Pub Date : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106654
Shirley Wang, Nicholas Anselmo, Miller Garrett, Ryan Remias, Matthew Trivett, Anders Christoffersen, N. Bezzo
Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular thanks to the multiplicity of operations in which they can be deployed such as surveillance, search and rescue, mapping, transportation, hobby and recreational activities. Although sensors like LIDARs and cameras are often present on such systems for motion planning to avoid obstacles, collisions can still occur in very dense and unstructured environments, especially if disturbances are present. In this work, we research techniques to recover UAVs after a collision has occurred. We note that the on-board sensors, especially the inertial sensor used to stabilize the UAV, run at a high frequencies obtaining hundreds of data points every second. At run-time, this can be leveraged at the moment of a collision to quickly detect and recover the system. Our approach considers knowledge of UAV system dynamics to predict the expected behavior of the vehicle under safe flight conditions and leverage such expectations together with inertial data to detect collisions rapidly (on the order of milliseconds). We also propose a potential field-based approach to map the collision and create the correct reactive maneuver to avoid the collided object and bring the system back to a stable and safe configuration. Experiments are executed using ROS on two micro-quadrotor UAV platforms having different dynamics and performances, while colliding with poles and walls positioned in different configurations. In our results, we are able to show that the UAVs are successfully able to detect and avoid a collision, while also providing a rigorous analysis of the conditions in which the system can recover from imminent collisions.
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Pub Date : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106651
Gabriela Barber, M. Cote, Finley Wetmore, Alec Yerkovich
A United States (US) government agency is charged with delivering US assistance to foreign countries in the aftermath of sudden onset disasters. A major element of this mission is the strategic storage of six critical commodities located at warehouses across the globe. A rapid needs assessment is necessary for determining the commodity types and amounts, which the agency then transports to the disaster location to be distributed to the affected population by partner organizations on the ground. Currently, the entire commodity shipment is sent via a chartered aircraft, obtained through an emergency bid process, to a target airfield for transfer to the distributing organization. Incremental commodity delivery is a potential strategy that would support decision efficiency. Based on the demand on the ground, shipments can be scheduled and sent when they are truly needed, using a variety of transport modes. The agency can reduce financial cost (transportation expenses) and carbon cost (environmental impact) while decreasing port inventory saturation that occurs when the number of commodities delivered exceeds the partner organizations’ distribution capacity. The incremental approach requires complex decision-making to assess available transport options and their financial and carbon efficiencies while meeting target arrival dates/times for each shipment. This project produced a decision support tool that uses historical and GIS data to project sequences of commodity increments and shipment alternatives that meet target arrival times. Users can then conduct informed tradeoff and scenario analyses during their decision process for specific disaster relief operations. The tool presents alternatives based upon four categories of constraints: delivery timing, cost efficiency, carbon efficiency, and the inventory capacity of the arrival port. User inputs include the commodity types and amounts to be delivered, the timeline within which they must arrive, and the target arrival port. The model utilizes multi-objective network optimization to present the potential tradeoffs between the current delivery strategy and the method of incremental shipments timed to meet commodity distribution rates. The tool may identify options that are more functionally and financially beneficial to the agency, its beneficiaries (i.e., more commodities can be provided if transportation costs decrease), and the distributing partners. It can also support an increase in environmentally conscious decisions, which is a growing priority in the humanitarian emergency community. The tool can also be adapted to meet the needs of similar organizations to support their decision-making pertaining to disaster supply chain management.
{"title":"Decision Support Tool for Enhancing Supply Chain Management in Disaster Relief Operations","authors":"Gabriela Barber, M. Cote, Finley Wetmore, Alec Yerkovich","doi":"10.1109/SIEDS49339.2020.9106651","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106651","url":null,"abstract":"A United States (US) government agency is charged with delivering US assistance to foreign countries in the aftermath of sudden onset disasters. A major element of this mission is the strategic storage of six critical commodities located at warehouses across the globe. A rapid needs assessment is necessary for determining the commodity types and amounts, which the agency then transports to the disaster location to be distributed to the affected population by partner organizations on the ground. Currently, the entire commodity shipment is sent via a chartered aircraft, obtained through an emergency bid process, to a target airfield for transfer to the distributing organization. Incremental commodity delivery is a potential strategy that would support decision efficiency. Based on the demand on the ground, shipments can be scheduled and sent when they are truly needed, using a variety of transport modes. The agency can reduce financial cost (transportation expenses) and carbon cost (environmental impact) while decreasing port inventory saturation that occurs when the number of commodities delivered exceeds the partner organizations’ distribution capacity. The incremental approach requires complex decision-making to assess available transport options and their financial and carbon efficiencies while meeting target arrival dates/times for each shipment. This project produced a decision support tool that uses historical and GIS data to project sequences of commodity increments and shipment alternatives that meet target arrival times. Users can then conduct informed tradeoff and scenario analyses during their decision process for specific disaster relief operations. The tool presents alternatives based upon four categories of constraints: delivery timing, cost efficiency, carbon efficiency, and the inventory capacity of the arrival port. User inputs include the commodity types and amounts to be delivered, the timeline within which they must arrive, and the target arrival port. The model utilizes multi-objective network optimization to present the potential tradeoffs between the current delivery strategy and the method of incremental shipments timed to meet commodity distribution rates. The tool may identify options that are more functionally and financially beneficial to the agency, its beneficiaries (i.e., more commodities can be provided if transportation costs decrease), and the distributing partners. It can also support an increase in environmentally conscious decisions, which is a growing priority in the humanitarian emergency community. The tool can also be adapted to meet the needs of similar organizations to support their decision-making pertaining to disaster supply chain management.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SIEDS49339.2020.9106670
Jacob Davis, A. Atchley, Hannah Smitherman, Hailey Simon, N. Tenhundfeld
Automation is becoming ever more prevalent in industrial system designs, and the aviation security industry is no exception. Automated decision aids are regularly used in airport security procedures (as with the TSA) to assist operators scanning baggage for hazardous items. However, there exists serious concerns regarding the human-machine interactions. In order to safely design systems that rely on human oversight, it is imperative that we understand the consequences of design on overall task performance and system usability. To do this, we combined an x-ray screening research paradigm with a ‘wizard-of-oz’ automation verification feature to create a novel research paradigm for exploring monitoring behavior (complacency) and performance in a simulated x-ray screening task. The automation in the x-ray task provided participants with a reliable recommendation to search (hazardous items detected) or clear (no hazardous weapons detected) the baggage 80% of the time. Users’ level of complacency was measured by registering the frequency with which they chose to verify the automation by clicking a “Request Info” button. Monitoring behavior, or the percent of trials in which the user requested additional information from the automation, was low overall. However, it was significantly higher when the automation provided an inaccurate recommendation. These results indicate that users experienced automation bias, the tendency to agree with an automated decision aid. Users also exhibited complacency during the task such that they were no longer actively monitoring the system. Users may have noticed the system was unreliable, given an increase in monitoring behavior in unreliable recommendation trials, but still chose to agree with the automation rather than visually search the baggage for evidence. This demonstrates a unique threat to safety in these domains, wherein users may rely on imperfect automation, rather than their own abilities, even when they believe something is amiss.
{"title":"Measuring Automation Bias and Complacency in an X-Ray Screening Task","authors":"Jacob Davis, A. Atchley, Hannah Smitherman, Hailey Simon, N. Tenhundfeld","doi":"10.1109/SIEDS49339.2020.9106670","DOIUrl":"https://doi.org/10.1109/SIEDS49339.2020.9106670","url":null,"abstract":"Automation is becoming ever more prevalent in industrial system designs, and the aviation security industry is no exception. Automated decision aids are regularly used in airport security procedures (as with the TSA) to assist operators scanning baggage for hazardous items. However, there exists serious concerns regarding the human-machine interactions. In order to safely design systems that rely on human oversight, it is imperative that we understand the consequences of design on overall task performance and system usability. To do this, we combined an x-ray screening research paradigm with a ‘wizard-of-oz’ automation verification feature to create a novel research paradigm for exploring monitoring behavior (complacency) and performance in a simulated x-ray screening task. The automation in the x-ray task provided participants with a reliable recommendation to search (hazardous items detected) or clear (no hazardous weapons detected) the baggage 80% of the time. Users’ level of complacency was measured by registering the frequency with which they chose to verify the automation by clicking a “Request Info” button. Monitoring behavior, or the percent of trials in which the user requested additional information from the automation, was low overall. However, it was significantly higher when the automation provided an inaccurate recommendation. These results indicate that users experienced automation bias, the tendency to agree with an automated decision aid. Users also exhibited complacency during the task such that they were no longer actively monitoring the system. Users may have noticed the system was unreliable, given an increase in monitoring behavior in unreliable recommendation trials, but still chose to agree with the automation rather than visually search the baggage for evidence. This demonstrates a unique threat to safety in these domains, wherein users may rely on imperfect automation, rather than their own abilities, even when they believe something is amiss.","PeriodicalId":331495,"journal":{"name":"2020 Systems and Information Engineering Design Symposium (SIEDS)","volume":"7 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113946475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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