Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147982
A. Speranzon, S. Shivkumar, R. Ghrist
We present a novel approach to localize an unknown planar sensor network based on sparse sampling of partially observable paths traversed by moving agents. The problem is inspired by mapping the geometry of a building floorplan via "uncooperative sensing", using data from camera feeds and other tracking-capable sensors. Unique challenges include having no knowledge of sensor placement, coverage or their extrinsic parameters nor the knowledge of the motion of the people within a floorplan. The methods used are, at first, topological, to build a combinatorial model with the appropriate topology. This model is then augmented to include weak geometric information, and optimization techniques are used to approximate the domain. Topological information is captured within the optimization problem to constrain the solution.
{"title":"On Sensor Network Localization Exploiting Topological Constraints*","authors":"A. Speranzon, S. Shivkumar, R. Ghrist","doi":"10.23919/ACC45564.2020.9147982","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147982","url":null,"abstract":"We present a novel approach to localize an unknown planar sensor network based on sparse sampling of partially observable paths traversed by moving agents. The problem is inspired by mapping the geometry of a building floorplan via \"uncooperative sensing\", using data from camera feeds and other tracking-capable sensors. Unique challenges include having no knowledge of sensor placement, coverage or their extrinsic parameters nor the knowledge of the motion of the people within a floorplan. The methods used are, at first, topological, to build a combinatorial model with the appropriate topology. This model is then augmented to include weak geometric information, and optimization techniques are used to approximate the domain. Topological information is captured within the optimization problem to constrain the solution.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"88 S5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132511675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147717
Huayi Li, Nan I. Li, I. Kolmanovsky, A. Girard
Connected and autonomous vehicles are expected to improve mobility and transportation, as well as to provide energy efficiency benefits. The integration of safety and energy efficiency aspects is challenging as there are certain tradeoffs between them, and also because the assessment of these attributes requires different time horizons. This paper illustrates the development of a controller for highway driving that, through reinforcement learning, can simultaneously address requirements of safety, comfort, performance and energy efficiency for battery electric vehicles. The training process of the decision policy exploits traffic simulations that are capable of representing the interactive behavior of vehicles in traffic based on game theory. Results indicate the potential for improved energy efficiency by adding powertrain-related states in the decision policy and by suitably defining the reward function.
{"title":"Energy-Efficient Autonomous Vehicle Control Using Reinforcement Learning and Interactive Traffic Simulations","authors":"Huayi Li, Nan I. Li, I. Kolmanovsky, A. Girard","doi":"10.23919/ACC45564.2020.9147717","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147717","url":null,"abstract":"Connected and autonomous vehicles are expected to improve mobility and transportation, as well as to provide energy efficiency benefits. The integration of safety and energy efficiency aspects is challenging as there are certain tradeoffs between them, and also because the assessment of these attributes requires different time horizons. This paper illustrates the development of a controller for highway driving that, through reinforcement learning, can simultaneously address requirements of safety, comfort, performance and energy efficiency for battery electric vehicles. The training process of the decision policy exploits traffic simulations that are capable of representing the interactive behavior of vehicles in traffic based on game theory. Results indicate the potential for improved energy efficiency by adding powertrain-related states in the decision policy and by suitably defining the reward function.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130074578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147294
Hrishik Mishra, M. Stefano, A. Giordano, R. Lampariello, C. Ott
In this paper, we investigate the task of approaching a rigid tumbling satellite (Target) with a fully-actuated manipulator-equipped spacecraft (Servicer). We consider a Servicer with an end-effector-mounted exteroceptive sensor for feedback of Target motion. This sensor, however, provides only a noisy relative pose (position and orientation) of the tumbling Target's grasping frame. For this time-varying scenario, we propose a novel method, which is a cascade interconnection of a geometric Extended Kalman Filter (EKF) observer and a geometric controller. The key idea is to estimate the unforced Target's full state-space with the proposed EKF, and then use these estimates in feed-forward and feedback terms of the control law, while exploiting the fully-actuated Servicer. This results in a cascade interconnection, for which we prove the Local Asymptotic Stability (LAS) property. Furthermore, the effectiveness of the proposed method for the approach task is demonstrated through simulation.
{"title":"A Geometric Controller for Fully-Actuated Robotic Capture of a Tumbling Target","authors":"Hrishik Mishra, M. Stefano, A. Giordano, R. Lampariello, C. Ott","doi":"10.23919/ACC45564.2020.9147294","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147294","url":null,"abstract":"In this paper, we investigate the task of approaching a rigid tumbling satellite (Target) with a fully-actuated manipulator-equipped spacecraft (Servicer). We consider a Servicer with an end-effector-mounted exteroceptive sensor for feedback of Target motion. This sensor, however, provides only a noisy relative pose (position and orientation) of the tumbling Target's grasping frame. For this time-varying scenario, we propose a novel method, which is a cascade interconnection of a geometric Extended Kalman Filter (EKF) observer and a geometric controller. The key idea is to estimate the unforced Target's full state-space with the proposed EKF, and then use these estimates in feed-forward and feedback terms of the control law, while exploiting the fully-actuated Servicer. This results in a cascade interconnection, for which we prove the Local Asymptotic Stability (LAS) property. Furthermore, the effectiveness of the proposed method for the approach task is demonstrated through simulation.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130116450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147878
Michelle L. Gegel, D. Bristow, R. Landers
Additive manufacturing processes fabricate parts in a layer-by-layer fashion, depositing material along a predefined path before incrementing to the next layer. Although the thickness of any given layer is bounded, in-layer dynamics can couple with layer-to-layer dynamics such that height defects amplify from one layer to the next. This is considered instability in the layer domain. By considering each layer as an iteration, additive processes can be categorized as repetitive processes. Although Repetitive Process Control (RPC) algorithms exist that can stabilize the process and converge to desired reference, it is typically assumed that the reference and disturbance are constant from layer to layer. In this paper, the problem of tracking references (layer thicknesses) that change from layer to layer is considered. The bandwidth of the changing references is considered bounded in both the spatial and layer domains. A loop-shaping design process is then considered, in which the bounds are mapped to a bound on the two-dimensional sensitivity function and projected onto weighting filters in an LQR control formulation. The layer-to-layer controller is then constructed from traditional LQR methods. The controller is demonstrated on a simulation of laser metal deposition for a wavy wall build having frequency content in both the spatial and layer domains.
{"title":"A Loop-Shaping Method for Frequency-Based Design of Layer-to-Layer Control for Laser Metal Deposition","authors":"Michelle L. Gegel, D. Bristow, R. Landers","doi":"10.23919/ACC45564.2020.9147878","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147878","url":null,"abstract":"Additive manufacturing processes fabricate parts in a layer-by-layer fashion, depositing material along a predefined path before incrementing to the next layer. Although the thickness of any given layer is bounded, in-layer dynamics can couple with layer-to-layer dynamics such that height defects amplify from one layer to the next. This is considered instability in the layer domain. By considering each layer as an iteration, additive processes can be categorized as repetitive processes. Although Repetitive Process Control (RPC) algorithms exist that can stabilize the process and converge to desired reference, it is typically assumed that the reference and disturbance are constant from layer to layer. In this paper, the problem of tracking references (layer thicknesses) that change from layer to layer is considered. The bandwidth of the changing references is considered bounded in both the spatial and layer domains. A loop-shaping design process is then considered, in which the bounds are mapped to a bound on the two-dimensional sensitivity function and projected onto weighting filters in an LQR control formulation. The layer-to-layer controller is then constructed from traditional LQR methods. The controller is demonstrated on a simulation of laser metal deposition for a wavy wall build having frequency content in both the spatial and layer domains.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130482329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/acc45564.2020.9147716
Ion Matei, Maksym Zhenirovskyy, J. Kleer, C. Somarakis, J. Baras
We address the problem of learning laws governing the behavior of physical systems. As a use case we choose the discovery of the dynamics of micron-scale chiplets in dielectric fluid whose motion is controlled by a set of electric potential. We use the port-Hamiltonian formalism as a high level model structure that is continuously refined based on our understanding of the physical process. In addition, we use machine learning inspired models as low level representations. Representation structure is key in learning generalizable models, as shown by the learning results.
{"title":"Learning physical laws: the case of micron size particles in dielectric fluid","authors":"Ion Matei, Maksym Zhenirovskyy, J. Kleer, C. Somarakis, J. Baras","doi":"10.23919/acc45564.2020.9147716","DOIUrl":"https://doi.org/10.23919/acc45564.2020.9147716","url":null,"abstract":"We address the problem of learning laws governing the behavior of physical systems. As a use case we choose the discovery of the dynamics of micron-scale chiplets in dielectric fluid whose motion is controlled by a set of electric potential. We use the port-Hamiltonian formalism as a high level model structure that is continuously refined based on our understanding of the physical process. In addition, we use machine learning inspired models as low level representations. Representation structure is key in learning generalizable models, as shown by the learning results.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"20 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147917
Yunus Emre Sahin, R. Quirynen, S. D. Cairano
We propose a decision-making system for auto-mated driving with formal guarantees, synthesized from Signal Temporal Logic (STL) specifications. STL formulae specifying overall and intermediate driving goals and the traffic rules are encoded as mixed-integer inequalities and combined with a simplified vehicle motion model, resulting in a mixed-integer optimization problem. The specification satisfaction for the actual vehicle motion is guaranteed by imposing constraints on the quantitative semantics of STL. For reducing the com-putational burden, we propose an STL encoding that results in a block-sparse structure. The same STL formulae are used for monitoring faults due to imperfect prediction on the vehicle and environment. We demonstrate our method on an urban scenario with intersections, obstacles, and no-pass zones.
{"title":"Autonomous Vehicle Decision-Making and Monitoring based on Signal Temporal Logic and Mixed-Integer Programming","authors":"Yunus Emre Sahin, R. Quirynen, S. D. Cairano","doi":"10.23919/ACC45564.2020.9147917","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147917","url":null,"abstract":"We propose a decision-making system for auto-mated driving with formal guarantees, synthesized from Signal Temporal Logic (STL) specifications. STL formulae specifying overall and intermediate driving goals and the traffic rules are encoded as mixed-integer inequalities and combined with a simplified vehicle motion model, resulting in a mixed-integer optimization problem. The specification satisfaction for the actual vehicle motion is guaranteed by imposing constraints on the quantitative semantics of STL. For reducing the com-putational burden, we propose an STL encoding that results in a block-sparse structure. The same STL formulae are used for monitoring faults due to imperfect prediction on the vehicle and environment. We demonstrate our method on an urban scenario with intersections, obstacles, and no-pass zones.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131637446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/acc45564.2020.9147707
Meryem Deniz, P. L. Devi, S. Balakrishnan
Although rigorous framework exists for handling state variable inequality constraints under optimal control formulations, it is quite involved and difficult to incorporate for online use. In this study, an alternative approach is proposed by combining a state-dependent Riccati equation (SDRE) based inverse optimal control formulation with a set-theoretic barrier Lyapunov function (STBLF). Necessary derivations are presented. Both regulator and tracking type problems are considered. The performance of the proposed method is evaluated using numerical examples.
{"title":"Inverse Optimal Control with Set-Theoretic Barrier Lyapunov Function for Handling State Constraints","authors":"Meryem Deniz, P. L. Devi, S. Balakrishnan","doi":"10.23919/acc45564.2020.9147707","DOIUrl":"https://doi.org/10.23919/acc45564.2020.9147707","url":null,"abstract":"Although rigorous framework exists for handling state variable inequality constraints under optimal control formulations, it is quite involved and difficult to incorporate for online use. In this study, an alternative approach is proposed by combining a state-dependent Riccati equation (SDRE) based inverse optimal control formulation with a set-theoretic barrier Lyapunov function (STBLF). Necessary derivations are presented. Both regulator and tracking type problems are considered. The performance of the proposed method is evaluated using numerical examples.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131643929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147340
Isaac E. Weintraub, Alexander Von Moll, Eloy García, D. Casbeer, Z. Demers, M. Pachter
This paper considers a two agent scenario containing an observer and a non-maneuvering target. The observer is maneuverable but is slower than the course-holding target. In this scenario, the observer is endowed with a nonzero radius of observation within which he strives at keeping the target for as long as possible. Using the calculus of variations, we pose and solve the optimal control problem, solving for the heading of the observer which maximizes the amount of time the target remains inside the radius of observation. Utilizing the optimal observer heading we compute the exposure time based upon the angle by which the target is initially captured. Presented, along with an example, are the zero-time of exposure heading, maximum time of observation heading, and proof that observation is persistent under optimal control.
{"title":"Maximum Observation of a Faster Non-Maneuvering Target by a Slower Observer","authors":"Isaac E. Weintraub, Alexander Von Moll, Eloy García, D. Casbeer, Z. Demers, M. Pachter","doi":"10.23919/ACC45564.2020.9147340","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147340","url":null,"abstract":"This paper considers a two agent scenario containing an observer and a non-maneuvering target. The observer is maneuverable but is slower than the course-holding target. In this scenario, the observer is endowed with a nonzero radius of observation within which he strives at keeping the target for as long as possible. Using the calculus of variations, we pose and solve the optimal control problem, solving for the heading of the observer which maximizes the amount of time the target remains inside the radius of observation. Utilizing the optimal observer heading we compute the exposure time based upon the angle by which the target is initially captured. Presented, along with an example, are the zero-time of exposure heading, maximum time of observation heading, and proof that observation is persistent under optimal control.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130784813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147734
Felix Boewing, Maximilian Schiffer, Mauro Salazar, M. Pavone
This paper presents an algorithmic framework to optimize the operation of an Autonomous Mobility-on-Demand system whereby a centrally controlled fleet of electric self-driving vehicles provides on-demand mobility. In particular, we first present a mixed-integer linear program that captures the joint vehicle coordination and charge scheduling problem, accounting for the battery level of the single vehicles and the energy availability in the power grid. Second, we devise a heuristic algorithm to compute near-optimal solutions in polynomial time. Finally, we apply our algorithm to realistic case studies for Newport Beach, CA. Our results validate the near optimality of our method with respect to the global optimum, whilst suggesting that through vehicle-to-grid operation we can enable a 100% penetration of renewable energy sources and still provide a high-quality mobility service.
{"title":"A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems","authors":"Felix Boewing, Maximilian Schiffer, Mauro Salazar, M. Pavone","doi":"10.23919/ACC45564.2020.9147734","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147734","url":null,"abstract":"This paper presents an algorithmic framework to optimize the operation of an Autonomous Mobility-on-Demand system whereby a centrally controlled fleet of electric self-driving vehicles provides on-demand mobility. In particular, we first present a mixed-integer linear program that captures the joint vehicle coordination and charge scheduling problem, accounting for the battery level of the single vehicles and the energy availability in the power grid. Second, we devise a heuristic algorithm to compute near-optimal solutions in polynomial time. Finally, we apply our algorithm to realistic case studies for Newport Beach, CA. Our results validate the near optimality of our method with respect to the global optimum, whilst suggesting that through vehicle-to-grid operation we can enable a 100% penetration of renewable energy sources and still provide a high-quality mobility service.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131012667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-01DOI: 10.23919/ACC45564.2020.9147886
Zhenyuan Yuan, Minghui Zhu
We propose a communication-aware Gaussian process regression algorithm that allows a network of robots to collaboratively learn about a common latent function in real time using streaming data. We quantify the improvement that inter-robot communication brings on the transient performance of the learning algorithm. Simulations are performed to validate the proposed algorithm.
{"title":"Communication-aware Distributed Gaussian Process Regression Algorithms for Real-time Machine Learning","authors":"Zhenyuan Yuan, Minghui Zhu","doi":"10.23919/ACC45564.2020.9147886","DOIUrl":"https://doi.org/10.23919/ACC45564.2020.9147886","url":null,"abstract":"We propose a communication-aware Gaussian process regression algorithm that allows a network of robots to collaboratively learn about a common latent function in real time using streaming data. We quantify the improvement that inter-robot communication brings on the transient performance of the learning algorithm. Simulations are performed to validate the proposed algorithm.","PeriodicalId":288450,"journal":{"name":"2020 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130747228","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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