Pub Date : 2007-12-01DOI: 10.1109/ARSO.2007.4531416
R. Luo, Yi J. Chen, C.T. Liao, An-Chih Tsai
Monitoring and tracking human from a mobile robot is an essential technology in robot applications. This paper presents a data fusion modeling methodology to detect and track human. Each image with human is simultaneously acquired with a range profundity scanning from a laser range finder (LRF). In the image, the face is detected and tracked by our modified AdaBoost scheme. The human body is modeled and extracted from the range data. The probability of the two models, face and human body, are both defined based on the Gaussian distribution. And the two probabilities are fused by statistical independence. According to the result of fusing algorithm, the motion planning for the robot is obtained by the Jacobian transformation. In the experiment, we exploit our proposed method to our robot for human tracking under the scenario of human-robot interaction. The experimental results show that the proposed method is successfully implemented for human tracking by fusing range and intensity data.
{"title":"Mobile robot based human detection and tracking using range and intensity data fusion","authors":"R. Luo, Yi J. Chen, C.T. Liao, An-Chih Tsai","doi":"10.1109/ARSO.2007.4531416","DOIUrl":"https://doi.org/10.1109/ARSO.2007.4531416","url":null,"abstract":"Monitoring and tracking human from a mobile robot is an essential technology in robot applications. This paper presents a data fusion modeling methodology to detect and track human. Each image with human is simultaneously acquired with a range profundity scanning from a laser range finder (LRF). In the image, the face is detected and tracked by our modified AdaBoost scheme. The human body is modeled and extracted from the range data. The probability of the two models, face and human body, are both defined based on the Gaussian distribution. And the two probabilities are fused by statistical independence. According to the result of fusing algorithm, the motion planning for the robot is obtained by the Jacobian transformation. In the experiment, we exploit our proposed method to our robot for human tracking under the scenario of human-robot interaction. The experimental results show that the proposed method is successfully implemented for human tracking by fusing range and intensity data.","PeriodicalId":344670,"journal":{"name":"2007 IEEE Workshop on Advanced Robotics and Its Social Impacts","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115639537","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 : 2007-12-01DOI: 10.1109/ARSO.2007.4531413
R. Luo, W.H. Chang
We have constructed a self-maintain system based on the concept of the artificial ecological system (AES). Under the framework of the AES, we proposed a model of ecological balancing include the sensor nodes dynamics model (SNDM), the sensor nodes ecological model (SNEM) and the population growth limit model (PGLM). The SNDM is used to implement the diffusion, and the SNEM is used to maintain the sensor nodes. The PGLM can control the sensor network density. We discussed the effect of the prey node searching and handling. With these models, we can create an ecological balance environment with automatic recharge, recycle and quantity control. It is desired to keep these sensor nodes reach the blanket coverage and maximize, two species exist and ensure they will not die out. As a result, save tremendous human resource needed and cost on this self-maintain ecological system.
{"title":"Self managed system of sensor network — an artificial ecological system","authors":"R. Luo, W.H. Chang","doi":"10.1109/ARSO.2007.4531413","DOIUrl":"https://doi.org/10.1109/ARSO.2007.4531413","url":null,"abstract":"We have constructed a self-maintain system based on the concept of the artificial ecological system (AES). Under the framework of the AES, we proposed a model of ecological balancing include the sensor nodes dynamics model (SNDM), the sensor nodes ecological model (SNEM) and the population growth limit model (PGLM). The SNDM is used to implement the diffusion, and the SNEM is used to maintain the sensor nodes. The PGLM can control the sensor network density. We discussed the effect of the prey node searching and handling. With these models, we can create an ecological balance environment with automatic recharge, recycle and quantity control. It is desired to keep these sensor nodes reach the blanket coverage and maximize, two species exist and ensure they will not die out. As a result, save tremendous human resource needed and cost on this self-maintain ecological system.","PeriodicalId":344670,"journal":{"name":"2007 IEEE Workshop on Advanced Robotics and Its Social Impacts","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129277497","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 : 2007-12-01DOI: 10.1109/ARSO.2007.4531426
P. Cheng, Chun-Yen Chen
The current paper proposes a novel algorithm to construct an efficient path for each joint of the weightlifting robot based on the proposed "momentum method". The Dijkstra algorithm, a typical searching method in artificial intelligence, is adopted to obtain the shortest path. A novel idea is proposed to improve the efficiency of Dijkstra algorithm so that it took less time to search for solutions with high accuracy. In order to obtain a high efficiency computing processes in dynamics, we formulize the optimal path with the discrete grid points based on the B-spline theory, so that we can calculate the angular velocity, angular acceleration and moment more precisely. The path planning dynamic model of three joints weightlifting robot is presented. The results of simulations demonstrated the effective and practical work with the proposed method in this paper.
{"title":"Path planning and dynamic simulation of weightlifting robot manipulator","authors":"P. Cheng, Chun-Yen Chen","doi":"10.1109/ARSO.2007.4531426","DOIUrl":"https://doi.org/10.1109/ARSO.2007.4531426","url":null,"abstract":"The current paper proposes a novel algorithm to construct an efficient path for each joint of the weightlifting robot based on the proposed \"momentum method\". The Dijkstra algorithm, a typical searching method in artificial intelligence, is adopted to obtain the shortest path. A novel idea is proposed to improve the efficiency of Dijkstra algorithm so that it took less time to search for solutions with high accuracy. In order to obtain a high efficiency computing processes in dynamics, we formulize the optimal path with the discrete grid points based on the B-spline theory, so that we can calculate the angular velocity, angular acceleration and moment more precisely. The path planning dynamic model of three joints weightlifting robot is presented. The results of simulations demonstrated the effective and practical work with the proposed method in this paper.","PeriodicalId":344670,"journal":{"name":"2007 IEEE Workshop on Advanced Robotics and Its Social Impacts","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115230611","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}
{"title":"IEEE workshop on advanced robotics and its social impacts","authors":"J. Tokyo","doi":"10.1109/arso.2007.4531433","DOIUrl":"https://doi.org/10.1109/arso.2007.4531433","url":null,"abstract":"The following topics are dealt with: mobile robot; path planning; manipulator; sensor fusion; humanoid robot; SLAM; security robot; surgical robot.","PeriodicalId":344670,"journal":{"name":"2007 IEEE Workshop on Advanced Robotics and Its Social Impacts","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125108574","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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