273 research outputs found
Collision Avoidance of Two Autonomous Quadcopters
Traffic collision avoidance systems (TCAS) are used in order to avoid
incidences of mid-air collisions between aircraft. We present a game-theoretic
approach of a TCAS designed for autonomous unmanned aerial vehicles (UAVs). A
variant of the canonical example of game-theoretic learning, fictitious play,
is used as a coordination mechanism between the UAVs, that should choose
between the alternative altitudes to fly and avoid collision. We present the
implementation results of the proposed coordination mechanism in two
quad-copters flying in opposite directions
A Bio-inspired Collision Detecotr for Small Quadcopter
Sense and avoid capability enables insects to fly versatilely and robustly in
dynamic complex environment. Their biological principles are so practical and
efficient that inspired we human imitating them in our flying machines. In this
paper, we studied a novel bio-inspired collision detector and its application
on a quadcopter. The detector is inspired from LGMD neurons in the locusts, and
modeled into an STM32F407 MCU. Compared to other collision detecting methods
applied on quadcopters, we focused on enhancing the collision selectivity in a
bio-inspired way that can considerably increase the computing efficiency during
an obstacle detecting task even in complex dynamic environment. We designed the
quadcopter's responding operation imminent collisions and tested this
bio-inspired system in an indoor arena. The observed results from the
experiments demonstrated that the LGMD collision detector is feasible to work
as a vision module for the quadcopter's collision avoidance task.Comment: 7 pages, 29 figure
Navigating Assistance System for Quadcopter with Deep Reinforcement Learning
In this paper, we present a deep reinforcement learning method for quadcopter
bypassing the obstacle on the flying path. In the past study, the algorithm
only controls the forward direction about quadcopter. In this letter, we use
two functions to control quadcopter. One is quadcopter navigating function. It
is based on calculating coordination point and find the straight path to the
goal. The other function is collision avoidance function. It is implemented by
deep Q-network model. Both two function will output rotating degree, the agent
will combine both output and turn direct. Besides, deep Q-network can also make
quadcopter fly up and down to bypass the obstacle and arrive at the goal. Our
experimental result shows that the collision rate is 14% after 500 flights.
Based on this work, we will train more complex sense and transfer model to the
real quadcopter.Comment: conferenc
Recent Developments in Aerial Robotics: A Survey and Prototypes Overview
In recent years, research and development in aerial robotics (i.e., unmanned
aerial vehicles, UAVs) has been growing at an unprecedented speed, and there is
a need to summarize the background, latest developments, and trends of UAV
research. Along with a general overview on the definition, types, categories,
and topics of UAV, this work describes a systematic way to identify 1,318
high-quality UAV papers from more than thirty thousand that have been appeared
in the top journals and conferences. On top of that, we provide a bird's-eye
view of UAV research since 2001 by summarizing various statistical information,
such as the year, type, and topic distribution of the UAV papers. We make our
survey list public and believe that the list can not only help researchers
identify, study, and compare their work, but is also useful for understanding
research trends in the field. From our survey results, we find there are many
types of UAV, and to the best of our knowledge, no literature has attempted to
summarize all types in one place. With our survey list, we explain the types
within our survey and outline the recent progress of each. We believe this
summary can enhance readers' understanding on the UAVs and inspire researchers
to propose new methods and new applications.Comment: 14 pages, 16 figures, typos correcte
Decentralized Connectivity Control in Quadcopters: a Field Study of Communication Performance
Redundancy and parallelism make decentralized multi-robot systems appealing
solutions for the exploration of extreme environments. However, effective
cooperation often requires team-wide connectivity and a carefully designed
communication strategy. Several recently proposed decentralized connectivity
maintenance approaches exploit elegant algebraic results drawn from spectral
graph theory. Yet, these proposals are rarely taken beyond simulations or
laboratory implementations. In this work, we present two major contributions:
(i) we describe the full-stack implementation---from hardware to software---of
a decentralized control law for robust connectivity maintenance; and (ii) we
assess, in the field, our setup's ability to correctly exchange all the
necessary information required to maintain connectivity in a team of
quadcopters.Comment: 7 pages, 7 figure
Decentralized Multi-target Tracking in Urban Environments: Overview and Challenges
In multi-target tracking, sensor control involves dynamically configuring
sensors to achieve improved tracking performance. Many of these techniques
focus on sensors with memoryless states (e.g., waveform adaptation, beam
scheduling, and sensor selection), lending themselves to computationally
efficient control strategies. Mobile sensor control for multi-target tracking,
however, is significantly more challenging due to the complexity of the
platform state dynamics. This platform complexity necessitates high-fidelity,
non-myopic control strategies in order to achieve strong tracking performance
while maintaining safe operation. These sensor control techniques are
particularly important in non-cooperative urban surveillance applications
including person of interest, vehicle, and unauthorized UAV interdiction. In
this overview paper, we highlight the current state of the art in mobile sensor
control for multi-target tracking in urban environments. We use this
application to motivate the need for closer collaboration between the
information fusion, tracking, and control research communities across three
challenge areas relevant to the urban surveillance problem.Comment: 22nd International Conference on Information Fusio
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.Comment: 12 pages, 16 figure
Learning Vision-based Cohesive Flight in Drone Swarms
This paper presents a data-driven approach to learning vision-based
collective behavior from a simple flocking algorithm. We simulate a swarm of
quadrotor drones and formulate the controller as a regression problem in which
we generate 3D velocity commands directly from raw camera images. The dataset
is created by simultaneously acquiring omnidirectional images and computing the
corresponding control command from the flocking algorithm. We show that a
convolutional neural network trained on the visual inputs of the drone can
learn not only robust collision avoidance but also coherence of the flock in a
sample-efficient manner. The neural controller effectively learns to localize
other agents in the visual input, which we show by visualizing the regions with
the most influence on the motion of an agent. This weakly supervised saliency
map can be computed efficiently and may be used as a prior for subsequent
detection and relative localization of other agents. We remove the dependence
on sharing positions among flock members by taking only local visual
information into account for control. Our work can therefore be seen as the
first step towards a fully decentralized, vision-based flock without the need
for communication or visual markers to aid detection of other agents
Fast Collision Probability Estimation Based on Finite-Dimensional Monte Carlo Method
The safety concern for unmanned systems, namely the concern for the potential
casualty caused by system abnormalities, has been a bottleneck for their
development, especially in populated areas. Evidently, the collision between
the unmanned system and the obstacles, including both moving and static
objects, accounts for a great proportion of the system abnormalities. The route
planning and corresponding controller are established in order to avoid the
collision, whereas, in the presence of uncertainties, it is possible that the
unmanned system would deviate from the predetermined route and collide with the
obstacles. Therefore, for the safety of unmanned systems, collision probability
estimation and further safety decision are very important. To estimate the
collision probability, the Monte Carlo method could be applied, however, it is
generally rather slow. This paper introduces a fast collision probability
estimation method based on finite-dimensional distribution, whose main idea is
to filter out the sampling points needed and generate the states directly by
samples of finite-dimensional distribution, reducing the estimation time
significantly. Besides, further techniques including the probabilistic
equidistance sampling and dimension reduction, also serve to reduce the
estimation time. The simulation shows that the proposed method reduces over 99%
of the estimation time.Comment: 26 pages, 8 figure
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