Pub Date : 2019-07-23DOI: 10.1142/S2301385019410024
Jung-Hwan Kim, Joonhong Lim
Most current vehicle black boxes provide a bird’s-eye view of 2D type. This viewpoint has a limited viewing angle which makes it difficult to conduct an accident investigation, parking and awareness of space. To solve these problems, we propose the 3D Around View Monitoring (AVM) algorithm using image composition which combines planar and hemispherical projection method. The experimental results indicate that the proposed method overcome the 2D AVM disadvantages and may be useful for accurate accident investigation, autonomous driving system and other system.
{"title":"Four-Channel 3D Around View Monitoring Algorithm Using Hemispherical Projection Model","authors":"Jung-Hwan Kim, Joonhong Lim","doi":"10.1142/S2301385019410024","DOIUrl":"https://doi.org/10.1142/S2301385019410024","url":null,"abstract":"Most current vehicle black boxes provide a bird’s-eye view of 2D type. This viewpoint has a limited viewing angle which makes it difficult to conduct an accident investigation, parking and awareness of space. To solve these problems, we propose the 3D Around View Monitoring (AVM) algorithm using image composition which combines planar and hemispherical projection method. The experimental results indicate that the proposed method overcome the 2D AVM disadvantages and may be useful for accurate accident investigation, autonomous driving system and other system.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117248295","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 : 2019-07-23DOI: 10.1142/S2301385019410048
Yoan Espada, N. Cuperlier, Guillaume Bresson, Olivier Romain
The navigation of autonomous vehicles is confronted to the problem of an efficient place recognition system which is able to handle outdoor environments on the long run. The current Simultaneous Localization and Mapping (SLAM) and place recognition solutions have limitations that prevent them from achieving the performances needed for autonomous driving. This paper suggests handling the problem from another perspective by taking inspiration from biological models. We propose a neural architecture for the localization of an autonomous vehicle based on a neurorobotic model of the place cells (PC) found in the hippocampus of mammals. This model is based on an attentional mechanism and only takes into account visual information from a mono-camera and the orientation information to self-localize. It has the advantage to work with low resolution camera without the need of calibration. It also does not need a long learning phase as it uses a one-shot learning system. Such a localization model has already been integrated in a robot control architecture which allows for successful navigation both in indoor and small outdoor environments. The contribution of this paper is to study how it passes the scale change by evaluating the performance of this model over much larger outdoor environments. Eight experiments using real data (image and orientation) grabbed by a moving vehicle are studied (coming from the KITTI odometry datasets and datasets taken with VEDECOM vehicles). Results show the strong adaptability to different kinds of environments of this bio-inspired model primarily developed for indoor navigation.
{"title":"From Neurorobotic Localization to Autonomous Vehicles","authors":"Yoan Espada, N. Cuperlier, Guillaume Bresson, Olivier Romain","doi":"10.1142/S2301385019410048","DOIUrl":"https://doi.org/10.1142/S2301385019410048","url":null,"abstract":"The navigation of autonomous vehicles is confronted to the problem of an efficient place recognition system which is able to handle outdoor environments on the long run. The current Simultaneous Localization and Mapping (SLAM) and place recognition solutions have limitations that prevent them from achieving the performances needed for autonomous driving. This paper suggests handling the problem from another perspective by taking inspiration from biological models. We propose a neural architecture for the localization of an autonomous vehicle based on a neurorobotic model of the place cells (PC) found in the hippocampus of mammals. This model is based on an attentional mechanism and only takes into account visual information from a mono-camera and the orientation information to self-localize. It has the advantage to work with low resolution camera without the need of calibration. It also does not need a long learning phase as it uses a one-shot learning system. Such a localization model has already been integrated in a robot control architecture which allows for successful navigation both in indoor and small outdoor environments. The contribution of this paper is to study how it passes the scale change by evaluating the performance of this model over much larger outdoor environments. Eight experiments using real data (image and orientation) grabbed by a moving vehicle are studied (coming from the KITTI odometry datasets and datasets taken with VEDECOM vehicles). Results show the strong adaptability to different kinds of environments of this bio-inspired model primarily developed for indoor navigation.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129726070","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 : 2019-07-23DOI: 10.1142/S2301385019410012
Michelle Valente, C. Joly, A. D. L. Fortelle
This work introduces a new complete Simultaneous Localization and Mapping (SLAM) framework that uses an enriched representation of the world based on sensor fusion and is able to simultaneously provide an accurate localization of the vehicle. A method to create an Evidential grid representation from two very different sensors, laser scanner and stereo camera, allows a better handling of the dynamic aspects of the urban environment and a proper management of errors to create a more reliable map, thus having a more precise localization. A life-long layer with high level states is presented, it maintains a global map of the entire vehicle’s trajectory and distinguishes between static and dynamic obstacles. Finally, we propose a method that at each current map creation estimates the vehicle’s position by a grid matching algorithm based on image registration techniques. Results on a real road dataset show that the environment mapping data can be improved by adding relevant information that could be missed without the proposed approach. Moreover, the proposed localization method is able to reduce the drift and improve the localization compared to other methods using similar configurations.
{"title":"Evidential SLAM Fusing 2D Laser Scanner and Stereo Camera","authors":"Michelle Valente, C. Joly, A. D. L. Fortelle","doi":"10.1142/S2301385019410012","DOIUrl":"https://doi.org/10.1142/S2301385019410012","url":null,"abstract":"This work introduces a new complete Simultaneous Localization and Mapping (SLAM) framework that uses an enriched representation of the world based on sensor fusion and is able to simultaneously provide an accurate localization of the vehicle. A method to create an Evidential grid representation from two very different sensors, laser scanner and stereo camera, allows a better handling of the dynamic aspects of the urban environment and a proper management of errors to create a more reliable map, thus having a more precise localization. A life-long layer with high level states is presented, it maintains a global map of the entire vehicle’s trajectory and distinguishes between static and dynamic obstacles. Finally, we propose a method that at each current map creation estimates the vehicle’s position by a grid matching algorithm based on image registration techniques. Results on a real road dataset show that the environment mapping data can be improved by adding relevant information that could be missed without the proposed approach. Moreover, the proposed localization method is able to reduce the drift and improve the localization compared to other methods using similar configurations.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115096352","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 : 2019-07-23DOI: 10.1142/S2301385019020023
Han Wang
{"title":"Editorial: Special Issue on Artificial Intelligence and Computer Vision","authors":"Han Wang","doi":"10.1142/S2301385019020023","DOIUrl":"https://doi.org/10.1142/S2301385019020023","url":null,"abstract":"","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130524257","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 : 2019-07-01DOI: 10.1142/S2301385019410036
Ö. Erkent, Christian Wolf, C. Laugier
We propose semantic grid, a spatial 2D map of the environment around an autonomous vehicle consisting of cells which represent the semantic information of the corresponding region such as car, road, vegetation, bikes, etc. It consists of an integration of an occupancy grid, which computes the grid states with a Bayesian filter approach, and semantic segmentation information from monocular RGB images, which is obtained with a deep neural network. The network fuses the information and can be trained in an end-to-end manner. The output of the neural network is refined with a conditional random field. The proposed method is tested in various datasets (KITTI dataset, Inria-Chroma dataset and SYNTHIA) and different deep neural network architectures are compared.
{"title":"End-to-End Learning of Semantic Grid Estimation Deep Neural Network with Occupancy Grids","authors":"Ö. Erkent, Christian Wolf, C. Laugier","doi":"10.1142/S2301385019410036","DOIUrl":"https://doi.org/10.1142/S2301385019410036","url":null,"abstract":"We propose semantic grid, a spatial 2D map of the environment around an autonomous vehicle consisting of cells which represent the semantic information of the corresponding region such as car, road, vegetation, bikes, etc. It consists of an integration of an occupancy grid, which computes the grid states with a Bayesian filter approach, and semantic segmentation information from monocular RGB images, which is obtained with a deep neural network. The network fuses the information and can be trained in an end-to-end manner. The output of the neural network is refined with a conditional random field. The proposed method is tested in various datasets (KITTI dataset, Inria-Chroma dataset and SYNTHIA) and different deep neural network architectures are compared.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133507752","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 : 2019-06-19DOI: 10.1142/S2301385019500018
A. T. Hafez, M. A. Kamel
This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.
{"title":"Cooperative Task Assignment and Trajectory Planning of Unmanned Systems Via HFLC and PSO","authors":"A. T. Hafez, M. A. Kamel","doi":"10.1142/S2301385019500018","DOIUrl":"https://doi.org/10.1142/S2301385019500018","url":null,"abstract":"This paper investigates the problems of cooperative task assignment and trajectory planning for teams of cooperative unmanned aerial vehicles (UAVs). A novel approach of hierarchical fuzzy logic controller (HFLC) and particle swarm optimization (PSO) is proposed. Initially, teams of UAVs are moving in a pre-defined formation covering a specified area. When one or more targets are detected, the teams send a package of information to the ground station (GS) including the target’s degree of threat, degree of importance, and the separating distance between each team and each detected target. Based on the gathered information, the ground station assigns the teams to the targets. HFLC is implemented in the GS to solve the assignment problem ensuring that each team is assigned to a unique target. Next, each team plans its own path by formulating the path planning problem as an optimization problem. The objective in this case is to minimize the time to reach their destination considering the UAVs dynamic constraints and collision avoidance between teams. A hybrid approach of control parametrization and time discretization (CPTD) and PSO is proposed to solve this optimization problem. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithm.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115608336","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 : 2019-06-19DOI: 10.1142/S2301385019500055
Farbod Khoshnoud, I. Esat, C. D. Silva, M. Quadrelli
A quantum network may be realized by the entanglement of particles communicated by qubits between quantum computers, where the entangled photons of light are transferred for communication purposes. This technology has been proven to be feasible experimentally through free-space distribution of entangled photon pairs. Sending photons of light through nonlinear crystals produces correlated photon pairs, by splitting each photon into two half particles with each particle having the same level of energy, which results in entangled pairs. This entanglement is represented by photons, having both either horizontal or vertical polarization. This paper investigates collaborative robotic tasks of unmanned systems in a network where the agents are entangled. For instance, a leader robot sends two identical photons (e.g. with vertical polarization) to two follower robots/autonomous vehicles to communicate information about various tasks such as swarm, formation, trajectory tracking, path following and collaborative tasks. The potential advantages of quantum cooperation of robotic agents is the speed of the process, the ability to achieve security with immunity against cyberattacks, and fault tolerance, through entanglement. If a Quantum Network is implemented in a robotic application, it would present an effective solution; for example, for a group of unmanned systems working securely together. An analytical basis of such systems is investigated in this paper, and the formulation of quantum cooperation of unmanned systems is presented and discussed. The concept of experimental quantum entanglement, as well as quantum cryptography (QC), for robotics applications is presented.
{"title":"Quantum Network of Cooperative Unmanned Autonomous Systems","authors":"Farbod Khoshnoud, I. Esat, C. D. Silva, M. Quadrelli","doi":"10.1142/S2301385019500055","DOIUrl":"https://doi.org/10.1142/S2301385019500055","url":null,"abstract":"A quantum network may be realized by the entanglement of particles communicated by qubits between quantum computers, where the entangled photons of light are transferred for communication purposes. This technology has been proven to be feasible experimentally through free-space distribution of entangled photon pairs. Sending photons of light through nonlinear crystals produces correlated photon pairs, by splitting each photon into two half particles with each particle having the same level of energy, which results in entangled pairs. This entanglement is represented by photons, having both either horizontal or vertical polarization. This paper investigates collaborative robotic tasks of unmanned systems in a network where the agents are entangled. For instance, a leader robot sends two identical photons (e.g. with vertical polarization) to two follower robots/autonomous vehicles to communicate information about various tasks such as swarm, formation, trajectory tracking, path following and collaborative tasks. The potential advantages of quantum cooperation of robotic agents is the speed of the process, the ability to achieve security with immunity against cyberattacks, and fault tolerance, through entanglement. If a Quantum Network is implemented in a robotic application, it would present an effective solution; for example, for a group of unmanned systems working securely together. An analytical basis of such systems is investigated in this paper, and the formulation of quantum cooperation of unmanned systems is presented and discussed. The concept of experimental quantum entanglement, as well as quantum cryptography (QC), for robotics applications is presented.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131471647","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 : 2019-06-19DOI: 10.1142/S2301385019500031
Michael D. Zollars, R. Cobb, David J. Grymin
Small Unmanned Aircraft Systems have grown in autonomy and capability and continue to complement Department of Defense mission objectives. Teaming unmanned aircraft with manned vehicles can expand mission profiles and reduce risk to human life. To fully leverage unmanned systems, vehicles must be efficient and autonomous in path planning development. The work herein explores direct orthogonal collocation optimal control techniques combined with fast geometric path planning algorithms to reduce computation time and increase solution accuracy for small unmanned aircraft systems path planning missions. Previous work in the two-dimensional plane demonstrated a methodology to provide optimal flight paths through defined simplex corridors and simplified the optimal control parameter bounds by formulating the problem in the barycentric coordinate system. These methodologies are extended in this paper for three-dimensional flight and are solved with two different formulations for flight in an urban environment. The first formulation solves the constrained optimal control problem using a single phase while modeling the building constraints with superquadric functions. The second formulation implements the simplex methodology, eliminating polygonal constraints from the search domain, and solving the optimal path in a multiple phase approach. Results illustrate the benefits gained in computation time and accuracy when implementing simplex methods into the optimal control design and provide a foundation for closing the gap to real-time, onboard operations for unmanned vehicle path planning.
{"title":"Optimal SUAS Path Planning in Three-Dimensional Constrained Environments","authors":"Michael D. Zollars, R. Cobb, David J. Grymin","doi":"10.1142/S2301385019500031","DOIUrl":"https://doi.org/10.1142/S2301385019500031","url":null,"abstract":"Small Unmanned Aircraft Systems have grown in autonomy and capability and continue to complement Department of Defense mission objectives. Teaming unmanned aircraft with manned vehicles can expand mission profiles and reduce risk to human life. To fully leverage unmanned systems, vehicles must be efficient and autonomous in path planning development. The work herein explores direct orthogonal collocation optimal control techniques combined with fast geometric path planning algorithms to reduce computation time and increase solution accuracy for small unmanned aircraft systems path planning missions. Previous work in the two-dimensional plane demonstrated a methodology to provide optimal flight paths through defined simplex corridors and simplified the optimal control parameter bounds by formulating the problem in the barycentric coordinate system. These methodologies are extended in this paper for three-dimensional flight and are solved with two different formulations for flight in an urban environment. The first formulation solves the constrained optimal control problem using a single phase while modeling the building constraints with superquadric functions. The second formulation implements the simplex methodology, eliminating polygonal constraints from the search domain, and solving the optimal path in a multiple phase approach. Results illustrate the benefits gained in computation time and accuracy when implementing simplex methods into the optimal control design and provide a foundation for closing the gap to real-time, onboard operations for unmanned vehicle path planning.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129478936","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 : 2019-06-19DOI: 10.1142/S230138501950002X
Kapil Sachan, R. Padhi
A waypoint constrained multi-phase nonlinear optimal guidance scheme is presented in this paper for the soft landing of a spacecraft on the Lunar surface by using the recently developed computationally efficient Generalized Model Predictive Static Programming (G-MPSP). The proposed guidance ensures that the spacecraft passes through two waypoints, which is a strong requirement to facilitate proper landing site detection by the on-board camera for mission safety. Constraints that are required at the waypoints as well as at the terminal point include position, velocity, and attitude of the spacecraft. In addition to successfully meeting these hard constraints, the G-MPSP guidance also minimizes the fuel consumption, which is a very good advantage. An optimal final time selection procedure is also presented in this paper to facilitate minimization of fuel requirement to the best extent possible. Extensive simulation studies have been carried out with various perturbations to illustrate the effectiveness of the algorithm. Finally, processor-in-loop simulation has been carried out, which demonstrates the feasibility of on-board implementation of the proposed guidance.
{"title":"Waypoint Constrained Multi-Phase Optimal Guidance of Spacecraft for Soft Lunar Landing","authors":"Kapil Sachan, R. Padhi","doi":"10.1142/S230138501950002X","DOIUrl":"https://doi.org/10.1142/S230138501950002X","url":null,"abstract":"A waypoint constrained multi-phase nonlinear optimal guidance scheme is presented in this paper for the soft landing of a spacecraft on the Lunar surface by using the recently developed computationally efficient Generalized Model Predictive Static Programming (G-MPSP). The proposed guidance ensures that the spacecraft passes through two waypoints, which is a strong requirement to facilitate proper landing site detection by the on-board camera for mission safety. Constraints that are required at the waypoints as well as at the terminal point include position, velocity, and attitude of the spacecraft. In addition to successfully meeting these hard constraints, the G-MPSP guidance also minimizes the fuel consumption, which is a very good advantage. An optimal final time selection procedure is also presented in this paper to facilitate minimization of fuel requirement to the best extent possible. Extensive simulation studies have been carried out with various perturbations to illustrate the effectiveness of the algorithm. Finally, processor-in-loop simulation has been carried out, which demonstrates the feasibility of on-board implementation of the proposed guidance.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132825631","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}
Time-varying formation analysis and design problems for general linear multi-agent systems with switching interaction topologies and time-varying delays are studied. Firstly, a consensus-based formation control protocol is constructed using local information of the neighboring agents. An algorithm with three steps is presented to design the proposed formation control protocol. Then, based on linear matrix inequality technique and common Lyapunove–Krasovskii stability theory, sufficient conditions for general linear multi-agent systems with switching topologies and time-varying delays to achieve time-varying formation are given together with a time-varying formation feasibility condition. Finally, a numerical simulation is given to demonstrate the effectiveness of the obtained theoretical results.
{"title":"Time-Varying Formation Control for Time-Delayed Multi-Agent Systems with General Linear Dynamics and Switching Topologies","authors":"Wei Xiao, Jianglong Yu, Rui Wang, Xiwang Dong, Qingdong Li, Z. Ren","doi":"10.1142/S2301385019400016","DOIUrl":"https://doi.org/10.1142/S2301385019400016","url":null,"abstract":"Time-varying formation analysis and design problems for general linear multi-agent systems with switching interaction topologies and time-varying delays are studied. Firstly, a consensus-based formation control protocol is constructed using local information of the neighboring agents. An algorithm with three steps is presented to design the proposed formation control protocol. Then, based on linear matrix inequality technique and common Lyapunove–Krasovskii stability theory, sufficient conditions for general linear multi-agent systems with switching topologies and time-varying delays to achieve time-varying formation are given together with a time-varying formation feasibility condition. Finally, a numerical simulation is given to demonstrate the effectiveness of the obtained theoretical results.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127904223","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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