of traffic encounter are proposed, and the relationship between required airborne surveillance range and closure speed is given. Finally, realistic scenarios are presented illustrating how this concept can reliably handle the density and traffic mix that fully implemented and successful UAM operations would entail
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