1,098 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire
Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the UniversitĂ© de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent rĂ©alisĂ©e Ă lâaide dâun capteur situĂ© au bout dâune perche que lâopĂ©rateur introduit dans le chantier, depuis une zone sĂ©curisĂ©e. Le capteur Ă©met des faisceaux laser qui fournissent la distance Ă un mur dĂ©tectĂ©, crĂ©ant ainsi une carte en 3D. Ceci produit des zones dâombres et une faible densitĂ© de points sur les parois Ă©loignĂ©es. Pour relever ces dĂ©fis, une Ă©quipe de recherche de lâUniversitĂ© de Sherbrooke conçoit un drone filaire Ă©quipĂ© dâun LiDAR rotatif pour cette mission, bĂ©nĂ©ficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimitĂ©, un partage de calcul et une communication en temps rĂ©el. Pour une compatibilitĂ© avec le mouvement du drone lors des coincements du fil, la longueur excĂ©dante est intĂ©grĂ©e dans une bobine embarquĂ©e, qui contribue Ă la charge utile du drone. Lors dâun pilotage manuel, le facteur humain entraĂźne des problĂšmes de perception et comprĂ©hension dâun environnement 3D virtuel, et dâexĂ©cution dâune mission optimale. Cette thĂšse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et lâexploration. Le systĂšme doit calculer une trajectoire qui cartographie lâenvironnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquĂ©e. La planification de trajectoire Ă lâaide dâun Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin rĂ©alisable, mais lâoptimisation est coĂ»teuse en calcul et la performance est variable et imprĂ©visible. Lâexploration par la mĂ©thode des frontiĂšres est reprĂ©sentative de lâespace Ă explorer et le chemin peut ĂȘtre optimisĂ© en rĂ©solvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considĂšrent que le cas 2D et nâoptimisent pas le chemin global. Pour relever ces dĂ©fis, cette thĂšse prĂ©sente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin Ă©gal ou plus court que les algorithmes existants en un temps de calcul jusquâĂ 70% plus court que le deuxiĂšme meilleur algorithme dans un environnement reprĂ©sentatif. Une version modifiĂ©e de RRT-connect calcule un chemin rĂ©alisable, raccourci avec une technique dĂ©terministe qui tire profit des noeuds intermĂ©diaires prĂ©alablement ajoutĂ©s. Le deuxiĂšme algorithme, TAPE, est la premiĂšre mĂ©thode dâexploration de cavitĂ©s en 3D qui minimise le temps de mission et la longueur du fil dĂ©roulĂ©. En moyenne, le trajet global est 4% plus long que la mĂ©thode qui rĂ©sout le TSP, mais le fil reste sous la longueur autorisĂ©e dans 100% des cas simulĂ©s, contre 53% avec la mĂ©thode initiale. Lâapproche utilise une architecture hiĂ©rarchique Ă 2 niveaux : la planification globale rĂ©sout un TSP aprĂšs extraction des frontiĂšres, et la planification locale minimise le coĂ»t du chemin et la longueur de fil via une fonction de dĂ©cision. LâintĂ©gration de ces deux outils dans le NetherDrone produit un systĂšme intelligent pour lâexploration autonome, dotĂ© de fonctionnalitĂ©s semi-autonomes pour une interaction avec lâopĂ©rateur. Les travaux rĂ©alisĂ©s ouvrent la porte Ă de nouvelles approches de navigation dans le domaine des missions dâinspection, de cartographie et de recherche et sauvetage
Structured Semidefinite Programming for Recovering Structured Preconditioners
We develop a general framework for finding approximately-optimal
preconditioners for solving linear systems. Leveraging this framework we obtain
improved runtimes for fundamental preconditioning and linear system solving
problems including the following. We give an algorithm which, given positive
definite with
nonzero entries, computes an -optimal
diagonal preconditioner in time , where is the
optimal condition number of the rescaled matrix. We give an algorithm which,
given that is either the pseudoinverse
of a graph Laplacian matrix or a constant spectral approximation of one, solves
linear systems in in time. Our diagonal
preconditioning results improve state-of-the-art runtimes of
attained by general-purpose semidefinite programming, and our solvers improve
state-of-the-art runtimes of where is the
current matrix multiplication constant. We attain our results via new
algorithms for a class of semidefinite programs (SDPs) we call
matrix-dictionary approximation SDPs, which we leverage to solve an associated
problem we call matrix-dictionary recovery.Comment: Merge of arXiv:1812.06295 and arXiv:2008.0172
The Public Performance Of Sanctions In Insolvency Cases: The Dark, Humiliating, And Ridiculous Side Of The Law Of Debt In The Italian Experience. A Historical Overview Of Shaming Practices
This study provides a diachronic comparative overview of how the law of debt has been applied by certain institutions in Italy. Specifically, it offers historical and comparative insights into the public performance of sanctions for insolvency through shaming and customary practices in Roman Imperial Law, in the Middle Ages, and in later periods.
The first part of the essay focuses on the Roman bonorum cessio culo nudo super lapidem and on the medieval customary institution called pietra della vergogna (stone of shame), which originates from the Roman model.
The second part of the essay analyzes the social function of the zecca and the pittima Veneziana during the Republic of Venice, and of the practice of lu soldate a castighe (no translation is possible).
The author uses a functionalist approach to apply some arguments and concepts from the current context to this historical analysis of ancient institutions that we would now consider ridiculous.
The article shows that the customary norms that play a crucial regulatory role in online interactions today can also be applied to the public square in the past. One of these tools is shaming. As is the case in contemporary online settings, in the public square in historic periods, shaming practices were used to enforce the rules of civility in a given community. Such practices can be seen as virtuous when they are intended for use as a tool to pursue positive change in forces entrenched in the culture, and thus to address social wrongs considered outside the reach of the law, or to address human rights abuses
University of Windsor Graduate Calendar 2023 Spring
https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp
DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities
With the electrification of transportation, the rising uptake of electric
vehicles (EVs) might stress distribution networks significantly, leaving their
performance degraded and stability jeopardized. To accommodate these new loads
cost-effectively, modern power grids require coordinated or ``smart'' charging
strategies capable of optimizing EV charging scheduling in a scalable and
efficient fashion. With this in view, the present work focuses on reservation
management programs for large-scale, networked EV charging stations. We
formulate a time-coupled binary optimization problem that maximizes EV users'
total welfare gain while accounting for the network's available power capacity
and stations' occupancy limits. To tackle the problem at scale while retaining
high solution quality, a data-driven optimization framework combining
techniques from the fields of Deep Learning and Approximation Algorithms is
introduced. The framework's key ingredient is a novel input-output processing
scheme for neural networks that allows direct extrapolation to problem sizes
substantially larger than those included in the training set. Extensive
numerical simulations based on synthetic and real-world data traces verify the
effectiveness and superiority of the presented approach over two representative
scheduling algorithms. Lastly, we round up the contributions by listing several
immediate extensions to the proposed framework and outlining the prospects for
further exploration
Independent Sets in Elimination Graphs with a Submodular Objective
Maximum weight independent set (MWIS) admits a -approximation in
inductively -independent graphs and a -approximation in
-perfectly orientable graphs. These are a a parameterized class of graphs
that generalize -degenerate graphs, chordal graphs, and intersection graphs
of various geometric shapes such as intervals, pseudo-disks, and several
others. We consider a generalization of MWIS to a submodular objective. Given a
graph and a non-negative submodular function , the goal is to approximately solve where is the set of independent sets of . We obtain an
-approximation for this problem in the two mentioned graph
classes. The first approach is via the multilinear relaxation framework and a
simple contention resolution scheme, and this results in a randomized algorithm
with approximation ratio at least . This approach also yields
parallel (or low-adaptivity) approximations. Motivated by the goal of designing
efficient and deterministic algorithms, we describe two other algorithms for
inductively -independent graphs that are inspired by work on streaming
algorithms: a preemptive greedy algorithm and a primal-dual algorithm. In
addition to being simpler and faster, these algorithms, in the monotone
submodular case, yield the first deterministic constant factor approximations
for various special cases that have been previously considered such as
intersection graphs of intervals, disks and pseudo-disks.Comment: Extended abstract to appear in Proceedings of APPROX 2023. v2
corrects technical typos in few place
Online Combinatorial Auctions for Resource Allocation with Supply Costs and Capacity Limits
We study a general online combinatorial auction problem in algorithmic
mechanism design. A provider allocates multiple types of capacity-limited
resources to customers that arrive in a sequential and arbitrary manner. Each
customer has a private valuation function on bundles of resources that she can
purchase (e.g., a combination of different resources such as CPU and RAM in
cloud computing). The provider charges payment from customers who purchase a
bundle of resources and incurs an increasing supply cost with respect to the
totality of resources allocated. The goal is to maximize the social welfare,
namely, the total valuation of customers for their purchased bundles, minus the
total supply cost of the provider for all the resources that have been
allocated. We adopt the competitive analysis framework and provide posted-price
mechanisms with optimal competitive ratios. Our pricing mechanism is optimal in
the sense that no other online algorithms can achieve a better competitive
ratio. We validate the theoretic results via empirical studies of online
resource allocation in cloud computing. Our numerical results demonstrate that
the proposed pricing mechanism is competitive and robust against system
uncertainties and outperforms existing benchmarks.Comment: arXiv admin note: text overlap with arXiv:2004.0964
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