Pub Date : 2016-04-22DOI: 10.1109/AMC.2016.7496330
Manabu Shinohara, Takatsugu Oda, K. Nonaka, K. Sekiguchi
There is a demand for autonomous driving control in front-wheel steering vehicles because it is expected to make driving safer and easier and also to reduce the driving workload. In order to perform safe driving with autonomous driving control, it is necessary to consider unexpected disturbances when the vehicle is moving and that tire forces have limitations. We propose autonomous driving control combining Model Predictive Control (MPC) and Sliding Mode Control (SMC). In this paper, we employ MPC in order to consider the maximum tire forces. SMC is employed to deal with unexpected disturbances that the model has not anticipated. Furthermore, we confirmed that path following control is possible by practical inspection using a small front-wheel steering vehicle that is susceptible to unexpected disturbances.
{"title":"Model predictive path following control with acceleration constraints for front steering vehicles","authors":"Manabu Shinohara, Takatsugu Oda, K. Nonaka, K. Sekiguchi","doi":"10.1109/AMC.2016.7496330","DOIUrl":"https://doi.org/10.1109/AMC.2016.7496330","url":null,"abstract":"There is a demand for autonomous driving control in front-wheel steering vehicles because it is expected to make driving safer and easier and also to reduce the driving workload. In order to perform safe driving with autonomous driving control, it is necessary to consider unexpected disturbances when the vehicle is moving and that tire forces have limitations. We propose autonomous driving control combining Model Predictive Control (MPC) and Sliding Mode Control (SMC). In this paper, we employ MPC in order to consider the maximum tire forces. SMC is employed to deal with unexpected disturbances that the model has not anticipated. Furthermore, we confirmed that path following control is possible by practical inspection using a small front-wheel steering vehicle that is susceptible to unexpected disturbances.","PeriodicalId":273847,"journal":{"name":"2016 IEEE 14th International Workshop on Advanced Motion Control (AMC)","volume":"744 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132091741","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 : 2016-04-22DOI: 10.1109/AMC.2016.7496383
M. Diop, Lee-Yeng Ong, T. Lim, L. Hun
This paper presents the design and analysis of a computer vision-aided motion sensing algorithm for wheeled mobile robot's indoor navigation. The algorithm is realized using two vision cameras attached on a wheeled mobile robot. The first camera is positioned at front-looking direction while the second camera is positioned at downward-looking direction. An algorithm is developed to process the images acquired from the cameras to yield the mobile robot's positions and orientations. The proposed algorithm is implemented on a wheeled mobile robot for real-world effectiveness testing. Results are compared and shown the accuracy of the proposed algorithm. At the end of the paper, an artificial landmark approach is introduced to improve the navigation efficiency. Future work involved implementing the proposed artificial landmark for indoor navigation applications with minimized accumulated errors.
{"title":"A computer vision-aided motion sensing algorithm for mobile robot's indoor navigation","authors":"M. Diop, Lee-Yeng Ong, T. Lim, L. Hun","doi":"10.1109/AMC.2016.7496383","DOIUrl":"https://doi.org/10.1109/AMC.2016.7496383","url":null,"abstract":"This paper presents the design and analysis of a computer vision-aided motion sensing algorithm for wheeled mobile robot's indoor navigation. The algorithm is realized using two vision cameras attached on a wheeled mobile robot. The first camera is positioned at front-looking direction while the second camera is positioned at downward-looking direction. An algorithm is developed to process the images acquired from the cameras to yield the mobile robot's positions and orientations. The proposed algorithm is implemented on a wheeled mobile robot for real-world effectiveness testing. Results are compared and shown the accuracy of the proposed algorithm. At the end of the paper, an artificial landmark approach is introduced to improve the navigation efficiency. Future work involved implementing the proposed artificial landmark for indoor navigation applications with minimized accumulated errors.","PeriodicalId":273847,"journal":{"name":"2016 IEEE 14th International Workshop on Advanced Motion Control (AMC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127306053","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 : 2016-04-22DOI: 10.1109/AMC.2016.7496373
Baptiste Rouzier, T. Murakami
The driving assistance technology is an interesting method to increase the safety on the road. By helping the driver to avoid dangerous situations while letting him in charge of the behavior of the vehicle during normal conditions, this kind of system combines both the rapid reactions of an automated system and the human ability to react to unpredictable scenarios. The main demanding aspect of such an assistance is the capability to detect every encountered hazard and to correctly estimate both its nature and location in the space of the moving vehicle. For that purpose, this paper describes an implementation of an active driving assistant on an electric car, as well as the detection process of the dangers, their identifications and location estimations. Moreover to ensure a better detection coverage of the surrounding of the vehicle, a sharing process of the detected hazards between different systems in the controlled car environment is presented.
{"title":"Hazard detection and cognition for an active driving assistance","authors":"Baptiste Rouzier, T. Murakami","doi":"10.1109/AMC.2016.7496373","DOIUrl":"https://doi.org/10.1109/AMC.2016.7496373","url":null,"abstract":"The driving assistance technology is an interesting method to increase the safety on the road. By helping the driver to avoid dangerous situations while letting him in charge of the behavior of the vehicle during normal conditions, this kind of system combines both the rapid reactions of an automated system and the human ability to react to unpredictable scenarios. The main demanding aspect of such an assistance is the capability to detect every encountered hazard and to correctly estimate both its nature and location in the space of the moving vehicle. For that purpose, this paper describes an implementation of an active driving assistant on an electric car, as well as the detection process of the dangers, their identifications and location estimations. Moreover to ensure a better detection coverage of the surrounding of the vehicle, a sharing process of the detected hazards between different systems in the controlled car environment is presented.","PeriodicalId":273847,"journal":{"name":"2016 IEEE 14th International Workshop on Advanced Motion Control (AMC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128785507","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 : 2016-04-22DOI: 10.1109/AMC.2016.7496410
Li Xie, W. Herberger, Weiliang Xu, K. Stol
The Mecanum wheel, due to its omnidirectional mobility and heavy-duty transporting ability on the ground plane, is widely applied in the industry. However, the Mecanum wheel trades off energy efficiency for maneuverability. This paper proposes a novel energy consumption model of the four-wheel omnidirectional Mecanum mobile robots. The model is built based on a comprehensive understanding of the kinematics, dynamics, and energy flow of the Mecanum mobile robot. The energy consumption model has been mathematically implemented in MATLAB, and experimentally validated on Auckbot, the Mecanum mobile robot, developed in our lab. Simulation and experimental results show that for omnidirectional motion primitives on the ground plane, the energy consumption model has over 98% accuracy. This proposed energy consumption model is essential to the energy-optimal motion planning for the Mecanum mobile robot.
{"title":"Experimental validation of energy consumption model for the four-wheeled omnidirectional Mecanum robots for energy-optimal motion control","authors":"Li Xie, W. Herberger, Weiliang Xu, K. Stol","doi":"10.1109/AMC.2016.7496410","DOIUrl":"https://doi.org/10.1109/AMC.2016.7496410","url":null,"abstract":"The Mecanum wheel, due to its omnidirectional mobility and heavy-duty transporting ability on the ground plane, is widely applied in the industry. However, the Mecanum wheel trades off energy efficiency for maneuverability. This paper proposes a novel energy consumption model of the four-wheel omnidirectional Mecanum mobile robots. The model is built based on a comprehensive understanding of the kinematics, dynamics, and energy flow of the Mecanum mobile robot. The energy consumption model has been mathematically implemented in MATLAB, and experimentally validated on Auckbot, the Mecanum mobile robot, developed in our lab. Simulation and experimental results show that for omnidirectional motion primitives on the ground plane, the energy consumption model has over 98% accuracy. This proposed energy consumption model is essential to the energy-optimal motion planning for the Mecanum mobile robot.","PeriodicalId":273847,"journal":{"name":"2016 IEEE 14th International Workshop on Advanced Motion Control (AMC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127178595","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：