Pub Date : 2014-11-20DOI: 10.1109/MMAR.2014.6957449
S. Radkowski, P. Szulim
In the paper, solution of the problem of vibrations which appears during the maneuvers of quadrocopters is presented. Those special kinds of vibration aren't related to mounting or manufacturing faults. In this kinds of flying machines the main sources of vibration comes from motors and/or propellers unbalance. The paper shows analytical description of the source of this problem. Mathematical description of rotor which was subjected of additional rotation had been modeled in Matlab - Simulink environment. It was shown, that correct set of parameters lead to total elimination of this kind of vibration. In paper is shown also investigation on real object and influence of results on vibration reduction.
{"title":"Analysis of vibration of rotors in unmanned aircraft","authors":"S. Radkowski, P. Szulim","doi":"10.1109/MMAR.2014.6957449","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957449","url":null,"abstract":"In the paper, solution of the problem of vibrations which appears during the maneuvers of quadrocopters is presented. Those special kinds of vibration aren't related to mounting or manufacturing faults. In this kinds of flying machines the main sources of vibration comes from motors and/or propellers unbalance. The paper shows analytical description of the source of this problem. Mathematical description of rotor which was subjected of additional rotation had been modeled in Matlab - Simulink environment. It was shown, that correct set of parameters lead to total elimination of this kind of vibration. In paper is shown also investigation on real object and influence of results on vibration reduction.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126628803","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957373
T. Glówka, J. Figwer
In the paper an approach to discrete-time frequency response identification of a plant operating under feedback in the case of periodic disturbances is presented. In the presented approach to discrete-time frequency response identification integrated bispectra and higher-order non-gaussian multisine excitations are applied. A focus on almost sure convergence of the identified discrete-time frequency response to true plant is given. The presented discussion is illustrated by examples showing effectiveness of the proposed approach.
{"title":"Frequency response identification using integrated bispectra — Periodic disturbances case","authors":"T. Glówka, J. Figwer","doi":"10.1109/MMAR.2014.6957373","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957373","url":null,"abstract":"In the paper an approach to discrete-time frequency response identification of a plant operating under feedback in the case of periodic disturbances is presented. In the presented approach to discrete-time frequency response identification integrated bispectra and higher-order non-gaussian multisine excitations are applied. A focus on almost sure convergence of the identified discrete-time frequency response to true plant is given. The presented discussion is illustrated by examples showing effectiveness of the proposed approach.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126953114","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957432
A. H. Qureshi, Saba Mumtaz, Wajeeha Khan, Abdul Ahad Ashfaq Sheikh, Fahad Iqbal Khawaja, Y. Ayaz, Osman Hasan
Collision free navigation in dynamic environments, where motion of moving obstacles is unknown, still presents a significant challenge. Sampling based algorithms are well known for their simplicity and are widely used in many real time motion planning problems. While many sampling based algorithms for dynamic environments exist, assumptions taken by these algorithms such as known trajectories of moving obstacles, make them unsuitable for motion planning in real-world problems. In this paper, we present RRT* based motion planning in unknown dynamic environments. Effectiveness of our idea is demonstrated in multiple simulations with more than 15 simultaneously moving obstacles placed in various environments.
{"title":"Augmenting RRT∗-planner with local trees for motion planning in complex dynamic environments","authors":"A. H. Qureshi, Saba Mumtaz, Wajeeha Khan, Abdul Ahad Ashfaq Sheikh, Fahad Iqbal Khawaja, Y. Ayaz, Osman Hasan","doi":"10.1109/MMAR.2014.6957432","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957432","url":null,"abstract":"Collision free navigation in dynamic environments, where motion of moving obstacles is unknown, still presents a significant challenge. Sampling based algorithms are well known for their simplicity and are widely used in many real time motion planning problems. While many sampling based algorithms for dynamic environments exist, assumptions taken by these algorithms such as known trajectories of moving obstacles, make them unsuitable for motion planning in real-world problems. In this paper, we present RRT* based motion planning in unknown dynamic environments. Effectiveness of our idea is demonstrated in multiple simulations with more than 15 simultaneously moving obstacles placed in various environments.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130094435","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957477
T. Itami
We control macroscopic Brownian motion by group robots. To make an object track a required path, feedforward control input is found using our preceding model of continuum mechanical description. We use dynamical balance in continuum mechanics to calculate the feedforward input in each time. We see a phenomenon that the object does not move in a required direction in initial stage of Brownian motion. Due to the phenomenon, large feedback component added to the feedforward input is not avoided to make the object track a required path. We show that feedforward with feedback component works well. But appropriate prediction of a dead time must be incorporated into our continuum model.
{"title":"Controlling Brownian motion applied to macroscopic group robots without mutual communication","authors":"T. Itami","doi":"10.1109/MMAR.2014.6957477","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957477","url":null,"abstract":"We control macroscopic Brownian motion by group robots. To make an object track a required path, feedforward control input is found using our preceding model of continuum mechanical description. We use dynamical balance in continuum mechanics to calculate the feedforward input in each time. We see a phenomenon that the object does not move in a required direction in initial stage of Brownian motion. Due to the phenomenon, large feedback component added to the feedforward input is not avoided to make the object track a required path. We show that feedforward with feedback component works well. But appropriate prediction of a dead time must be incorporated into our continuum model.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113959774","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957335
P. Mazurek
Dim objects tracking, like asteroids or satellites, require high performance algorithm. Track-Before-Detect (TBD) algorithms could be applied for such scenario, but the computation power is very demanding. Giant amount of trajectories that could be processed is the main problem. The application of GPGPU allows the computation using optimized Downsampled Spatio-Temporal TBD (ST-TBD) algorithm for small part of image sequences. Message Passing Interface (MPICH2) is applied for the image transmission and state-space exchange between computers and GPGPUs. Analysis of the system with multiple processing computers, for bandwidth limited network, is provided. The system could be limited by the bandwidth of network interface, not by GPGPU, quite often. The derived formula for this system allows the design with best optimization of GPGPUs.
{"title":"Parallel distributed downsampled spatio-temporal track-before-detect algorithm","authors":"P. Mazurek","doi":"10.1109/MMAR.2014.6957335","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957335","url":null,"abstract":"Dim objects tracking, like asteroids or satellites, require high performance algorithm. Track-Before-Detect (TBD) algorithms could be applied for such scenario, but the computation power is very demanding. Giant amount of trajectories that could be processed is the main problem. The application of GPGPU allows the computation using optimized Downsampled Spatio-Temporal TBD (ST-TBD) algorithm for small part of image sequences. Message Passing Interface (MPICH2) is applied for the image transmission and state-space exchange between computers and GPGPUs. Analysis of the system with multiple processing computers, for bandwidth limited network, is provided. The system could be limited by the bandwidth of network interface, not by GPGPU, quite often. The derived formula for this system allows the design with best optimization of GPGPUs.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122346821","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957444
Dominik Luczak, Krzysztof Nowopolski, Krzysztof Siembab, Bartlomiej Wicher
In this paper three methods of electric motor angular speed calculation are compared. The source of the measurement signal is a 14-bit absolute encoder. The authors compared the well-known classic methods M with more advanced approaches. First of the complex methods is based on utilization of phase loop lock system in an estimation process using on-line system model. The third method discussed in the paper is focused on application of Chebyshev filter. Short description of all of the mentioned method is followed by simulation and experimental results. The results are presented on an instance of permanent magnet synchronous motor drive, with both speed controller and speed calculation algorithms implemented in a digital signal processor system.
{"title":"Speed calculation methods in electrical drive with non-ideal position sensor","authors":"Dominik Luczak, Krzysztof Nowopolski, Krzysztof Siembab, Bartlomiej Wicher","doi":"10.1109/MMAR.2014.6957444","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957444","url":null,"abstract":"In this paper three methods of electric motor angular speed calculation are compared. The source of the measurement signal is a 14-bit absolute encoder. The authors compared the well-known classic methods M with more advanced approaches. First of the complex methods is based on utilization of phase loop lock system in an estimation process using on-line system model. The third method discussed in the paper is focused on application of Chebyshev filter. Short description of all of the mentioned method is followed by simulation and experimental results. The results are presented on an instance of permanent magnet synchronous motor drive, with both speed controller and speed calculation algorithms implemented in a digital signal processor system.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122410719","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957337
P. Kozierski, M. Lis, A. Owczarkowski, D. Horla
The article proposes an approach to power system state estimation allowing the division of the network into smaller parts and performing calculations for each part at the same time. The latter can be implemented in parallel, but the main aim has been to propose a method for dispersed calculations, i.e. calculations that may be performed on computing units located at various points of the whole power system. In the paper, there are 3 algorithms for which dispersed versions have been proposed: Extended Kalman Filter, Particle Filter and Extended Kalman Particle Filter. As a result of the simulations, it has been verified that the Dispersed Particle Filter works better than simple Particle Filter. In two other cases, distributed algorithms work worse, but for the Extended Kalman Filter degradation in the estimation quality is not significant.
{"title":"Dispersed filters for power system state estimation","authors":"P. Kozierski, M. Lis, A. Owczarkowski, D. Horla","doi":"10.1109/MMAR.2014.6957337","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957337","url":null,"abstract":"The article proposes an approach to power system state estimation allowing the division of the network into smaller parts and performing calculations for each part at the same time. The latter can be implemented in parallel, but the main aim has been to propose a method for dispersed calculations, i.e. calculations that may be performed on computing units located at various points of the whole power system. In the paper, there are 3 algorithms for which dispersed versions have been proposed: Extended Kalman Filter, Particle Filter and Extended Kalman Particle Filter. As a result of the simulations, it has been verified that the Dispersed Particle Filter works better than simple Particle Filter. In two other cases, distributed algorithms work worse, but for the Extended Kalman Filter degradation in the estimation quality is not significant.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"352 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122843809","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957408
S. Butt, R. Prabel, Robert Grimmecke, H. Aschemann
In this paper, a nonlinear control approach based on a control-oriented model of an engine cooling system for vehicles is presented. An electrically driven coolant pump and a servo-controlled bypass valve act as control inputs. Due to physical limitations regarding the volume flow provided by the coolant pump as well as the opening section of the servo-controlled bypass valve, a constrained control problem arises. Therefore, a nonlinear model-predictive controller is employed, which explicitly takes the actuator limitations into account. A reduced-order disturbance observer is used to estimate unmeasured heat flows within the system in order to obtain a reliable prediction. An experimental analysis highlights the effectiveness of the nonlinear model-predictive control strategy in combination with a reduced-order observer.
{"title":"Nonlinear model-predictive control for an engine cooling system with smart valve and pump","authors":"S. Butt, R. Prabel, Robert Grimmecke, H. Aschemann","doi":"10.1109/MMAR.2014.6957408","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957408","url":null,"abstract":"In this paper, a nonlinear control approach based on a control-oriented model of an engine cooling system for vehicles is presented. An electrically driven coolant pump and a servo-controlled bypass valve act as control inputs. Due to physical limitations regarding the volume flow provided by the coolant pump as well as the opening section of the servo-controlled bypass valve, a constrained control problem arises. Therefore, a nonlinear model-predictive controller is employed, which explicitly takes the actuator limitations into account. A reduced-order disturbance observer is used to estimate unmeasured heat flows within the system in order to obtain a reliable prediction. An experimental analysis highlights the effectiveness of the nonlinear model-predictive control strategy in combination with a reduced-order observer.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122973214","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957413
P. Kicman, J. Narkiewicz
In this paper the monocular visual odometry algorithm augmented with pose graph optimization is presented. The algorithm was tested using five different combinations of feature extractors and descriptors and evaluated using two challenging datasets from KITTI database. The main result of this study is that the implementation of pose graph optimization may lead to reduction of position error ranging between 1.53% to 76.05%. The error reduction depends on a feature type and dataset used.
{"title":"Pose graph for improved monocular visual odometry","authors":"P. Kicman, J. Narkiewicz","doi":"10.1109/MMAR.2014.6957413","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957413","url":null,"abstract":"In this paper the monocular visual odometry algorithm augmented with pose graph optimization is presented. The algorithm was tested using five different combinations of feature extractors and descriptors and evaluated using two challenging datasets from KITTI database. The main result of this study is that the implementation of pose graph optimization may lead to reduction of position error ranging between 1.53% to 76.05%. The error reduction depends on a feature type and dataset used.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589753","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 : 2014-11-20DOI: 10.1109/MMAR.2014.6957429
V. Vaitkus, Paulius Lengvenis, G. Zylius
Driving style can be characteristically divided into normal and aggressive. Related researches show that useful information about driving style can be extracted using vehicle's inertial measurement signals with the help of GPS. However, for public transportation the GPS sensor isn't necessary because of repetition of the route. This assumption helps to create low-cost intelligent public transport monitoring system that is capable to classify aggressive and normal driver. In this paper, we propose pattern recognition approach to classify driving style into aggressive or normal automatically without expert evaluation and knowledge using accelerometer data when driving the same route in different driving styles. 3-axis accelerometer signal statistical features were used as classifier inputs. The results show that aggressive and normal driving style classification of 100% precision is achieved using collected data when driving the same route.
{"title":"Driving style classification using long-term accelerometer information","authors":"V. Vaitkus, Paulius Lengvenis, G. Zylius","doi":"10.1109/MMAR.2014.6957429","DOIUrl":"https://doi.org/10.1109/MMAR.2014.6957429","url":null,"abstract":"Driving style can be characteristically divided into normal and aggressive. Related researches show that useful information about driving style can be extracted using vehicle's inertial measurement signals with the help of GPS. However, for public transportation the GPS sensor isn't necessary because of repetition of the route. This assumption helps to create low-cost intelligent public transport monitoring system that is capable to classify aggressive and normal driver. In this paper, we propose pattern recognition approach to classify driving style into aggressive or normal automatically without expert evaluation and knowledge using accelerometer data when driving the same route in different driving styles. 3-axis accelerometer signal statistical features were used as classifier inputs. The results show that aggressive and normal driving style classification of 100% precision is achieved using collected data when driving the same route.","PeriodicalId":166287,"journal":{"name":"2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"156 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128763592","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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