Pub Date : 2021-11-19DOI: 10.1142/s2301385022500170
Abdelwahhab Bouras, Y. Bouzid, M. Guiatni
{"title":"Multi-UAV Coverage Path Planning for Gas Distribution Map Applications","authors":"Abdelwahhab Bouras, Y. Bouzid, M. Guiatni","doi":"10.1142/s2301385022500170","DOIUrl":"https://doi.org/10.1142/s2301385022500170","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134049702","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 : 2021-10-27DOI: 10.1142/s2301385021500199
Ruohan Yang, Lu Liu, Gang Feng
{"title":"An Overview of Recent Advances in Distributed Coordination of Multi-Agent Systems","authors":"Ruohan Yang, Lu Liu, Gang Feng","doi":"10.1142/s2301385021500199","DOIUrl":"https://doi.org/10.1142/s2301385021500199","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114225076","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 : 2021-09-17DOI: 10.1142/s230138502250011x
Sofie Ahlberg, Agnes Axelsson, Pian Yu, Wenceslao Shaw Cortez, Yuan Gao, Ali Ghadirzadeh, Ginevra Castellano, D. Kragic, Gabriel Skantze, Dimos V. Dimarogonas
The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human–robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human–robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work.
{"title":"Co-adaptive Human-Robot Cooperation: Summary and Challenges","authors":"Sofie Ahlberg, Agnes Axelsson, Pian Yu, Wenceslao Shaw Cortez, Yuan Gao, Ali Ghadirzadeh, Ginevra Castellano, D. Kragic, Gabriel Skantze, Dimos V. Dimarogonas","doi":"10.1142/s230138502250011x","DOIUrl":"https://doi.org/10.1142/s230138502250011x","url":null,"abstract":"The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human–robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human–robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128143741","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 : 2021-09-16DOI: 10.1142/s2301385022500145
Chenyuan He, Yan Wan, Junfei Xie
{"title":"Automated Playbook for UAV Traffic Management Based on Spatiotemporal Scenario Data","authors":"Chenyuan He, Yan Wan, Junfei Xie","doi":"10.1142/s2301385022500145","DOIUrl":"https://doi.org/10.1142/s2301385022500145","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526592","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 : 2021-09-16DOI: 10.1142/s2301385022500133
M. S. A. Isaac, A. Ragab, Enrique Caballero Garcés, M. A. Luna, P. F. Peña, P. Cervera
{"title":"Mathematical Modeling and Designing a Heavy Hybrid-Electric Quadcopter, Controlled by Flaps","authors":"M. S. A. Isaac, A. Ragab, Enrique Caballero Garcés, M. A. Luna, P. F. Peña, P. Cervera","doi":"10.1142/s2301385022500133","DOIUrl":"https://doi.org/10.1142/s2301385022500133","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134379539","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 : 2021-09-16DOI: 10.1142/s2301385022500066
Ahmed Allam, A. Nemra, M. Tadjine
Flexible and robust Time-Varying Formation (TVF) tracking of Unmanned Ground Vehicles (UGVs) guided by an Unmanned Aerial Vehicle (UAV) is considered in this paper. The UAV–UGVs system control model is based on leader-follower approach, where the control scheme consists of two consecutive tasks, namely, deployment task and TVF tracking. Accordingly, two novel nonlinear controllers are proposed for controlling the UGVs formation. First, unlike the classical frameworks on UGVs formation tracking, for which only particular shapes are handled (e.g. circle, square, ellipse), we propose a UGVs deployment-controller ensuring to reach free-formation shapes. The key feature is in using the estimated implicit representation of the desired formation shape as a potential function to generate the UGVs reference trajectory. Second, in the TVF tracking task, a robust cascaded velocity/torque controller for UGVs is proposed based on kinematic and dynamic models. Differently from the classical backstepping framework, the key idea is in introducing an auxiliary control input, in such a way that the overall UGV dynamics is converted into a simpler and modular control structure. As such, the auxiliary input is used to control indirectly the actual UGVs velocity vector. A signum term is added to the torque-input to compensate for the unknown external disturbances and unmodeled dynamics. Numerical simulation shows the effectiveness of the proposed formation controllers compared with the case when the perfect velocity-tracking assumption holds. Experimental results are further provided using three festos Robtino robots to show the validity of the proposed TVF tracking velocity-control scheme.
{"title":"Parametric and Implicit Features-Based UAV-UGVs Time-Varying Formation Tracking: Dynamic Approach","authors":"Ahmed Allam, A. Nemra, M. Tadjine","doi":"10.1142/s2301385022500066","DOIUrl":"https://doi.org/10.1142/s2301385022500066","url":null,"abstract":"Flexible and robust Time-Varying Formation (TVF) tracking of Unmanned Ground Vehicles (UGVs) guided by an Unmanned Aerial Vehicle (UAV) is considered in this paper. The UAV–UGVs system control model is based on leader-follower approach, where the control scheme consists of two consecutive tasks, namely, deployment task and TVF tracking. Accordingly, two novel nonlinear controllers are proposed for controlling the UGVs formation. First, unlike the classical frameworks on UGVs formation tracking, for which only particular shapes are handled (e.g. circle, square, ellipse), we propose a UGVs deployment-controller ensuring to reach free-formation shapes. The key feature is in using the estimated implicit representation of the desired formation shape as a potential function to generate the UGVs reference trajectory. Second, in the TVF tracking task, a robust cascaded velocity/torque controller for UGVs is proposed based on kinematic and dynamic models. Differently from the classical backstepping framework, the key idea is in introducing an auxiliary control input, in such a way that the overall UGV dynamics is converted into a simpler and modular control structure. As such, the auxiliary input is used to control indirectly the actual UGVs velocity vector. A signum term is added to the torque-input to compensate for the unknown external disturbances and unmodeled dynamics. Numerical simulation shows the effectiveness of the proposed formation controllers compared with the case when the perfect velocity-tracking assumption holds. Experimental results are further provided using three festos Robtino robots to show the validity of the proposed TVF tracking velocity-control scheme.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130862321","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 : 2021-09-16DOI: 10.1142/s2301385022500078
C. Zammit, E. Kampen
This paper aims to present a comparative analysis of the two most utilized graph-based and sampling-based algorithms and their variants, in view of 3D UAV path planning in complex indoor environment. The findings of this analysis outline the usability of the methods and can assist future UAV path planning designers to select the best algorithm with the best parameter configuration in relation to the specific application. An extensive literature review of graph-based and sampling-based methods and their variants is first presented. The most utilized algorithms which are the A* for graph-based methods and Rapidly-Exploring Random Tree (RRT) for the sampling-based methods, are defined. A set of variants is also developed to mitigate with inherent shortcomings in the standard algorithms. All algorithms are then tested in the same scenarios and analyzed using the same performance measures. The A* algorithm generates shorter paths with respect to the RRT algorithm. The A* algorithm only explores volumes required for path generation while the RRT algorithms explore the space evenly. The A* algorithm exhibits an oscillatory behavior at different resolutions for the same scenario that is attenuated with the novel A* ripple reduction algorithm. The Multiple RRT generated longer unsmoothed paths in shorter planning times but required more smoothing over RRT. This work is the first attempt to compare graph-based and sampling-based algorithms in 3D path planning of UAVs. Furthermore, this work addresses shortcomings in both A* and RRT standard algorithms by developing a novel A* ripple reduction algorithm, a novel RRT variant and a specifically designed smoothing algorithm.
{"title":"Comparison Between A* and RRT Algorithms for 3D UAV Path Planning","authors":"C. Zammit, E. Kampen","doi":"10.1142/s2301385022500078","DOIUrl":"https://doi.org/10.1142/s2301385022500078","url":null,"abstract":"This paper aims to present a comparative analysis of the two most utilized graph-based and sampling-based algorithms and their variants, in view of 3D UAV path planning in complex indoor environment. The findings of this analysis outline the usability of the methods and can assist future UAV path planning designers to select the best algorithm with the best parameter configuration in relation to the specific application. An extensive literature review of graph-based and sampling-based methods and their variants is first presented. The most utilized algorithms which are the A* for graph-based methods and Rapidly-Exploring Random Tree (RRT) for the sampling-based methods, are defined. A set of variants is also developed to mitigate with inherent shortcomings in the standard algorithms. All algorithms are then tested in the same scenarios and analyzed using the same performance measures. The A* algorithm generates shorter paths with respect to the RRT algorithm. The A* algorithm only explores volumes required for path generation while the RRT algorithms explore the space evenly. The A* algorithm exhibits an oscillatory behavior at different resolutions for the same scenario that is attenuated with the novel A* ripple reduction algorithm. The Multiple RRT generated longer unsmoothed paths in shorter planning times but required more smoothing over RRT. This work is the first attempt to compare graph-based and sampling-based algorithms in 3D path planning of UAVs. Furthermore, this work addresses shortcomings in both A* and RRT standard algorithms by developing a novel A* ripple reduction algorithm, a novel RRT variant and a specifically designed smoothing algorithm.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116977425","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 : 2021-09-15DOI: 10.1142/s2301385022500108
Álvaro Martínez Novo, Liang Lu, P. Campoy
This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.
{"title":"FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs","authors":"Álvaro Martínez Novo, Liang Lu, P. Campoy","doi":"10.1142/s2301385022500108","DOIUrl":"https://doi.org/10.1142/s2301385022500108","url":null,"abstract":"This paper addresses the challenge to build an autonomous exploration system using Micro-Aerial Vehicles (MAVs). MAVs are capable of flying autonomously, generating collision-free paths to navigate in unknown areas and also reconstructing the environment at which they are deployed. One of the contributions of our system is the “3D-Sliced Planner” for exploration. The main innovation is the low computational resources needed. This is because Optimal-Frontier-Points (OFP) to explore are computed in 2D slices of the 3D environment using a global Rapidly-exploring Random Tree (RRT) frontier detector. Then, the MAV can plan path routes to these points to explore the surroundings with our new proposed local “FAST RRT* Planner” that uses a tree reconnection algorithm based on cost, and a collision checking algorithm based on Signed Distance Field (SDF). The results show the proposed explorer takes 43.95% less time to compute exploration points and paths when compared with the State-of-the-Art represented by the Receding Horizon Next Best View Planner (RH-NBVP) in Gazebo simulations.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125740018","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 : 2021-09-10DOI: 10.1142/s2301385022500121
S. Pitcher
Unmanned Aircraft System (UAS) growth in the past several years has been rising at a steady pace which has complicated the attempts to safely integrate them into the National Airspace System, as evidenced by an increasing number of UAS sighting reports being submitted to the Federal Aviation Administration. The analysis consisted of a mixed method approach using quantitative analysis of more than 9000 Federal Aviation Administration Unmanned Aircraft System Sighting reports from 2015 through 2019, as well as U.S. Census data, and weather data. The qualitative analysis focused on UAS regulation, and heatmap data of both population density and UAS sighting location density. The findings for the five states with the most and the least sighting reports show that major metropolitan areas, which have high population and population density, higher median household incomes, high percentage of college graduates, and are located in areas that have stable weather and negligible weather effects such as rain and high winds during the summer months, have both high and concentrated levels of UAS sightings.
{"title":"Analysis of Unmanned Aircraft Systems Sightings Reports: Determination of Factors Leading to High Sighting Reports","authors":"S. Pitcher","doi":"10.1142/s2301385022500121","DOIUrl":"https://doi.org/10.1142/s2301385022500121","url":null,"abstract":"Unmanned Aircraft System (UAS) growth in the past several years has been rising at a steady pace which has complicated the attempts to safely integrate them into the National Airspace System, as evidenced by an increasing number of UAS sighting reports being submitted to the Federal Aviation Administration. The analysis consisted of a mixed method approach using quantitative analysis of more than 9000 Federal Aviation Administration Unmanned Aircraft System Sighting reports from 2015 through 2019, as well as U.S. Census data, and weather data. The qualitative analysis focused on UAS regulation, and heatmap data of both population density and UAS sighting location density. The findings for the five states with the most and the least sighting reports show that major metropolitan areas, which have high population and population density, higher median household incomes, high percentage of college graduates, and are located in areas that have stable weather and negligible weather effects such as rain and high winds during the summer months, have both high and concentrated levels of UAS sightings.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121768943","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 : 2021-09-03DOI: 10.1142/s2301385022500091
Jun Jet Tai, S. K. Phang, Felicia Yen Myan Wong
Obstacle avoidance and navigation (OAN) algorithms typically employ offline or online methods. The former is fast but requires knowledge of a global map, while the latter is usually more computationally heavy in explicit solution methods, or is lacking in configurability in the form of artificial intelligence (AI) enabled agents. In order for OAN algorithms to be brought to mass produced robots, more specifically for multirotor unmanned aerial vehicles (UAVs), the computational requirement of these algorithms must be brought low enough such that its computation can be done entirely onboard a companion computer, while being flexible enough to function without a prior map, as is the case of most real life scenarios. In this paper, a highly configurable algorithm, dubbed Closest Obstacle Avoidance and A* (COAA*), that is lightweight enough to run on the companion computer of the UAV is proposed. This algorithm frees up from the conventional drawbacks of offline and online OAN algorithms, while having guaranteed convergence to a global minimum. The algorithms have been successfully implemented on the Heavy Lift Experimental (HLX) UAV of the Autonomous Robots Research Cluster in Taylor’s University, and the simulated results match the real results sufficiently to show that the algorithm has potential for widespread implementation.
{"title":"COAA* - An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments","authors":"Jun Jet Tai, S. K. Phang, Felicia Yen Myan Wong","doi":"10.1142/s2301385022500091","DOIUrl":"https://doi.org/10.1142/s2301385022500091","url":null,"abstract":"Obstacle avoidance and navigation (OAN) algorithms typically employ offline or online methods. The former is fast but requires knowledge of a global map, while the latter is usually more computationally heavy in explicit solution methods, or is lacking in configurability in the form of artificial intelligence (AI) enabled agents. In order for OAN algorithms to be brought to mass produced robots, more specifically for multirotor unmanned aerial vehicles (UAVs), the computational requirement of these algorithms must be brought low enough such that its computation can be done entirely onboard a companion computer, while being flexible enough to function without a prior map, as is the case of most real life scenarios. In this paper, a highly configurable algorithm, dubbed Closest Obstacle Avoidance and A* (COAA*), that is lightweight enough to run on the companion computer of the UAV is proposed. This algorithm frees up from the conventional drawbacks of offline and online OAN algorithms, while having guaranteed convergence to a global minimum. The algorithms have been successfully implemented on the Heavy Lift Experimental (HLX) UAV of the Autonomous Robots Research Cluster in Taylor’s University, and the simulated results match the real results sufficiently to show that the algorithm has potential for widespread implementation.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123450693","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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