7,046 research outputs found
A Survey on Passing-through Control of Multi-Robot Systems in Cluttered Environments
This survey presents a comprehensive review of various methods and algorithms
related to passing-through control of multi-robot systems in cluttered
environments. Numerous studies have investigated this area, and we identify
several avenues for enhancing existing methods. This survey describes some
models of robots and commonly considered control objectives, followed by an
in-depth analysis of four types of algorithms that can be employed for
passing-through control: leader-follower formation control, multi-robot
trajectory planning, control-based methods, and virtual tube planning and
control. Furthermore, we conduct a comparative analysis of these techniques and
provide some subjective and general evaluations.Comment: 18 pages, 19 figure
Differential Flatness of Lifting-Wing Quadcopters Subject to Drag and Lift for Accurate Tracking
In this paper, we propose an effective unified control law for accurately
tracking agile trajectories for lifting-wing quadcopters with different
installation angles, which have the capability of vertical takeoff and landing
(VTOL) as well as high-speed cruise flight. First, we derive a differential
flatness transform for the lifting-wing dynamics with a nonlinear model under
coordinated turn condition. To increase the tracking performance on agile
trajectories, the proposed controller incorporates the state and input
variables calculated from differential flatness as feedforward. In particular,
the jerk, the 3-order derivative of the trajectory, is converted into angular
velocity as a feedforward item, which significantly improves the system
bandwidth. At the same time, feedback and feedforward outputs are combined to
deal with external disturbances and model mismatch. The control algorithm has
been thoroughly evaluated in the outdoor flight tests, which show that it can
achieve accurate trajectory tracking
Distributed Control for a Multi-Agent System to Pass through a Connected Quadrangle Virtual Tube
In order to guide the multi-agent system in a cluttered environment, a
connected quadrangle virtual tube is designed for all agents to keep moving
within it, whose basis is called the single trapezoid virtual tube. There is no
obstacle inside the tube, namely the area inside the tube can be seen as a
safety zone. Then, a distributed swarm controller is proposed for the single
trapezoid virtual tube passing problem. This issue is resolved by a gradient
vector field method with no local minima. Formal analyses and proofs are made
to show that all agents are able to pass the single trapezoid virtual tube.
Finally, a modified controller is put forward for convenience in practical use.
For the connected quadrangle virtual tube, a modified switching logic is
proposed to avoid the deadlock and prevent agents from moving outside the
virtual tube. Finally, the effectiveness of the proposed method is validated by
numerical simulations and real experiments.Comment: 12 pages,14 figures. arXiv admin note: substantial text overlap with
arXiv:2112.0100
Distributed Control within a Trapezoid Virtual Tube Containing Obstacles for UAV Swarm Subject to Speed Constraints
For guiding the UAV swarm to pass through narrow openings, a trapezoid
virtual tube is designed in our previous work. In this paper, we generalize its
application range to the condition that there exist obstacles inside the
trapezoid virtual tube and UAVs have strict speed constraints. First, a
distributed vector field controller is proposed for the trapezoid virtual tube
with no obstacle inside. The relationship between the trapezoid virtual tube
and the speed constraints is also presented. Then, a switching logic for the
obstacle avoidance is put forward. The key point is to divide the trapezoid
virtual tube containing obstacles into several sub trapezoid virtual tubes with
no obstacle inside. Formal analyses and proofs are made to show that all UAVs
are able to pass through the trapezoid virtual tube safely. Besides, the
effectiveness of the proposed method is validated by numerical simulations and
real experiments.Comment: 11 pages, 12 figure
Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube
It is often necessary for drones to complete delivery, photography, and
rescue in the shortest time to increase efficiency. Many autonomous drone races
provide platforms to pursue algorithms to finish races as quickly as possible
for the above purpose. Unfortunately, existing methods often fail to keep
training and racing time short in drone racing competitions. This motivates us
to develop a high-efficient learning method by imitating the training
experience of top racing drivers. Unlike traditional iterative learning control
methods for accurate tracking, the proposed approach iteratively learns a
trajectory online to finish the race as quickly as possible. Simulations and
experiments using different models show that the proposed approach is
model-free and is able to achieve the optimal result with low computation
requirements. Furthermore, this approach surpasses some state-of-the-art
methods in racing time on a benchmark drone racing platform. An experiment on a
real quadcopter is also performed to demonstrate its effectiveness
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