3,205 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
Towards Adaptive, Self-Configuring Networked Unmanned Aerial Vehicles
Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to simultaneously localize, map and visually infer the challenging environment, even when individual drones are resource-limited in terms of computation and communication due to payload restrictions
Smart environment monitoring through micro unmanned aerial vehicles
In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
Close Formation Flight Missions Using Vision-Based Position Detection System
In this thesis, a formation flight architecture is described along with the implementation and evaluation of a state-of-the-art vision-based algorithm for solving the problem of estimating and tracking a leader vehicle within a close-formation configuration. A vision-based algorithm that uses Darknet architecture and a formation flight control law to track and follow a leader with desired clearance in forward, lateral directions are developed and implemented. The architecture is run on a flight computer that handles the process in real-time while integrating navigation sensors and a stereo camera. Numerical simulations along with indoor and outdoor actual flight tests demonstrate the capabilities of detection and tracking by providing a low cost, compact size and low weight solution for the problem of estimating the location of other cooperative or non-cooperative flying vehicles within a formation architecture
Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision
Signal capture stands in the forefront to perceive and understand the
environment and thus imaging plays the pivotal role in mobile vision. Recent
explosive progresses in Artificial Intelligence (AI) have shown great potential
to develop advanced mobile platforms with new imaging devices. Traditional
imaging systems based on the "capturing images first and processing afterwards"
mechanism cannot meet this unprecedented demand. Differently, Computational
Imaging (CI) systems are designed to capture high-dimensional data in an
encoded manner to provide more information for mobile vision systems.Thanks to
AI, CI can now be used in real systems by integrating deep learning algorithms
into the mobile vision platform to achieve the closed loop of intelligent
acquisition, processing and decision making, thus leading to the next
revolution of mobile vision.Starting from the history of mobile vision using
digital cameras, this work first introduces the advances of CI in diverse
applications and then conducts a comprehensive review of current research
topics combining CI and AI. Motivated by the fact that most existing studies
only loosely connect CI and AI (usually using AI to improve the performance of
CI and only limited works have deeply connected them), in this work, we propose
a framework to deeply integrate CI and AI by using the example of self-driving
vehicles with high-speed communication, edge computing and traffic planning.
Finally, we outlook the future of CI plus AI by investigating new materials,
brain science and new computing techniques to shed light on new directions of
mobile vision systems
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