2,233 research outputs found
Succession of Coleoptera on freshly killed loblolly pine (Pinus taeda L.) and southern red oak (Quercus falcata Michaux) in Louisiana
Wood is important in forest ecology because its large biomass serves as a nutritional substrate and habitat for many organisms, including Coleoptera, and beetles contribute greatly to nutrient recycling in forests. Overlapping complexes of beetles invade dead wood according to the species of tree, ambient conditions, and most importantly, stage of decomposition. Beetle succession was studied in loblolly pines (Pinus taeda L.) and southern red oaks (Quercus falcata Michx.) by documenting beetle arrival and residency in cut, reassembled, and standing bolts. Twelve trees of each species at Feliciana Preserve in West Feliciana parish, LA were felled during October 2004 and April 2005 for a total of 24 trees sampled from October 2004 – September 2005. Four 48-inch bolts were cut from each felled tree. Each bolt was further cut into eight six-inch sections, reassembled in proper order, and positioned standing upright. Beetles were aspirated from section interfaces weekly the first month and then monthly for the duration of the study. A total 51,119 specimens from 190 taxa were collected from 3822 samples during 18 sampling events. Species richness and abundance were higher on southern red oak wood (144 taxa, 40874 specimens) than loblolly pine (122 taxa, 10245 specimens); abundance was significantly higher. Colonization and species composition patterns of coleoptera were significantly affected by host tree species, the season in which the tree died, the period of decay, the position or height along the woody substrate and many complex interactions of these effects. Loblolly pine bolts showed a slightly more rapid turnover of taxa than southern red oak bolts. Wood characteristics such as loss of moisture, which caused bark to loosen on pines, and higher quality substrate hardwood in oaks presumably account for the greater number of taxa and specimens collected from southern red oak than loblolly pine. This study has increased the number of species known to inhabit recently dead loblolly pine and southern red oak, two economically important tree species. Studies of this nature supplement investigations into the importance of coarse woody debris in forests by documenting ecological patterns of saproxylic coleoptera
Crowd Vetting: Rejecting Adversaries via Collaboration--with Application to Multi-Robot Flocking
We characterize the advantage of using a robot's neighborhood to find and
eliminate adversarial robots in the presence of a Sybil attack. We show that by
leveraging the opinions of its neighbors on the trustworthiness of transmitted
data, robots can detect adversaries with high probability. We characterize a
number of communication rounds required to achieve this result to be a function
of the communication quality and the proportion of legitimate to malicious
robots. This result enables increased resiliency of many multi-robot
algorithms. Because our results are finite time and not asymptotic, they are
particularly well-suited for problems with a time critical nature. We develop
two algorithms, \emph{FindSpoofedRobots} that determines trusted neighbors with
high probability, and \emph{FindResilientAdjacencyMatrix} that enables
distributed computation of graph properties in an adversarial setting. We apply
our methods to a flocking problem where a team of robots must track a moving
target in the presence of adversarial robots. We show that by using our
algorithms, the team of robots are able to maintain tracking ability of the
dynamic target
Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand
We derive a learning framework to generate routing/pickup policies for a
fleet of vehicles tasked with servicing stochastically appearing requests on a
city map. We focus on policies that 1) give rise to coordination amongst the
vehicles, thereby reducing wait times for servicing requests, 2) are
non-myopic, considering a-priori unknown potential future requests, and 3) can
adapt to changes in the underlying demand distribution. Specifically, we are
interested in adapting to fluctuations of actual demand conditions in urban
environments, such as on-peak vs. off-peak hours. We achieve this through a
combination of (i) online play, a lookahead optimization method that improves
the performance of rollout methods via an approximate policy iteration step,
and (ii) an offline approximation scheme that allows for adapting to changes in
the underlying demand model. In particular, we achieve adaptivity of our
learned policy to different demand distributions by quantifying a region of
validity using the q-valid radius of a Wasserstein Ambiguity Set. We propose a
mechanism for switching the originally trained offline approximation when the
current demand is outside the original validity region. In this case, we
propose to use an offline architecture, trained on a historical demand model
that is closer to the current demand in terms of Wasserstein distance. We learn
routing and pickup policies over real taxicab requests in downtown San
Francisco with high variability between on-peak and off-peak hours,
demonstrating the ability of our method to adapt to real fluctuation in demand
distributions. Our numerical results demonstrate that our method outperforms
rollout-based reinforcement learning, as well as several benchmarks based on
classical methods from the field of operations research.Comment: 7 pages, 6 figures, 3 tables, submitted to ICR
Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)
In this paper, we focus on the autonomous multiagent taxi routing problem for
a large urban environment where the location and number of future ride requests
are unknown a-priori, but follow an estimated empirical distribution. Recent
theory has shown that if a base policy is stable then a rollout-based algorithm
with such a base policy produces a near-optimal stable policy. Although,
rollout-based approaches are well-suited for learning cooperative multiagent
policies with considerations for future demand, applying such methods to a
large urban environment can be computationally expensive. Large environments
tend to have a large volume of requests, and hence require a large fleet of
taxis to guarantee stability. In this paper, we aim to address the
computational bottleneck of multiagent (one-at-a-time) rollout, where the
computational complexity grows linearly in the number of agents. We propose an
approximate one-at-a-time rollout-based two-phase algorithm that reduces the
computational cost, while still achieving a stable near-optimal policy. Our
approach partitions the graph into sectors based on the predicted demand and an
user-defined maximum number of agents that can be planned for using the
one-at-a-time rollout approach. The algorithm then applies instantaneous
assignment (IA) for re-balancing taxis across sectors and a sector-wide
one-at-a-time rollout algorithm that is executed in parallel for each sector.
We characterize the number of taxis that is sufficient for IA base policy
to be stable, and derive a necessary condition on as time goes to infinity.
Our numerical results show that our approach achieves stability for an that
satisfies the theoretical conditions. We also empirically demonstrate that our
proposed two-phase algorithm has comparable performance to the one-at-a-time
rollout over the entire map, but with significantly lower runtimes.Comment: 11 pages, 5 figures, 1 lemma, and 2 theorem
Guaranteeing Spoof-Resilient Multi-Robot Networks
Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely disrupted by the Sybil attack, where even a single malicious robot can spoof a large number of fake clients. This paper proposes a new solution to defend against the Sybil attack, without requiring expensive cryptographic key-distribution. Our core contribution is a novel algorithm implemented on commercial Wi-Fi radios that can "sense" spoofers using the physics of wireless signals. We derive theoretical guarantees on how this algorithm bounds the impact of the Sybil Attack on a broad class of robotic coverage problems. We experimentally validate our claims using a team of AscTec quadrotor servers and iRobot Create ground clients, and demonstrate spoofer detection rates over 96%
Projected Push-Pull For Distributed Constrained Optimization Over Time-Varying Directed Graphs (extended version)
We introduce the Projected Push-Pull algorithm that enables multiple agents
to solve a distributed constrained optimization problem with private cost
functions and global constraints, in a collaborative manner. Our algorithm
employs projected gradient descent to deal with constraints and a lazy update
rule to control the trade-off between the consensus and optimization steps in
the protocol. We prove that our algorithm achieves geometric convergence over
time-varying directed graphs while ensuring that the decision variable always
stays within the constraint set. We derive explicit bounds for step sizes that
guarantee geometric convergence based on the strong-convexity and smoothness of
cost functions, and graph properties. Moreover, we provide additional
theoretical results on the usefulness of lazy updates, revealing the challenges
in the analysis of any gradient tracking method that uses projection operators
in a distributed constrained optimization setting. We validate our theoretical
results with numerical studies over different graph types, showing that our
algorithm achieves geometric convergence empirically.Comment: 16 pages, 2 figure
Decentralized Control for Optimizing Communication with Infeasible Regions
In this paper we present a decentralized gradient-based controller that optimizes communication between mobile aerial vehicles and stationary ground sensor vehicles in an environment with infeasible regions. The formulation of our problem as a MIQP is easily implementable, and we show that the addition of a scaling matrix can improve the range of attainable converged solutions by influencing trajectories to move around infeasible regions. We demonstrate the robustness of the controller in 3D simulation with agent failure, and in 10 trials of a multi-agent hardware experiment with quadrotors and ground sensors in an indoor environment. Lastly, we provide analytical guarantees that our controller strictly minimizes a nonconvex cost along agent trajectories, a desirable property for general multi-agent coordination tasks.United States. Army Research Office (Grant W911NF-08-2-0004
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