10,355 research outputs found
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
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
Tracking Multiple Fast Targets With Swarms: Interplay Between Social Interaction and Agent Memory
The task of searching for and tracking of multiple targets is a challenging
one. However, most works in this area do not consider evasive targets that move
faster than the agents comprising the multi-robot system. This is due to the
assumption that the movement patterns of such targets, combined with their
excessive speed, would make the task nearly impossible to accomplish. In this
work, we show that this is not the case and we propose a decentralized search
and tracking strategy in which the level of exploration and exploitation
carried out by the swarm is adjustable. By tuning a swarm's exploration and
exploitation dynamics, we demonstrate that there exists an optimal balance
between the level of exploration and exploitation performed. This optimum
maximizes its tracking performance and changes depending on the number of
targets and the targets' movement profiles. We also show that the use of
agent-based memory is critical in enabling the tracking of an evasive target.
The obtained simulation results are validated through experimental tests with a
decentralized swarm of six robots tracking a virtual fast-moving target
Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking
Current strategies employed for maritime target search and tracking are
primarily based on the use of agents following a predetermined path to perform
a systematic sweep of a search area. Recently, dynamic Particle Swarm
Optimization (PSO) algorithms have been used together with swarming multi-robot
systems (MRS), giving search and tracking solutions the added properties of
robustness, scalability, and flexibility. Swarming MRS also give the end-user
the opportunity to incrementally upgrade the robotic system, inevitably leading
to the use of heterogeneous swarming MRS. However, such systems have not been
well studied and incorporating upgraded agents into a swarm may result in
degraded mission performances. In this paper, we propose a PSO-based strategy
using a topological k-nearest neighbor graph with tunable exploration and
exploitation dynamics with an adaptive repulsion parameter. This strategy is
implemented within a simulated swarm of 50 agents with varying proportions of
fast agents tracking a target represented by a fictitious binary function.
Through these simulations, we are able to demonstrate an increase in the
swarm's collective response level and target tracking performance by
substituting in a proportion of fast buoys.Comment: Accepted for IEEE/MTS OCEANS 2020, Singapor
Random Finite Set Theory and Optimal Control of Large Collaborative Swarms
Controlling large swarms of robotic agents has many challenges including, but
not limited to, computational complexity due to the number of agents,
uncertainty in the functionality of each agent in the swarm, and uncertainty in
the swarm's configuration. This work generalizes the swarm state using Random
Finite Set (RFS) theory and solves the control problem using Model Predictive
Control (MPC) to overcome the aforementioned challenges. Computationally
efficient solutions are obtained via the Iterative Linear Quadratic Regulator
(ILQR). Information divergence is used to define the distance between the swarm
RFS and the desired swarm configuration. Then, a stochastic optimal control
problem is formulated using a modified L2^2 distance. Simulation results using
MPC and ILQR show that swarm intensities converge to a target destination, and
the RFS control formulation can vary in the number of target destinations. ILQR
also provides a more computationally efficient solution to the RFS swarm
problem when compared to the MPC solution. Lastly, the RFS control solution is
applied to a spacecraft relative motion problem showing the viability for this
real-world scenario.Comment: arXiv admin note: text overlap with arXiv:1801.0731
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