Drones or unoccupied aerial vehicles are rapidly being used for a spectrum of applications, including replacing traditional occupied aircraft as a means of approaching wildlife from the air. Though less intrusive to wildlife than occupied aircraft, drones can still cause varying levels of disturbance. Policies and protocols to guide lowest-impact drone flights are most likely to succeed if considerations are derived from knowledge from scientific literature. This study examines trends in the scientific literature on using drones to approach wildlife between 2000 and 2020, specifically in relation to the type of publications, scientific journals works are published in, the purposes of drone flights reported, taxa studied, and locations of studies. From 223 publications, we observed a large increase in relevant scientific literature, the majority of which were peer-reviewed articles published across 87 scientific journals. The largest proportions of peer-reviewed research articles related to aquatic mammals or aquatic birds, and the use or trial of drone flights for conducting population surveys, animal detection or investigations of animal responses to drone flights. The largest proportion of articles were studies conducted in North America and Australia. Since animal responses to drone flights vary between taxa, populations, and geographic locations, we encourage further growth in the volume of relevant scientific literature needed to inform policies and protocols for specific taxa and/or locations, particularly where knowledge gaps exist.
{"title":"An examination of trends in the growing scientific literature on approaching wildlife with drones","authors":"M. Mo, Katarina Bonatakis","doi":"10.1139/dsa-2021-0003","DOIUrl":"https://doi.org/10.1139/dsa-2021-0003","url":null,"abstract":"Drones or unoccupied aerial vehicles are rapidly being used for a spectrum of applications, including replacing traditional occupied aircraft as a means of approaching wildlife from the air. Though less intrusive to wildlife than occupied aircraft, drones can still cause varying levels of disturbance. Policies and protocols to guide lowest-impact drone flights are most likely to succeed if considerations are derived from knowledge from scientific literature. This study examines trends in the scientific literature on using drones to approach wildlife between 2000 and 2020, specifically in relation to the type of publications, scientific journals works are published in, the purposes of drone flights reported, taxa studied, and locations of studies. From 223 publications, we observed a large increase in relevant scientific literature, the majority of which were peer-reviewed articles published across 87 scientific journals. The largest proportions of peer-reviewed research articles related to aquatic mammals or aquatic birds, and the use or trial of drone flights for conducting population surveys, animal detection or investigations of animal responses to drone flights. The largest proportion of articles were studies conducted in North America and Australia. Since animal responses to drone flights vary between taxa, populations, and geographic locations, we encourage further growth in the volume of relevant scientific literature needed to inform policies and protocols for specific taxa and/or locations, particularly where knowledge gaps exist.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45847181","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}
Patrick M. Jagielski, Andrew F. Barnas, H. Gilchrist, E. Richardson, O. Love, C. Semeniuk
Climate-induced sea-ice loss represents the greatest threat to polar bears (Ursus maritimus), and utilizing drones to characterize behavioural responses to sea-ice loss is valuable to forecasting polar bear persistence. In this manuscript, we review previously published literature and draw on our own experience of using multirotor aerial drones to study polar bear behaviour in the Canadian Arctic. Specifically, we suggest that drones can minimize human-bear conflicts by allowing users to observe bears from a safe vantage point; produce high-quality behavioural data that can be reviewed as many times as needed and shared with multiple stakeholders; and foster knowledge generation through co-production with northern communities. We posit that in some instances drones may be considered as an alternative tool for studying polar bear foraging behaviour, interspecific interactions, human-bear interactions, human safety and conflict mitigation, and den-site location at individual-level, small spatial scales. Finally, we discuss flying techniques to ensure ethical operation around polar bears, regulatory requirements to consider, and recommend that future research focus on understanding polar bears’ behavioural and physiological responses to drones and the efficacy of drones as a deterrent tool for safety purposes.
{"title":"The utility of drones for studying polar bear behaviour in the Canadian Arctic: opportunities and recommendations","authors":"Patrick M. Jagielski, Andrew F. Barnas, H. Gilchrist, E. Richardson, O. Love, C. Semeniuk","doi":"10.1139/dsa-2021-0018","DOIUrl":"https://doi.org/10.1139/dsa-2021-0018","url":null,"abstract":"Climate-induced sea-ice loss represents the greatest threat to polar bears (Ursus maritimus), and utilizing drones to characterize behavioural responses to sea-ice loss is valuable to forecasting polar bear persistence. In this manuscript, we review previously published literature and draw on our own experience of using multirotor aerial drones to study polar bear behaviour in the Canadian Arctic. Specifically, we suggest that drones can minimize human-bear conflicts by allowing users to observe bears from a safe vantage point; produce high-quality behavioural data that can be reviewed as many times as needed and shared with multiple stakeholders; and foster knowledge generation through co-production with northern communities. We posit that in some instances drones may be considered as an alternative tool for studying polar bear foraging behaviour, interspecific interactions, human-bear interactions, human safety and conflict mitigation, and den-site location at individual-level, small spatial scales. Finally, we discuss flying techniques to ensure ethical operation around polar bears, regulatory requirements to consider, and recommend that future research focus on understanding polar bears’ behavioural and physiological responses to drones and the efficacy of drones as a deterrent tool for safety purposes.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43781866","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}
Madison L. Harasyn, W. Chan, Emma L. Ausen, D. Barber
Aerial imagery surveys are commonly used in marine mammal research to determine population size, distribution and habitat use. Analysis of aerial photos involves hours of manually identifying individuals present in each image and converting raw counts into useable biological statistics. Our research proposes the use of deep learning algorithms to increase the efficiency of the marine mammal research workflow. To test the feasibility of this proposal, the existing YOLOv4 convolutional neural network model was trained to detect belugas, kayaks and motorized boats in oblique drone imagery, collected from a stationary tethered system. Automated computer-based object detection achieved the following precision and recall, respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and kayak = 96%/96%. We then tested the performance of computer vision tracking of belugas and manned watercraft in drone videos using the DeepSORT tracking algorithm, which achieved a multiple-object tracking accuracy (MOTA) ranging from 37% – 88% and multiple object tracking precision (MOTP) between 63% – 86%. Results from this research indicate that deep learning technology can detect and track features more consistently than human annotators, allowing for larger datasets to be processed within a fraction of the time while avoiding discrepancies introduced by labeling fatigue or multiple human annotators.
{"title":"Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning","authors":"Madison L. Harasyn, W. Chan, Emma L. Ausen, D. Barber","doi":"10.1139/juvs-2021-0024","DOIUrl":"https://doi.org/10.1139/juvs-2021-0024","url":null,"abstract":"Aerial imagery surveys are commonly used in marine mammal research to determine population size, distribution and habitat use. Analysis of aerial photos involves hours of manually identifying individuals present in each image and converting raw counts into useable biological statistics. Our research proposes the use of deep learning algorithms to increase the efficiency of the marine mammal research workflow. To test the feasibility of this proposal, the existing YOLOv4 convolutional neural network model was trained to detect belugas, kayaks and motorized boats in oblique drone imagery, collected from a stationary tethered system. Automated computer-based object detection achieved the following precision and recall, respectively, for each class: beluga = 74%/72%; boat = 97%/99%; and kayak = 96%/96%. We then tested the performance of computer vision tracking of belugas and manned watercraft in drone videos using the DeepSORT tracking algorithm, which achieved a multiple-object tracking accuracy (MOTA) ranging from 37% – 88% and multiple object tracking precision (MOTP) between 63% – 86%. Results from this research indicate that deep learning technology can detect and track features more consistently than human annotators, allowing for larger datasets to be processed within a fraction of the time while avoiding discrepancies introduced by labeling fatigue or multiple human annotators.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46296801","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}
Interest in advanced air mobility (AAM) and urban air mobility (UAM) operations for on-demand passenger and cargo transport continues to grow. There is ongoing research on market demand and forecast, community acceptance, privacy, and security. There is also ongoing research by NASA, FAA, academia, and industry on airspace integration, regulatory, process, and procedural challenges. Safe integration of UAM and AAM will also require different stakeholder perspectives such as air traffic controllers, manned aircraft pilots, remote pilots, UAM operators, and the community. This research aimed to assess the willingness of manned aircraft pilots to operate in UAM integrated airspace based on airspace complexity and UAM automation level. In addition, a moderated mediation analysis was conducted using trust and perceived risk as mediators and operator type as a moderating variable. The results indicated that automation level influenced pilots’ willingness to operate an aircraft in integrated airspace. A moderating effect of operation type on automation level and willingness to pilot an aircraft was also observed: professional pilots were more amenable to UAM operations with a pilot on-board compared to remotely piloted operations. Results from the study are expected to inform airspace integration challenges, processes, and procedures for UAM integrated operations.
{"title":"Pilots’ Willingness to Operate in Urban Air Mobility Integrated Airspace: A Moderated Mediation Analysis","authors":"L. Vempati, Sabrina Woods, S. Winter","doi":"10.1139/juvs-2021-0009","DOIUrl":"https://doi.org/10.1139/juvs-2021-0009","url":null,"abstract":"Interest in advanced air mobility (AAM) and urban air mobility (UAM) operations for on-demand passenger and cargo transport continues to grow. There is ongoing research on market demand and forecast, community acceptance, privacy, and security. There is also ongoing research by NASA, FAA, academia, and industry on airspace integration, regulatory, process, and procedural challenges. Safe integration of UAM and AAM will also require different stakeholder perspectives such as air traffic controllers, manned aircraft pilots, remote pilots, UAM operators, and the community. This research aimed to assess the willingness of manned aircraft pilots to operate in UAM integrated airspace based on airspace complexity and UAM automation level. In addition, a moderated mediation analysis was conducted using trust and perceived risk as mediators and operator type as a moderating variable. The results indicated that automation level influenced pilots’ willingness to operate an aircraft in integrated airspace. A moderating effect of operation type on automation level and willingness to pilot an aircraft was also observed: professional pilots were more amenable to UAM operations with a pilot on-board compared to remotely piloted operations. Results from the study are expected to inform airspace integration challenges, processes, and procedures for UAM integrated operations.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49151152","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}
This study seeks to contribute to the literature by presenting a discussion of potential cyber risks and precautionary measures concerning unmanned vehicles as a whole. In this study, Global Navigation Satellite System (GNSS) spoofing, jamming, password cracking, Denial-of-Service (DoS), injecting malware, and modification of firmware are identified as potential cyberattack methods against unmanned vehicles. Potential deterrents against the aforementioned cyberattack methods are suggested as well. Illustrations of such safeguards include creating an architecture of the multi-agent system, using solid-state storage components, applying distributed programming tools and techniques, implementing sophisticated encryption techniques for data storage and transmission, deploying additional sensors and systems, and comparing the data received from different sensors.
{"title":"Potential Cyber Threats, Vulnerabilities, and Protections of Unmanned Vehicles","authors":"A. Oruc","doi":"10.1139/juvs-2021-0022","DOIUrl":"https://doi.org/10.1139/juvs-2021-0022","url":null,"abstract":"This study seeks to contribute to the literature by presenting a discussion of potential cyber risks and precautionary measures concerning unmanned vehicles as a whole. In this study, Global Navigation Satellite System (GNSS) spoofing, jamming, password cracking, Denial-of-Service (DoS), injecting malware, and modification of firmware are identified as potential cyberattack methods against unmanned vehicles. Potential deterrents against the aforementioned cyberattack methods are suggested as well. Illustrations of such safeguards include creating an architecture of the multi-agent system, using solid-state storage components, applying distributed programming tools and techniques, implementing sophisticated encryption techniques for data storage and transmission, deploying additional sensors and systems, and comparing the data received from different sensors.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47367027","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}
A quadcopter manipulator system is an aerial robot consisting of a quadcopter with a robotic arm attached to it. The system has coupled non-linear dynamics with uncertain time-varying parameters. The work in this paper focuses on designing an adaptive non-linear controller to facilitate the uncertain system’s trajectory tracking and stability. The novelty of the proposed work is the design and implementation of an adaptive feedback linearization controller, called adaptive augmented torque (AAT) control, for the aerial robot. The control law is based on a feedback linearization controller with model reference adaptive controller and a tracking error-based augmented term. Using the input-to-state (ISS) stability concept, a bound on the parameter estimation error is also developed. In the presented methodology, the controller uses estimated values of system parameters obtained from the adaptive mechanism and the tracking error to compute the control input using the AAT control law. An adaptive law for estimating unknown parameters is obtained using the strictly positive real-Lyapunov method. The asymptotic stability of the closed-loop system is analyzed via the Lyapunov theory. Simulations implemented in MATLAB and ROS/Gazebo and preliminary hardware experiments are presented to validate the theoretical results and to corroborate the performance of the AAT control law.
{"title":"Adaptive Augmented Torque Control of a Quadcopter with an Aerial Manipulator","authors":"V. Sumathy, D. Ghose","doi":"10.1139/juvs-2021-0014","DOIUrl":"https://doi.org/10.1139/juvs-2021-0014","url":null,"abstract":"A quadcopter manipulator system is an aerial robot consisting of a quadcopter with a robotic arm attached to it. The system has coupled non-linear dynamics with uncertain time-varying parameters. The work in this paper focuses on designing an adaptive non-linear controller to facilitate the uncertain system’s trajectory tracking and stability. The novelty of the proposed work is the design and implementation of an adaptive feedback linearization controller, called adaptive augmented torque (AAT) control, for the aerial robot. The control law is based on a feedback linearization controller with model reference adaptive controller and a tracking error-based augmented term. Using the input-to-state (ISS) stability concept, a bound on the parameter estimation error is also developed. In the presented methodology, the controller uses estimated values of system parameters obtained from the adaptive mechanism and the tracking error to compute the control input using the AAT control law. An adaptive law for estimating unknown parameters is obtained using the strictly positive real-Lyapunov method. The asymptotic stability of the closed-loop system is analyzed via the Lyapunov theory. Simulations implemented in MATLAB and ROS/Gazebo and preliminary hardware experiments are presented to validate the theoretical results and to corroborate the performance of the AAT control law.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42779133","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}
Effective management of animal populations requires knowledge of life history parameters and estimates of population abundance. One method commonly used to estimate abundance is capture/recapture analyses of photographs. Small, relatively inexpensive, rotary-wing drones have become an effective platform for obtaining high-quality aerial photographs of whales. To conduct capture/recapture analyses the animal needs to be defined as marked or unmarked and the photographs must be of high quality. While a system for scoring quality and markedness has previously been developed for bowhead whales (Balaena mysticetus) (Rugh et al. 1998), a revised scoring system was needed to incorporate increased information in photographs taken by drones. We present a revised scoring system that enlarges two of the previously defined areas of the whale examined for markings and incorporates smaller markings into the definition of marked whales. We scored 30 whales using the previous criteria and the revised criteria developed in this paper. More whales were identified as marked (23%) and mark scores were higher for 30% of the zones scored using the new system. Increasing the number of marked whales during capture/recapture studies increases the precision of estimated parameters and permits us to make those estimates with smaller samples of photographs.
动物种群的有效管理需要了解生活史参数和种群丰度的估计。一种常用的估算丰度的方法是对照片进行捕捉/再捕捉分析。小型、相对便宜的旋翼无人机已经成为获取高质量鲸鱼航空照片的有效平台。为了进行捕获/重新捕获分析，需要将动物定义为标记或未标记，并且照片必须高质量。虽然以前已经为弓头鲸(Balaena mysticetus)开发了评分质量和标记系统(Rugh et al. 1998)，但需要修改评分系统，以纳入无人机拍摄的照片中的更多信息。我们提出了一个修订的评分系统，扩大了两个先前定义的鲸鱼检查标记的区域，并将较小的标记纳入标记鲸鱼的定义。我们使用之前的标准和本文中开发的修订标准对30名鲸鱼玩家进行了评分。更多鲸鱼被识别为标记(23%)，并且使用新系统评分的区域中有30%的标记分数更高。在捕获/再捕获研究中增加标记鲸鱼的数量可以提高估计参数的精度，并允许我们使用更小的照片样本进行估计。
{"title":"A New Scoring System for use in Capture-Recapture Studies for Bowhead Whales Photographed with Drones","authors":"W. Koski, B. Young","doi":"10.1139/juvs-2021-0027","DOIUrl":"https://doi.org/10.1139/juvs-2021-0027","url":null,"abstract":"Effective management of animal populations requires knowledge of life history parameters and estimates of population abundance. One method commonly used to estimate abundance is capture/recapture analyses of photographs. Small, relatively inexpensive, rotary-wing drones have become an effective platform for obtaining high-quality aerial photographs of whales. To conduct capture/recapture analyses the animal needs to be defined as marked or unmarked and the photographs must be of high quality. While a system for scoring quality and markedness has previously been developed for bowhead whales (Balaena mysticetus) (Rugh et al. 1998), a revised scoring system was needed to incorporate increased information in photographs taken by drones. We present a revised scoring system that enlarges two of the previously defined areas of the whale examined for markings and incorporates smaller markings into the definition of marked whales. We scored 30 whales using the previous criteria and the revised criteria developed in this paper. More whales were identified as marked (23%) and mark scores were higher for 30% of the zones scored using the new system. Increasing the number of marked whales during capture/recapture studies increases the precision of estimated parameters and permits us to make those estimates with smaller samples of photographs.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44957812","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}
Susan N. Ellis‐Felege, Tanner J. Stechmann, Samuel D. Hervey, Christopher J. Felege, R. Rockwell, Andrew F. Barnas
Drones may be valuable in polar research because they can minimize researcher activity and overcome logistic, financial, and safety obstacles associated with wildlife research in Polar Regions. Because Polar species may be particularly sensitive to disturbance and some research suggests behavioral responses to drones are species-specific, there is a need for focal species-specific disturbance assessments. We evaluated behavioral responses of nesting Common Eiders (Somateria mollissima, n =19 incubating females) to first, second, or in a few cases third exposure of fixed-wing drone surveys using nest cameras. We found no effect of drone flights (F1,23 = 0, P < 1.0) or previous exposures (F1,23 = 0.75, P = 0.397) on the probability of a daily recess event (bird leaves nests). Drone flights did not impact recess length (F1,25 = 1.34, P = 0.26); however, eiders with prior drone exposure took longer recess events (F1,25 = 5.27, P = 0.03). We did not observe any overhead vigilance behaviors common in other species while the drone was in the air, which may reflect eider’s anti-predator strategies of reducing activity at nests in response to aerial predators. Surveying nesting common eider colonies with a fixed-wing drone did not result in biologically meaningful behavioral changes, providing a potential tool for research and monitoring this Polar nesting species.
无人机在极地研究中可能很有价值，因为它们可以最大限度地减少研究人员的活动，并克服与极地野生动物研究相关的后勤、财务和安全障碍。由于极地物种可能对干扰特别敏感，并且一些研究表明对无人机的行为反应是物种特异性的，因此有必要对焦点物种特异性干扰进行评估。研究人员利用固定翼无人机对巢内摄像机进行了第一次、第二次或少数情况下的第三次暴露，评估了筑巢中的普通绒鸭(Somateria mollissima, n =19只孵化雌性绒鸭)的行为反应。我们发现无人机飞行(F1,23 = 0, P < 1.0)或以前的暴露(F1,23 = 0.75, P = 0.397)对每日休息事件(鸟窝)的概率没有影响。无人机飞行对课间休息长度没有影响(F1,25 = 1.34, P = 0.26);然而，先前暴露于无人机的羽绒鸭需要更长时间的休息事件(F1,25 = 5.27, P = 0.03)。当无人机在空中时，我们没有观察到任何其他物种常见的头顶警戒行为，这可能反映了绒鸭的反捕食者策略，即减少巢穴活动以应对空中捕食者。用固定翼无人机调查筑巢的普通绒鸭群落并没有导致生物学上有意义的行为变化，为研究和监测这种极地筑巢物种提供了一个潜在的工具。
{"title":"Nesting common eiders (Somateria mollissima) show little behavioral response to fixed-wing drone surveys","authors":"Susan N. Ellis‐Felege, Tanner J. Stechmann, Samuel D. Hervey, Christopher J. Felege, R. Rockwell, Andrew F. Barnas","doi":"10.1139/juvs-2021-0012","DOIUrl":"https://doi.org/10.1139/juvs-2021-0012","url":null,"abstract":"Drones may be valuable in polar research because they can minimize researcher activity and overcome logistic, financial, and safety obstacles associated with wildlife research in Polar Regions. Because Polar species may be particularly sensitive to disturbance and some research suggests behavioral responses to drones are species-specific, there is a need for focal species-specific disturbance assessments. We evaluated behavioral responses of nesting Common Eiders (Somateria mollissima, n =19 incubating females) to first, second, or in a few cases third exposure of fixed-wing drone surveys using nest cameras. We found no effect of drone flights (F1,23 = 0, P < 1.0) or previous exposures (F1,23 = 0.75, P = 0.397) on the probability of a daily recess event (bird leaves nests). Drone flights did not impact recess length (F1,25 = 1.34, P = 0.26); however, eiders with prior drone exposure took longer recess events (F1,25 = 5.27, P = 0.03). We did not observe any overhead vigilance behaviors common in other species while the drone was in the air, which may reflect eider’s anti-predator strategies of reducing activity at nests in response to aerial predators. Surveying nesting common eider colonies with a fixed-wing drone did not result in biologically meaningful behavioral changes, providing a potential tool for research and monitoring this Polar nesting species.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45660201","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}
We present a deep neural net-based controller trained by a model-free reinforcement learning (RL) algorithm to achieve hover stabilization for a quadrotor unmanned aerial vehicle (UAV). With RL, two neural nets are trained. One neural net is used as a stochastic controller which gives the distribution of control inputs. The other maps the UAV state to a scalar which estimates the reward of the controller. A proximal policy optimization (PPO) method, which is an actor-critic policy gradient approach, is used to train the neural nets. Simulation results show that the trained controller achieves a comparable level of performance to a manually-tuned PID controller, despite not depending on any model information. The paper considers different choices of reward function and their influence on controller performance.
{"title":"Quadrotor Motion Control Using Deep Reinforcement Learning","authors":"Zifei Jiang, Alan Francis Lynch","doi":"10.1139/juvs-2021-0010","DOIUrl":"https://doi.org/10.1139/juvs-2021-0010","url":null,"abstract":"We present a deep neural net-based controller trained by a model-free reinforcement learning (RL) algorithm to achieve hover stabilization for a quadrotor unmanned aerial vehicle (UAV). With RL, two neural nets are trained. One neural net is used as a stochastic controller which gives the distribution of control inputs. The other maps the UAV state to a scalar which estimates the reward of the controller. A proximal policy optimization (PPO) method, which is an actor-critic policy gradient approach, is used to train the neural nets. Simulation results show that the trained controller achieves a comparable level of performance to a manually-tuned PID controller, despite not depending on any model information. The paper considers different choices of reward function and their influence on controller performance.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47564840","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}
This paper describes a method by which the degree of autonomy of a system can be quantified in a manner that allows comparison between systems. The methodology revisits, refines, and extends the contextual autonomous capability (CAC) model proposed by the National Institute of Science and Technology (NIST) by defining three orthogonal system metrics against which the performance of a system may be assessed. During the development of this model, it was recognized that there existed two different but coupled domains of autonomy — the Executive Autonomy describing the degree of independence of a system during the execution of the mission; and the Developmental Autonomy describing the degree of independence of the system during preparation for the mission. The resulting methodology is explicitly developed to be system agnostic such that it could be applied to humans as well as computerized systems. As such, it provides a means of quantifiably comparing the performance of any two systems — including human and computer — that are performing comparable sets of missions. The proposed model is called the system-agnostic quantification of autonomy levels (SQuAL) model.
{"title":"Quantifying Turing: a systems approach to quantitatively assessing the degree of autonomy of any system","authors":"Mike Meakin","doi":"10.1139/juvs-2021-0001","DOIUrl":"https://doi.org/10.1139/juvs-2021-0001","url":null,"abstract":"This paper describes a method by which the degree of autonomy of a system can be quantified in a manner that allows comparison between systems. The methodology revisits, refines, and extends the contextual autonomous capability (CAC) model proposed by the National Institute of Science and Technology (NIST) by defining three orthogonal system metrics against which the performance of a system may be assessed. During the development of this model, it was recognized that there existed two different but coupled domains of autonomy — the Executive Autonomy describing the degree of independence of a system during the execution of the mission; and the Developmental Autonomy describing the degree of independence of the system during preparation for the mission. The resulting methodology is explicitly developed to be system agnostic such that it could be applied to humans as well as computerized systems. As such, it provides a means of quantifiably comparing the performance of any two systems — including human and computer — that are performing comparable sets of missions. The proposed model is called the system-agnostic quantification of autonomy levels (SQuAL) model.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45333380","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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