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 $m$ that is sufficient for IA base policy to be stable, and derive a necessary condition on $m$ as time goes to infinity. Our numerical results show that our approach achieves stability for an $m$ 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