Mobile Robots Navigation

Because our system does not explicitly model the geometric world, its geometric accuracy is limited. Therefore, when compared with map-based approaches using calibrated cameras (Royer et al., 2007), the errors exhibited by the simple control scheme of our algorithm are rather large. Nevertheless, the remarkable flexibility and versatility of the system offer some important advantages over more precise techniques. With our approach, one can literally take an off-the-shelf camera, attach it to the robot, align it approximately in the forward direction, and start the system. The algorithm is not perfect, and there are scenarios in which it will fail. For example, occasionally the algorithm does not properly transition to the next milestone image, in which case the overlap between the current and milestone image can decrease to the point that an insufficient number of features are matched. Also, untextured scenes containing distant trees, bushes, or undecorated indoor hallways sometimes prevent the KLT algorithm from successfully tracking enough features to accurately compute the heading direction. While only a handful of features are necessary for the algorithm to succeed, it is important that features exist on both sides of the image, and that some number of features remain visible throughout the milestone. Another source of error is due to distant features. Although features near the center of the image produce a narrow funnel lane even when they are far from the camera, distant features near the side of the image produce much larger funnel lanes which are less useful for navigation. Moreover, image parallax is inversely proportional to the distance to a feature. As a result, distant features are primarily useful for correcting the rotation of the robot and are quite incapable of informing the robot about minor translation errors. This problem is compounded by the inherent ambiguity between rotation and translation in the funnel lane itself. Even though this ambiguity has little effect when the robot is near the path, it hinders the ability of the visual information to correctly determine the correct amount of rotation when the robot has deviated significantly. Odometry helps to overcome this limitation, and we have conducted experiments in which the robot consistently returns to the path after deviating by several meters. However, much larger deviations either initially or during replay cannot be handled by our present system. At any rate, it should be noted that odometry drift is not an issue because we only store odometry values local to the segment, not in a global coordinate frame Preface......Page 5 Rémi Boutteau, Xavier Savatier, Jean-Yves Ertaud and Bélahcène Mazari......Page 15 Keita Atsuumi and Manabu Sano......Page 39 Oscar Montiel, Alfredo González and Roberto Sepúlveda......Page 55 Dirk Holz, David Droeschel, Sven Behnke, Stefan May and Hartmut Surmann......Page 67 S.Parasuraman, Bijan Shirinzadeh and V.Ganapathy......Page 99 Elmar Mair and Darius Burschka......Page 121 Shung Han Cho, Yuntai Kyong, Yunyoung Nam, Sangjin Hong and We-Duke Cho......Page 145 Young Jae Lee and Sankyung Sung......Page 171 Toon Goedemé and Luc Van Gool......Page 185 Diana D. Tsankova......Page 211 Yasar Ayaz, Atsushi Konno, Khalid Munawar, Teppei Tsujita and Masaru Uchiyama......Page 237 E. Jauregi, E. Lazkano and B. Sierra......Page 255 Andres Mora, Keiji Nagatani and Kazuya Yoshida......Page 277 S. Veera Ragavan and Velappa Ganapathy......Page 303 Carlos Astengo-Noguez, Gildardo Sanchez-Ante, José Ramón Calzada and Ricardo Sisnett-Hernández......Page 321 Lech Polkowski and Pawel Osmialowski......Page 343 Victor Ayala-Ramirez, Jose A. Gasca-Martinez, Rigoberto Lopez-Padilla and Raul E. Sanchez-Yanez......Page 369 Masaki Takahashi, Yoshimasa Tada, Takafumi Suzuki, and Kazuo Yoshida......Page 381 Juan Marcos Toibero, Flavio Roberti, Fernando Auat Cheein, Carlos Soria and Ricardo Carelli......Page 393 You-gen Chen, Seiji Yasunobu,Wei-hua Gui, Ren-yong Wei and Zhi-yong Li......Page 415 Zhichao Chen and Stanley T. Birchfield......Page 441 Tomás Arredondo and Wolfgang Freund......Page 461 Andrew Lammas, Karl Sammut and Fangpo He......Page 471 Nelson David Muñoz Ceballos, Jaime Alejandro Valencia and Nelson Londoño Ospina......Page 499 Pablo Piñol, Otoniel López, Miguel Martínez-Rach, M.P. Malumbres, José Oliver and Carlos Calafate......Page 515 Leonimer F Melo , Jose F Mangili Jr, Fernando C Dias Neto and Joao M Rosario......Page 529 Alejandra Barrera......Page 549 Raquel Frizera Vassallo, Hans Jörg Andreas Schneebeli and José Santos-Victor......Page 577 Julián Sánchez-Hermosilla, Francisco Rodríguez, Ramón González, José Luís Guzmán and Manuel Berenguel......Page 597 Yoshiyuki Noda, Akira Kawaguchi and Kazuhiko Terashima......Page 623 Riaan Stopforth (ZS5RSA), Glen Bright and R. Harley......Page 643 Benítez-Read, Jorge S. and Rojas-Ramírez, Erick......Page 669 In this chapter we have analyzed the performance of current commercial video codecs for video streaming through 802.11 networks running in a remotely teleoperated mobile robots testbed. We have developed a client-server application based in DirectX/DirectShow architecture for testing video compression, data delivery and error resilience behavior in a real scenario. A first analysis was performed to study the teleoperation control loop delays and the performance of video delivery process in order to get a first impression of overall system behavior. For this kind of applications, compression is mandatory for proper operation in order to cope with the minimum video quality and bounded delay demanded by them. The use of video compressors allows us to adjust the required quality without exhausting network resources like available bandwidth and they significantly reduce the control loop delay improving the functionality of this kind of applications. With respect to packet losses we have observed that H.264/AVC codec is the one that best performance results achieves. Also, we have checked that intra coding mode gets better results than inter mode, since it avoids error propagation. In the packetization process, the RTP packet size determines the resulting application bandwidth (goodput). Results show that the longer the packet size is, the higher goodput the application gets. Finally, we think that telerobotic applications running under standard wireless network technologies like IEEE 802.11 can benefit from the use of state-of-the-art video encoders, like H.264/AVC, to improve throughput, error resilience and real-time feedback The use of a sensor fusion layer, which processes the data in a fuzzy manner, reduces the amount of information to be processed by the stage that controls the speed and direction of the robot. Matlab has been used to implement the control scheme on the PC. The signals (commands and measurements) to and from the robot are transmitted through radiofrequency modules mounted on both the PC and the Khepera robot. The processing is fast enough, preventing delays that could damage the robot. In addition, the fusion sensor layer reduces the disturbances caused by the noise, mainly originated by the light sources, thus giving certain degree of robustness to the system: the robot moves smoothly. On the other hand, the response of the neural network is very close to the ideal one. In fact, part of the error comes from the non homogeneous disposition of the sensors. Nevertheless, there offers a resolution of ±2 degrees, with an execution time of 10 ms, which is ideal for applications that require real time processing. At this moment, only temperature is being measured by the prototype. The temperature measured value is sent to the PC using a radiofrequency module