Pub Date : 2018-04-11DOI: 10.1109/ICCPS.2018.00038
Shunsuke Aoki, R. Rajkumar
Connected and automated vehicles are expected to be at the core of future intelligent transportation systems. One of the main practical challenges for self-driving vehicles on public roads is safe cooperation and collaboration among multiple vehicles when conflicts arise on shared road segments. Intersections controlled by traffic lights and stop signs are common examples of such potential conflicts, and cooperative protocols for such intersections have been studied. On the other hand, there are many different types of shared road segments. In this paper, we study Dynamic Intersections that might appear almost anytime and anywhere on public roads and that might lead to automobile accidents. We consider how a self-driving vehicle can safely navigate these dynamic intersections by using sensor-based perception and inter-vehicle communications. We present a cooperative protocol for dynamic intersections which can be used by self-driving vehicles for safely coordinating with other vehicles. Under our protocol, self-driving vehicles can also create a vehicular communication-based traffic manager named Cyber Traffic Light when the area is congested. A cyber traffic light functions as a self-optimizing traffic light by estimating the traffic volumes and by wirelessly coordinating among multiple self-driving vehicles. Our simulation results show that our protocol has higher traffic throughput, compared to simple traffic protocols while ensuring safety.
{"title":"Dynamic Intersections and Self-Driving Vehicles","authors":"Shunsuke Aoki, R. Rajkumar","doi":"10.1109/ICCPS.2018.00038","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00038","url":null,"abstract":"Connected and automated vehicles are expected to be at the core of future intelligent transportation systems. One of the main practical challenges for self-driving vehicles on public roads is safe cooperation and collaboration among multiple vehicles when conflicts arise on shared road segments. Intersections controlled by traffic lights and stop signs are common examples of such potential conflicts, and cooperative protocols for such intersections have been studied. On the other hand, there are many different types of shared road segments. In this paper, we study Dynamic Intersections that might appear almost anytime and anywhere on public roads and that might lead to automobile accidents. We consider how a self-driving vehicle can safely navigate these dynamic intersections by using sensor-based perception and inter-vehicle communications. We present a cooperative protocol for dynamic intersections which can be used by self-driving vehicles for safely coordinating with other vehicles. Under our protocol, self-driving vehicles can also create a vehicular communication-based traffic manager named Cyber Traffic Light when the area is congested. A cyber traffic light functions as a self-optimizing traffic light by estimating the traffic volumes and by wirelessly coordinating among multiple self-driving vehicles. Our simulation results show that our protocol has higher traffic throughput, compared to simple traffic protocols while ensuring safety.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114901439","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00027
A. Sanand, A. Dilip, N. Athanasopoulos, R. Jungers
We consider control systems where the input signal is transferred over a network and therefore, it is subject to packet losses. In this setting, the closed-loop behavior can be described as a constrained switching system. We investigate whether there exists a switching signal that prevents reachability of some target state, or alternatively, how much additional input energy is required to reach a target state in comparison to the dropout-free case. Mathematically, we formulate a reachability problem defined on a hybrid automaton and tackle an optimization problem, whose feasibility variants, the controllability and reachability properties, have been recently shown to be decidable. To do so, we provide automata-theoretic algorithms to study the properties of an appropriate generalization of the Controllability Gramian matrix. Additionally, we provide polynomial time heuristics for computations for a specific family of automata and show numerical evidence that they work well in practice. Last, we extend our observations to the analogous observability energy problem.
{"title":"The Impact of Packet Dropouts on the Reachability Energy","authors":"A. Sanand, A. Dilip, N. Athanasopoulos, R. Jungers","doi":"10.1109/ICCPS.2018.00027","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00027","url":null,"abstract":"We consider control systems where the input signal is transferred over a network and therefore, it is subject to packet losses. In this setting, the closed-loop behavior can be described as a constrained switching system. We investigate whether there exists a switching signal that prevents reachability of some target state, or alternatively, how much additional input energy is required to reach a target state in comparison to the dropout-free case. Mathematically, we formulate a reachability problem defined on a hybrid automaton and tackle an optimization problem, whose feasibility variants, the controllability and reachability properties, have been recently shown to be decidable. To do so, we provide automata-theoretic algorithms to study the properties of an appropriate generalization of the Controllability Gramian matrix. Additionally, we provide polynomial time heuristics for computations for a specific family of automata and show numerical evidence that they work well in practice. Last, we extend our observations to the analogous observability energy problem.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130167384","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00033
Ilija Jovanov, Michael Naumann, Karthik Kumaravelu, W. Grill, M. Pajic
Deep Brain Stimulation (DBS) is effective at alleviating symptoms of neurological disorders such as Parkinson's disease. Yet, despite its safety-critical nature, there does not exist a platform for integrated design and testing of new algorithms or devices. Consequently, we introduce a model-based design framework for DBS controllers based on a physiologically relevant basal-ganglia model (BGM) that we capture as a network of nonlinear hybrid automata, synchronized via neural activation events. The BGM is parametrized by the number of neurons used to model each of the BG regions, which supports tradeoffs between fidelity and complexity of the model. Our hybrid-automata representation is exploited for design of software (Simulink) and hardware (FPGA) BGM platforms, with the latter enabling real-time model simulation and device testing. We demonstrate that the BGM platform is capable of generating physiologically relevant responses to DBS, and validate the BGM using a set of requirements obtained from existing work. We present the use of our framework for design and test of DBS controllers with varying levels of adaptation/feedback. Our evaluations are based on Quality-of-Control metrics that we introduce for runtime monitoring of DBS effectiveness.
{"title":"Platform for Model-Based Design and Testing for Deep Brain Stimulation","authors":"Ilija Jovanov, Michael Naumann, Karthik Kumaravelu, W. Grill, M. Pajic","doi":"10.1109/ICCPS.2018.00033","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00033","url":null,"abstract":"Deep Brain Stimulation (DBS) is effective at alleviating symptoms of neurological disorders such as Parkinson's disease. Yet, despite its safety-critical nature, there does not exist a platform for integrated design and testing of new algorithms or devices. Consequently, we introduce a model-based design framework for DBS controllers based on a physiologically relevant basal-ganglia model (BGM) that we capture as a network of nonlinear hybrid automata, synchronized via neural activation events. The BGM is parametrized by the number of neurons used to model each of the BG regions, which supports tradeoffs between fidelity and complexity of the model. Our hybrid-automata representation is exploited for design of software (Simulink) and hardware (FPGA) BGM platforms, with the latter enabling real-time model simulation and device testing. We demonstrate that the BGM platform is capable of generating physiologically relevant responses to DBS, and validate the BGM using a set of requirements obtained from existing work. We present the use of our framework for design and test of DBS controllers with varying levels of adaptation/feedback. Our evaluations are based on Quality-of-Control metrics that we introduce for runtime monitoring of DBS effectiveness.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130859274","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00023
Maxence Dutreix, S. Coogan
We present an efficient computational procedure to perform model checking on discrete-time, mixed monotone stochastic systems subject to an affine random disturbance. Specifically, we exploit the structure of such systems in order to efficiently compute a finite-state Interval-valued Markov Chain (IMC) that over-approximates the system's behavior. To that end, we first make the assumption that the disturbance is unimodal, symmetric, and independent on each coordinate of the domain. Next, given a rectangular partition of the state-space, we compute bounds on the probability of transition between all the states in the partition. The ease of computing the one-step reachable set of rectangular states under mixed monotone dynamics renders the computation of these transition bounds highly efficient. We furthermore investigate a method for over-approximating the IMC of mixed monotone systems when the disturbance is only approximately unimodal symmetric, and we discuss state-space refinement heuristics. Lastly, we present two verification case studies.
{"title":"Efficient Verification for Stochastic Mixed Monotone Systems","authors":"Maxence Dutreix, S. Coogan","doi":"10.1109/ICCPS.2018.00023","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00023","url":null,"abstract":"We present an efficient computational procedure to perform model checking on discrete-time, mixed monotone stochastic systems subject to an affine random disturbance. Specifically, we exploit the structure of such systems in order to efficiently compute a finite-state Interval-valued Markov Chain (IMC) that over-approximates the system's behavior. To that end, we first make the assumption that the disturbance is unimodal, symmetric, and independent on each coordinate of the domain. Next, given a rectangular partition of the state-space, we compute bounds on the probability of transition between all the states in the partition. The ease of computing the one-step reachable set of rectangular states under mixed monotone dynamics renders the computation of these transition bounds highly efficient. We furthermore investigate a method for over-approximating the IMC of mixed monotone systems when the disturbance is only approximately unimodal symmetric, and we discuss state-space refinement heuristics. Lastly, we present two verification case studies.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129777663","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00035
S. Kato, Shota Tokunaga, Yuya Maruyama, Seiya Maeda, Manato Hirabayashi, Yuki Kitsukawa, Abraham Monrroy, Tomohito Ando, Yusuke Fujii, Takuya Azumi
This paper presents Autoware on Board, a new profile of Autoware, especially designed to enable autonomous vehicles with embedded systems. Autoware is a popular open-source software project that provides a complete set of self-driving modules, including localization, detection, prediction, planning, and control. We customize and extend the software stack of Autoware to accommodate embedded computing capabilities. In particular, we use DRIVE PX2 as a reference computing platform, which is manufactured by NVIDIA Corporation for development of autonomous vehicles, and evaluate the performance of Autoware on ARM-based embedded processing cores and Tegra-based embedded graphics processing units (GPUs). Given that low-power CPUs are often preferred over high-performance GPUs, from the functional safety point of view, this paper focuses on the application of Autoware on ARM cores rather than Tegra ones. However, some Autoware modules still need to be executed on the Tegra cores to achieve load balancing and real-time processing. The experimental results show that the execution latency imposed on the DRIVE PX2 platform is capped at about three times as much as that on a high-end laptop computer. We believe that this observed computing performance is even acceptable for real-world production of autonomous vehicles in certain scenarios.
本文介绍了车载汽车软件(Autoware on Board)，这是一种新的汽车软件，专门为实现嵌入式系统的自动驾驶汽车而设计。Autoware是一个流行的开源软件项目，它提供了一套完整的自动驾驶模块，包括定位、检测、预测、规划和控制。我们定制和扩展Autoware的软件堆栈，以适应嵌入式计算能力。我们特别以NVIDIA公司为开发自动驾驶汽车而制造的DRIVE PX2作为参考计算平台，在基于arm的嵌入式处理内核和基于tegra的嵌入式图形处理单元(gpu)上评估Autoware的性能。考虑到低功耗的cpu往往比高性能的gpu更受青睐，从功能安全的角度考虑，本文主要研究Autoware在ARM内核上的应用，而不是在Tegra内核上的应用。然而，一些Autoware模块仍然需要在Tegra内核上执行，以实现负载平衡和实时处理。实验结果表明，DRIVE PX2平台上的执行延迟上限约为高端笔记本电脑的三倍。我们相信，在某些情况下，这种观察到的计算性能甚至可以用于自动驾驶汽车的实际生产。
{"title":"Autoware on Board: Enabling Autonomous Vehicles with Embedded Systems","authors":"S. Kato, Shota Tokunaga, Yuya Maruyama, Seiya Maeda, Manato Hirabayashi, Yuki Kitsukawa, Abraham Monrroy, Tomohito Ando, Yusuke Fujii, Takuya Azumi","doi":"10.1109/ICCPS.2018.00035","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00035","url":null,"abstract":"This paper presents Autoware on Board, a new profile of Autoware, especially designed to enable autonomous vehicles with embedded systems. Autoware is a popular open-source software project that provides a complete set of self-driving modules, including localization, detection, prediction, planning, and control. We customize and extend the software stack of Autoware to accommodate embedded computing capabilities. In particular, we use DRIVE PX2 as a reference computing platform, which is manufactured by NVIDIA Corporation for development of autonomous vehicles, and evaluate the performance of Autoware on ARM-based embedded processing cores and Tegra-based embedded graphics processing units (GPUs). Given that low-power CPUs are often preferred over high-performance GPUs, from the functional safety point of view, this paper focuses on the application of Autoware on ARM cores rather than Tegra ones. However, some Autoware modules still need to be executed on the Tegra cores to achieve load balancing and real-time processing. The experimental results show that the execution latency imposed on the DRIVE PX2 platform is capped at about three times as much as that on a high-end laptop computer. We believe that this observed computing performance is even acceptable for real-world production of autonomous vehicles in certain scenarios.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125990906","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00044
Hiroki Hayakawa, Takuya Azumi, Akinori Sakaguchi, T. Ushio
This paper proposes a support system for supervision of multiple unmanned aerial vehicles (UAVs) by a single operator. The proposed system makes a plan to complete a given misson such as a delivery service and supervises cooperative behaviors of UAVs. We currently implement a part of the proposed system based on Robot Operating System.
{"title":"ROS-Based Support System for Supervision of Multiple UAVs by a Single Operator","authors":"Hiroki Hayakawa, Takuya Azumi, Akinori Sakaguchi, T. Ushio","doi":"10.1109/ICCPS.2018.00044","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00044","url":null,"abstract":"This paper proposes a support system for supervision of multiple unmanned aerial vehicles (UAVs) by a single operator. The proposed system makes a plan to complete a given misson such as a delivery service and supervises cooperative behaviors of UAVs. We currently implement a part of the proposed system based on Robot Operating System.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132451745","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00048
Ilija Jovanov, Michael Naumann, Karthik Kumaravelu, Vuk Lesi, Aditya Zutshi, W. Grill, M. Pajic
By employing low-voltage electrical stimulation of the basal ganglia (BG) regions of the brain, deep brain stimulation (DBS) devices are used to alleviate the symptoms of several neurological disorders, including Parkinson's disease (PD). Recently, we have developed a Basal Ganglia Model (BGM) that can be utilized for design and evaluation of DBS devices. In this work, we focus on the use of a hardware (FPGA) implementation of the BGM platform to facilitate development of new control policies. Specifically, we introduce a design-time framework that allows for development of suitable control policies, in the form of electrical pulses with variable temporal patterns, while supporting tradeoffs between energy efficiency and efficacy (i.e., Quality-of-Control) of the therapy. The developed framework exploits machine learning and optimization based methods for design-space exploration where predictive behavior for any control configuration (i.e., temporal pattern) is obtained using the BGM platform that simulates physiological response to the considered control in real-time. To illustrate the use of the developed framework, in our demonstration we present how the BGM can be utilized for physiologically relevant BG modeling and design-state exploration for DBS controllers, as well as show the effectiveness of obtained controllers that significantly outperform conventional DBS controllers.
{"title":"Learning-Based Control Design for Deep Brain Stimulation","authors":"Ilija Jovanov, Michael Naumann, Karthik Kumaravelu, Vuk Lesi, Aditya Zutshi, W. Grill, M. Pajic","doi":"10.1109/ICCPS.2018.00048","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00048","url":null,"abstract":"By employing low-voltage electrical stimulation of the basal ganglia (BG) regions of the brain, deep brain stimulation (DBS) devices are used to alleviate the symptoms of several neurological disorders, including Parkinson's disease (PD). Recently, we have developed a Basal Ganglia Model (BGM) that can be utilized for design and evaluation of DBS devices. In this work, we focus on the use of a hardware (FPGA) implementation of the BGM platform to facilitate development of new control policies. Specifically, we introduce a design-time framework that allows for development of suitable control policies, in the form of electrical pulses with variable temporal patterns, while supporting tradeoffs between energy efficiency and efficacy (i.e., Quality-of-Control) of the therapy. The developed framework exploits machine learning and optimization based methods for design-space exploration where predictive behavior for any control configuration (i.e., temporal pattern) is obtained using the BGM platform that simulates physiological response to the considered control in real-time. To illustrate the use of the developed framework, in our demonstration we present how the BGM can be utilized for physiologically relevant BG modeling and design-state exploration for DBS controllers, as well as show the effectiveness of obtained controllers that significantly outperform conventional DBS controllers.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130294476","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00017
Matthew Potok, Chien-Ying Chen, S. Mitra, Sibin Mohan
Discrete manufacturing systems are complex cyber-physical systems (CPS) and their availability, performance, and quality have a big impact on the economy. Smart manufacturing promises to improve these aspects. One key approach that is being pursued in this context is the creation of centralized software-defined control (SDC) architectures and strategies that use diverse sensors and data sources to make manufacturing more adaptive, resilient, and programmable. In this paper, we present SDCWorks—a modeling and simulation framework for SDC. It consists of the semantic structures for creating models, a baseline controller, and an open source implementation of a discrete event simulator for SDCWorks models. We provide the semantics of such a manufacturing system in terms of a discrete transition system which sets up the platform for future research in a new class of problems in formal verification, synthesis, and monitoring. We illustrate the expressive power of SDCWorks by modeling the realistic SMART manufacturing testbed of University of Michigan. We show how our open source SDCWorks simulator can be used to evaluate relevant metrics (throughput, latency, and load) for example manufacturing systems.
{"title":"SDCWorks: A Formal Framework for Software Defined Control of Smart Manufacturing Systems","authors":"Matthew Potok, Chien-Ying Chen, S. Mitra, Sibin Mohan","doi":"10.1109/ICCPS.2018.00017","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00017","url":null,"abstract":"Discrete manufacturing systems are complex cyber-physical systems (CPS) and their availability, performance, and quality have a big impact on the economy. Smart manufacturing promises to improve these aspects. One key approach that is being pursued in this context is the creation of centralized software-defined control (SDC) architectures and strategies that use diverse sensors and data sources to make manufacturing more adaptive, resilient, and programmable. In this paper, we present SDCWorks—a modeling and simulation framework for SDC. It consists of the semantic structures for creating models, a baseline controller, and an open source implementation of a discrete event simulator for SDCWorks models. We provide the semantics of such a manufacturing system in terms of a discrete transition system which sets up the platform for future research in a new class of problems in formal verification, synthesis, and monitoring. We illustrate the expressive power of SDCWorks by modeling the realistic SMART manufacturing testbed of University of Michigan. We show how our open source SDCWorks simulator can be used to evaluate relevant metrics (throughput, latency, and load) for example manufacturing systems.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128257695","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00050
Anna Lukina, Arjun Kumar, Matt Schmittle, Abhijeet Singh, J. Das, Stephen A. Rees, C. V. Buskirk, J. Sztipanovits, R. Grosu, Vijay R. Kumar
Simulation tools offer a low barrier to entry and enable testing and validation before field trials. However, most of the well-known simulators today are challenging to use at scale due to the need for powerful computers and the time required for initial set up. The OpenUAV Swarm Simulator was developed to address these challenges, enabling multi- UAV simulations on the cloud through the NSF CPS-VO. We leverage the Containers as a Service (CaaS) technology to enable students and researchers carry out simulations on the cloud on demand. We have based our framework on opensource tools including ROS, Gazebo, Docker, and the PX4 flight stack, and we designed the simulation framework so that it has no special hardware requirements. The demo and poster will showcase UAV swarm trajectory optimization, and multi- UAV persistent monitoring on the CPS-VO. The code for the simulator is available on GitHub: https://github.com/Open-UAV.
{"title":"Formation Control and Persistent Monitoring in the OpenUAV Swarm Simulator on the NSF CPS-VO","authors":"Anna Lukina, Arjun Kumar, Matt Schmittle, Abhijeet Singh, J. Das, Stephen A. Rees, C. V. Buskirk, J. Sztipanovits, R. Grosu, Vijay R. Kumar","doi":"10.1109/ICCPS.2018.00050","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00050","url":null,"abstract":"Simulation tools offer a low barrier to entry and enable testing and validation before field trials. However, most of the well-known simulators today are challenging to use at scale due to the need for powerful computers and the time required for initial set up. The OpenUAV Swarm Simulator was developed to address these challenges, enabling multi- UAV simulations on the cloud through the NSF CPS-VO. We leverage the Containers as a Service (CaaS) technology to enable students and researchers carry out simulations on the cloud on demand. We have based our framework on opensource tools including ROS, Gazebo, Docker, and the PX4 flight stack, and we designed the simulation framework so that it has no special hardware requirements. The demo and poster will showcase UAV swarm trajectory optimization, and multi- UAV persistent monitoring on the CPS-VO. The code for the simulator is available on GitHub: https://github.com/Open-UAV.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"308 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114416024","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 : 2018-04-11DOI: 10.1109/ICCPS.2018.00025
S. Das, I. Saha
Coverage of a partially known workspace for information gathering is the core problem for several applications, such as search and rescue, precision agriculture and monitoring of critical infrastructures. We propose a planning framework for the coverage of a partially known environment employing multiple robots. To cope with the limitation of having incomplete information, our planner adopts a receding horizon planning strategy where the safe trajectories of the robots are generated optimally for a short duration based on the currently available information about the workspace. Moreover, as multi-robot motion planning for coverage is a computationally complex problem, our framework clusters the robots into small groups to increase the planning efficiency dynamically. In each time horizon, the robots follow the motion plans provided by the planner, gather information about the workspace while executing their plans and update the global knowledge base about the workspace. The planning algorithm manages the activities of the robots in such a way that the energy consumption by the robots and the total time required for the complete coverage of the workspace get minimized. Simulation results show that the proposed hierarchical framework efficiently ensures the coverage quality of a partially known workspace, as well as scales up effectively with the number of robots and the size of the workspace.
{"title":"Rhocop: Receding Horizon Multi-Robot Coverage","authors":"S. Das, I. Saha","doi":"10.1109/ICCPS.2018.00025","DOIUrl":"https://doi.org/10.1109/ICCPS.2018.00025","url":null,"abstract":"Coverage of a partially known workspace for information gathering is the core problem for several applications, such as search and rescue, precision agriculture and monitoring of critical infrastructures. We propose a planning framework for the coverage of a partially known environment employing multiple robots. To cope with the limitation of having incomplete information, our planner adopts a receding horizon planning strategy where the safe trajectories of the robots are generated optimally for a short duration based on the currently available information about the workspace. Moreover, as multi-robot motion planning for coverage is a computationally complex problem, our framework clusters the robots into small groups to increase the planning efficiency dynamically. In each time horizon, the robots follow the motion plans provided by the planner, gather information about the workspace while executing their plans and update the global knowledge base about the workspace. The planning algorithm manages the activities of the robots in such a way that the energy consumption by the robots and the total time required for the complete coverage of the workspace get minimized. Simulation results show that the proposed hierarchical framework efficiently ensures the coverage quality of a partially known workspace, as well as scales up effectively with the number of robots and the size of the workspace.","PeriodicalId":199062,"journal":{"name":"2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122356981","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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