Search for: All records

In this work, we address a visbility-based target tracking problem in a polygonal environment in which a group of mobile observers try to maintain a line-of-sight with a mobile intruder. We build a bridge between data mining and visibility-based tracking using a novel tiling scheme for the polygon. First, we propose a tracking strategy for a team of guards located on the tiles to dynamically track an intruder when complete coverage of the polygon cannot be ensured. Next, we propose a novel variant of the Voronoi Diagram to construct navigation strategies for a team of co-located guards to track an intruder from any initial position in the environment. We present empirical analysis to illustrate the efficacy of the proposed tiling scheme. Simulations and testbed demonstrations are present in a video attachment. 

Motivated by the problem of optimal security resource deployment in critical infrastructure systems, we study a non-zero-sum security game over the substations of a power system in which the player payoffs depend upon the maturity of the security resources at each substation according to NERCCIP standards. Extending previous work, we give a structural characterization of the possible types of Nash equilibria in our non-zero-sum additive security game model, present feasibility conditions for equilibria of each type, and propose a novel algorithm to compute an equilibrium. Utilizing our characterization of the possible equilibria in additive security games, we propose a method to obtain a suboptimal solution to the problem of maximizing the expected outcome to the system operator by varying the maturity of security resources deployed at each substation and demonstrate the method by simulation. 

We consider the problem of generating a fixed path for a mobile observer in a polygonal environment that can maintain a line-of-sight with an unpredictable target. In contrast to purely off-line or on-line techniques, we propose a hierarchical tracking strategy in which an off-line path generation technique based on a RRT is coupled with an online feedback-control technique to generate trajectories for the mobile observer. 

This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an mTSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms. 

In this paper, we consider the refuel scheduling problem for a team of ground robots deployed in "aislelike" environments wherein the robots are constrained to move along rows. In order to maintain a minimum service rate or throughput for the ground robots, we investigate the problem of scheduling a team of mobile charging stations deployed to replace the batteries on-board the ground robots without any interruption in their task. We propose two scheduling schemes for the mobile chargers to serve the ground robots for long-term service, and derive the parameters associated with the system required for persistent uninterrupted operation. 

The increasing penetration of cyber systems into smart grids has resulted in these grids being more vulnerable to cyber physical attacks. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. However, a system operator can leverage private information about the underlying network to maintain a strategic advantage over an adversary equipped with superior computational capability and situational awareness. In this work, we examine the following scenario: A malicious entity intrudes the cyber-layer of a power network and trips the transmission lines. The objective of the system operator is to deploy security measures in the cyber-layer to minimize the impact of such attacks. Due to budget constraints, the attacker and the system operator have limits on the maximum number of transmission lines they can attack or defend. We model this adversarial interaction as a resource-constrained attacker-defender game. The computational intractability of solving large security games is well known. However, we exploit the approximately modular behavior of an impact metric known as the disturbance value to arrive at a linear-time algorithm for computing an optimal defense strategy. We validate the efficacy of the proposed strategy against attackers of various capabilities and provide an algorithm for a real-time implementation. 

In this work, we investigate a security game between an attacker and a defender, originally proposed in [6]. As is well known, the combinatorial nature of security games leads to a large cost matrix. Therefore, computing the value and optimal strategy for the players becomes computationally expensive. In this work, we analyze a special class of zero-sum games in which the payoff matrix has a special structure which results from the additive property of the utility function. Based on variational principles, we present structural properties of optimal attacker as well as defender’s strategy. We propose a linear-time algorithm to compute the value based on the structural properties, which is an improvement from our previous result in [6], especially in the context of large-scale zero-sum games. 

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean ( Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars. 

In this paper, we address the visibility-based target tracking problem in which a mobile observer moving along a p-route, which we define as a fixed path for target tracking, tries to keep a mobile target in its field-of-view. By drawing a connection to the watchman's route problem, we find a set of conditions that must be satisfied by the p-route. Then we propose a metric for tracking to estimate a sufficient speed for the observer given the geometry of the environment. We show that the problem of finding the p-route on which the observer requires minimum speed is computationally intractable. We present a technique to find a p-route on which the observer needs at most twice the minimum speed to track the intruder and a reactive motion strategy for the observer.