Laura E. Barnes

Swarm Formation Control Utilizing Elliptical Surfaces and Limiting Functions

In this paper, we present a strategy for organizing swarms of unmanned vehicles into a formation by utilizing artificial potential fields that were generated from normal and sigmoid functions. These functions construct the surface on which swarm members travel, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables that force the swarm to behave according to set constraints, formation, and member spacing. The artificial potential functions and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation and user-defined constraints. This approach is computationally efficient and scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of 10 and 40 robots that follow circle, ellipse, and wedge formations. Experimental results are included to demonstrate the applicability of the approach on a swarm of four custom-built unmanned ground vehicles (UGVs).

Unmanned ground vehicle swarm formation control using potential fields

A novel technique is presented for organizing swarms of robots into formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions construct the surface swarm members travel on, controlling the overall swarm geometry and the individual member spacing. Limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of four and ten particles following circle, ellipse and wedge formations. Experimental results are also included with four unmanned ground vehicles (UGV).

Multi-UAV Simulator Utilizing X-Plane

This paper describes the development of a simulator for multiple Unmanned Aerial Vehicles (UAVs) utilizing the commercially available simulator X-Plane and Matlab. Coordinated control of unmanned systems is currently being researched for a wide range of applications, including search and rescue, convoy protection, and building clearing to name a few. Although coordination and control of Unmanned Ground Vehicles (UGVs) has been a heavily researched area, the extension towards controlling multiple UAVs has seen minimal attention. This lack of development is due to numerous issues including the difficulty in realistically modeling and simulating multiple UAVs. This work attempts to overcome these limitations by creating an environment that can simultaneously simulate multiple air vehicles as well as provide state data and control input for the individual vehicles using a heavily developed and commercially available flight simulator (X-Plane). This framework will allow researchers to study multi-UAV control algorithms using realistic unmanned and manned aircraft models in real-world modeled environments. Validation of the system’s ability is shown through the demonstration of formation control algorithms implemented on four UAV helicopters with formation and navigation controllers built in Matlab/Simulink.

iCrowd : Near-Optimal Task Allocation for Piggyback Crowdsensing

This paper first defines a novel spatial-temporal coverage metric, k-depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd, a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and k-depth coverage objectives/ constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals-i.e., Goal. 1 near-maximal k-depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined k-depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher k-depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same k-depth coverage constraint.

Evidence of the need for social intelligence in rescue robots

This study investigates data collected from operating an Inuktun robot in an urban search and rescue (USAR) confined space training exercise task at Virginia Beach Training Center. Data was collected from coding approximately one hour of video. The video had no sound so all analysis is based on the video feed. Indicators of communication, gestures, physical interactions with the robot, and robot movements were analyzed. The findings indicate that the robot emerges as a virtual presence for the support of the team outside of the confined space. The team members spontaneously responded socially to the robot despite the robot not being engineered to have a social intelligence. This confirms numerous studies in the cognitive science, psychology, and affective computing literature that robots need a social interface regards of domain.

Heterogeneous Swarm Formation Control Using Bivariate Normal Functions to Generate Potential Fields

A novel method is presented for dynamic heterogeneous swarm formation control with potential fields generated from bivariate normal probability density functions (pdfs) used to construct the surface which swarm members move on, controlling swarm geometry, individual member spacing, and managing obstacle avoidance. Limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints. Bivariate normal functions and limiting functions are combined to guarantee obstacle avoidance and control swarm member orientation and swarm movement as a whole. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. The method is applied to a simple vehicle model and simulation results are presented for a heterogeneous swarm of ten robot vehicles following line and ellipse formations

Swarm Formation Control with Potential Fields Formed by Bivariate Normal Functions

A novel method is presented for swarm formation control with potential fields generated from bivariate normal probability density functions (pdfs) that construct the surface the swarm members move upon controlling the swarm geometry and member spacing as well as manage obstacle avoidance. Limiting functions provide tighter swarm control by modifying and adjusting a set of control variables, forcing the swarm to behave according to set constraints. Bivariate normal functions and limiting functions are combined to guarantee obstacle avoidance and control swarm member orientation and swarm movement as a whole. The presented approach, compared to others, is simple, computationally efficient, and scales well to different swarm sizes and swarm models. The method is applied to a simple vehicle model, and simulation results are presented on a homogeneous swarm of ten robot vehicles for different formations

Extensions to Regret-based Decision Curve Analysis: An application to hospice referral for terminal patients

BackgroundDespite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed.MethodsWe present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care.ResultsThe web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regretDCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patients threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available.ConclusionsWe present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned.

Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies

The burden of entry into mobile crowdsensing (MCS) is prohibitively high for human-subject researchers who lack a technical orientation. As a result, the benefits of MCS remain beyond the reach of research communities (e.g., psychologists) whose expertise in the study of human behavior might advance applications and understanding of MCS systems. This paper presents Sensus, a new MCS system for human-subject studies that bridges the gap between human-subject researchers and MCS methods. Sensus alleviates technical burdens with on-device, GUI-based design of sensing plans, simple and efficient distribution of sensing plans to study participants, and uniform participant experience across iOS and Android devices. Sensing plans support many hardware and software sensors, automatic deployment of sensor-triggered surveys, and double-blind assignment of participants within randomized controlled trials. Sensus offers these features to study designers without requiring knowledge of markup and programming languages. We demonstrate the feasibility of using Sensus within two human-subject studies, one in psychology and one in engineering. Feedback from non-technical users indicates that Sensus is an effective and low-burden system for MCS-based data collection and analysis.

Effective robot team control methodologies for battlefield applications

In this paper, we present algorithms and display concepts that allow Soldiers to efficiently interact with a robotic swarm that is participating in a representative convoy mission. A critical aspect of swarm control, especially in disrupted or degraded conditions, is Soldier-swarm interaction-the Soldier must be kept cognizant of swarm operations through an interface that allows him or her to monitor status and/or institute corrective actions. We provide a control method for the swarm that adapts easily to changing battlefield conditions, metrics and supervisory algorithms that enable swarm members to economically monitor changes in swarm status as they execute the mission, and display concepts that can efficiently and effectively communicate swarm status to Soldiers in challenging battlefield environments.