7,151 research outputs found
Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios
In this paper, we present a decentralized sensor-level collision avoidance
policy for multi-robot systems, which shows promising results in practical
applications. In particular, our policy directly maps raw sensor measurements
to an agent's steering commands in terms of the movement velocity. As a first
step toward reducing the performance gap between decentralized and centralized
methods, we present a multi-scenario multi-stage training framework to learn an
optimal policy. The policy is trained over a large number of robots in rich,
complex environments simultaneously using a policy gradient based reinforcement
learning algorithm. The learning algorithm is also integrated into a hybrid
control framework to further improve the policy's robustness and effectiveness.
We validate the learned sensor-level collision avoidance policy in a variety
of simulated and real-world scenarios with thorough performance evaluations for
large-scale multi-robot systems. The generalization of the learned policy is
verified in a set of unseen scenarios including the navigation of a group of
heterogeneous robots and a large-scale scenario with 100 robots. Although the
policy is trained using simulation data only, we have successfully deployed it
on physical robots with shapes and dynamics characteristics that are different
from the simulated agents, in order to demonstrate the controller's robustness
against the sim-to-real modeling error. Finally, we show that the
collision-avoidance policy learned from multi-robot navigation tasks provides
an excellent solution to the safe and effective autonomous navigation for a
single robot working in a dense real human crowd. Our learned policy enables a
robot to make effective progress in a crowd without getting stuck. Videos are
available at https://sites.google.com/view/hybridmrc
Decentralized Rendezvous of Nonholonomic Robots with Sensing and Connectivity Constraints
A group of wheeled robots with nonholonomic constraints is considered to
rendezvous at a common specified setpoint with a desired orientation while
maintaining network connectivity and ensuring collision avoidance within the
robots. Given communication and sensing constraints for each robot, only a
subset of the robots are aware or informed of the global destination, and the
remaining robots must move within the network connectivity constraint so that
the informed robots can guide the group to the goal. The mobile robots are also
required to avoid collisions with each other outside a neighborhood of the
common rendezvous point. To achieve the rendezvous control objective,
decentralized time-varying controllers are developed based on a navigation
function framework to steer the robots to perform rendezvous while preserving
network connectivity and ensuring collision avoidance. Only local sensing
feedback, which includes position feedback from immediate neighbors and
absolute orientation measurement, is used to navigate the robots and enables
radio silence during navigation. Simulation results demonstrate the performance
of the developed approach.Comment: 9 pages, 5 figures, submitted to Automatic
Realtime Collision Avoidance for Mobile Robots in Dense Crowds using Implicit Multi-sensor Fusion and Deep Reinforcement Learning
We present a novel learning-based collision avoidance algorithm, CrowdSteer,
for mobile robots operating in dense and crowded environments. Our approach is
end-to-end and uses multiple perception sensors such as a 2-D lidar along with
a depth camera to sense surrounding dynamic agents and compute collision-free
velocities. Our training approach is based on the sim-to-real paradigm and uses
high fidelity 3-D simulations of pedestrians and the environment to train a
policy using Proximal Policy Optimization (PPO). We show that our learned
navigation model is directly transferable to previously unseen virtual and
dense real-world environments. We have integrated our algorithm with
differential drive robots and evaluated its performance in narrow scenarios
such as dense crowds, narrow corridors, T-junctions, L-junctions, etc. In
practice, our approach can perform real-time collision avoidance and generate
smooth trajectories in such complex scenarios. We also compare the performance
with prior methods based on metrics such as trajectory length, mean time to
goal, success rate, and smoothness and observe considerable improvement.Comment: 8 pages, 7 figure
3-D Reciprocal Collision Avoidance on Physical Quadrotor Helicopters with On-Board Sensing for Relative Positioning
In this paper, we present an implementation of 3-D reciprocal collision
avoidance on real quadrotor helicopters where each quadrotor senses the
relative position and velocity of other quadrotors using an on-board camera. We
show that using our approach, quadrotors are able to successfully avoid
pairwise collisions in GPS and motion-capture denied environments, without
communication between the quadrotors, and even when human operators
deliberately attempt to induce collisions. To our knowledge, this is the first
time that reciprocal collision avoidance has been successfully implemented on
real robots where each agent independently observes the others using on-board
sensors. We theoretically analyze the response of the collision-avoidance
algorithm to the violated assumptions by the use of real robots. We
quantitatively analyze our experimental results. A particularly striking
observation is that at times the quadrotors exhibit "reciprocal dance"
behavior, which is also observed when humans move past each other in
constrained environments. This seems to be the result of sensing uncertainty,
which causes both robots involved to have a different belief about the relative
positions and velocities and, as a result, choose the same side on which to
pass.Comment: 8 pages, 9 Figures, 1 Tabl
Navigation Function Based Decentralized Control of A Multi-Agent System with Network Connectivity Constraints
A wide range of applications require or can benefit from collaborative
behavior of a group of agents. The technical challenge addressed in this
chapter is the development of a decentralized control strategy that enables
each agent to independently navigate to ensure agents achieve a collective goal
while maintaining network connectivity. Specifically, cooperative controllers
are developed for networked agents with limited sensing and network
connectivity constraints. By modeling the interaction among the agents as a
graph, several different approaches to address the problems of preserving
network connectivity are presented, with the focus on a method that utilizes
navigation function frameworks. By modeling network connectivity constraints as
artificial obstacles in navigation functions, a decentralized control strategy
is presented in two particular applications, formation control and rendezvous
for a system of autonomous agents, which ensures global convergence to the
unique minimum of the potential field (i.e., desired formation or desired
destination) while preserving network connectivity. Simulation results are
provided to demonstrate the developed strategy.Comment: 16 pages, 9 figures, submitted to NATO Science for Peace and Security
Series by IOS Press. arXiv admin note: substantial text overlap with
arXiv:1402.563
Particle Swarm Optimization Based Source Seeking
Signal source seeking using autonomous vehicles is a complex problem. The
complexity increases manifold when signal intensities captured by physical
sensors onboard are noisy and unreliable. Added to the fact that signal
strength decays with distance, noisy environments make it extremely difficult
to describe and model a decay function. This paper addresses our work with
seeking maximum signal strength in a continuous electromagnetic signal source
with mobile robots, using Particle Swarm Optimization (PSO). A one to one
correspondence with swarm members in a PSO and physical Mobile robots is
established and the positions of the robots are iteratively updated as the PSO
algorithm proceeds forward. Since physical robots are responsive to swarm
position updates, modifications were required to implement the interaction
between real robots and the PSO algorithm. The development of modifications
necessary to implement PSO on mobile robots, and strategies to adapt to real
life environments such as obstacles and collision objects are presented in this
paper. Our findings are also validated using experimental testbeds.Comment: 13 pages, 12 figure
A Hierarchical Collision Avoidance Architecture for Multiple Fixed-Wing UAVs in an Integrated Airspace
This paper studies the collision avoidance problem for autonomous multiple
fixedwing UAVs in the complex integrated airspace. By studying and combining
the online path planning method, the distributed model predictive control
algorithm, and the geometric reactive control approach, a three-layered
collision avoidance system integrating conflict detection and resolution
procedures is developed for multiple fixed-wing UAVs modeled by unicycle
kinematics subject to input constraints. The effectiveness of the proposed
methodology is evaluated and validated via test results of comparative
simulations under both deterministic and probabilistic sensing conditions.Comment: 6 pages, 3 figures, 21st IFAC World Congress 202
A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking
Wild-land fire fighting is a hazardous job. A key task for firefighters is to
observe the "fire front" to chart the progress of the fire and areas that will
likely spread next. Lack of information of the fire front causes many
accidents. Using Unmanned Aerial Vehicles (UAVs) to cover wildfire is promising
because it can replace humans in hazardous fire tracking and significantly
reduce operation costs. In this paper we propose a distributed control
framework designed for a team of UAVs that can closely monitor a wildfire in
open space, and precisely track its development. The UAV team, designed for
flexible deployment, can effectively avoid in-flight collisions and cooperate
well with neighbors. They can maintain a certain height level to the ground for
safe flight above fire. Experimental results are conducted to demonstrate the
capabilities of the UAV team in covering a spreading wildfire.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0263
Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle
Navigation underwater traditionally is done by keeping a safe distance from
obstacles, resulting in "fly-overs" of the area of interest. Movement of an
autonomous underwater vehicle (AUV) through a cluttered space, such as a
shipwreck or a decorated cave, is an extremely challenging problem that has not
been addressed in the past. This paper proposes a novel navigation framework
utilizing an enhanced version of Trajopt for fast 3D path-optimization planning
for AUVs. A sampling-based correction procedure ensures that the planning is
not constrained by local minima, enabling navigation through narrow spaces. Two
different modalities are proposed: planning with a known map results in
efficient trajectories through cluttered spaces; operating in an unknown
environment utilizes the point cloud from the visual features detected to
navigate efficiently while avoiding the detected obstacles. The proposed
approach is rigorously tested, both on simulation and in-pool experiments,
proven to be fast enough to enable safe real-time 3D autonomous navigation for
an AUV.Comment: ICRA 202
A survey on unmanned aerial vehicle collision avoidance systems
Collision avoidance is a key factor in enabling the integration of unmanned
aerial vehicle into real life use, whether it is in military or civil
application. For a long time there have been a large number of works to address
this problem; therefore a comparative summary of them would be desirable. This
paper presents a survey on the major collision avoidance systems developed in
up to date publications. Each collision avoidance system contains two main
parts: sensing and detection, and collision avoidance. Based on their
characteristics each part is divided into different categories; and those
categories are explained, compared and discussed about advantages and
disadvantages in this paper.Comment: This is only a draf
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