Event based localization in Ackermann steering limited resource mobile robots

“© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”This paper presents a local sensor fusion technique with an event-based global position correction to improve the localization of a mobile robot with limited computational resources. The proposed algorithms use a modified Kalman filter and a new local dynamic model of an Ackermann steering mobile robot. It has a similar performance but faster execution when compared to more complex fusion schemes, allowing its implementation inside the robot. As a global sensor, an event-based position correction is implemented using the Kalman filter error covariance and the position measurement obtained from a zenithal camera. The solution is tested during a long walk with different trajectories using a LEGO Mindstorm NXT robot.This work was supported by FEDER-CICYT projects with references DPI2011-28507-C02-01 and DPI2010-20814-C02-02, financed by the Ministerio de Ciencia e Innovacion (Spain). This work was also supported by the University of Costa Rica.Marín, L.; Vallés Miquel, M.; Soriano Vigueras, Á.; Valera Fernández, Á.; Albertos Pérez, P. (2014). Event based localization in Ackermann steering limited resource mobile robots. IEEE/ASME Transactions on Mechatronics. 19(4):1171-1182. doi:10.1109/TMECH.2013.2277271S1171118219

Precision improvement of MEMS gyros for indoor mobile robots with horizontal motion inspired by methods of TRIZ

In the paper, the problem of precision improvement for the MEMS gyrosensors on indoor robots with horizontal motion is solved by methods of TRIZ ("the theory of inventive problem solving").Comment: 6 pages, the paper is accepted to 9th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Hawaii, USA (IEEE-NEMS 2014) as an oral presentatio

Low cost inertial-based localization system for a service robot

Dissertation presented at Faculty of Sciences and Technology of the New University of Lisbon to attain the Master degree in Electrical and Computer Science EngineeringThe knowledge of a robot’s location it’s fundamental for most part of service robots. The success of tasks such as mapping and planning depend on a good robot’s position knowledge. The main goal of this dissertation is to present a solution that provides a estimation of the robot’s location. This is, a tracking system that can run either inside buildings or outside them, not taking into account just structured environments. Therefore, the localization system takes into account only measurements relative. In the presented solution is used an AHRS device and digital encoders placed on wheels to make a estimation of robot’s position. It also relies on the use of Kalman Filter to integrate sensorial information and deal with estimate errors. The developed system was testes in real environments through its integration on real robot. The results revealed that is not possible to attain a good position estimation using only low-cost inertial sensors. Thus, is required the integration of more sensorial information, through absolute or relative measurements technologies, to provide a more accurate position estimation

Contributions to automated realtime underwater navigation

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2012This dissertation presents three separate–but related–contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field–some have been demonstrated in all three arenas. The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors. The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current–the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework–without altering the filter structure or requiring any ad-hoc adjustments. The end result is a mathematically principled treatment of the delay that leads to more consistent estimates with lower error and uncertainty. Field results from dead reckoning aided by acoustic positioning systems demonstrate the applicability of this approach to real-world problems in underwater navigation.I have been financially supported by: the National Defense Science and Engineering Graduate (NDSEG) Fellowship administered by the American Society for Engineering Education, the Edwin A. Link Foundation Ocean Engineering and Instrumentation Fellowship, and WHOI Academic Programs office

Sensors Utilisation and Data Collection of Underground Mining

This study reviews IMU significance and performance for underground mine drone localisation. This research has designed a Kalman filter which extracts reliable information from raw data. Kalman filter for INS combines different measurements considering estimated errors to produce a trajectory including time, position and attitude. To evaluate the feasibility of the proposed method, a prototype has been designed and evaluated. Experimental results indicate that the designed Kalman filter estimates the internal states of a system

Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots

This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments.This work has been partially funded by FEDER-CICYT projects with references DPI2011-28507-C02-01 and DPI2010-20814-C02-02, financed by Ministerio de Ciencia e Innovacion (Spain). Also, the financial support from the University of Costa Rica is greatly appreciated.Marín, L.; Vallés Miquel, M.; Soriano Vigueras, Á.; Valera Fernández, Á.; Albertos Pérez, P. (2013). Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots. 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IEEE Transactions on Industrial Electronics, 60(10), 4562-4573. doi:10.1109/tie.2012.2216235Boccadoro, M., Martinelli, F., & Pagnottelli, S. (2010). Constrained and quantized Kalman filtering for an RFID robot localization problem. Autonomous Robots, 29(3-4), 235-251. doi:10.1007/s10514-010-9194-zMadhavan, R., Fregene, K., & Parker, L. E. (2004). Distributed Cooperative Outdoor Multirobot Localization and Mapping. Autonomous Robots, 17(1), 23-39. doi:10.1023/b:auro.0000032936.24187.41Yunchun Yang, & Farrell, J. A. (2003). Magnetometer and differential carrier phase GPS-aided INS for advanced vehicle control. IEEE Transactions on Robotics and Automation, 19(2), 269-282. doi:10.1109/tra.2003.809591Zhang, T., & Xu, X. (2012). A new method of seamless land navigation for GPS/INS integrated system. Measurement, 45(4), 691-701. doi:10.1016/j.measurement.2011.12.021Shen, Z., Georgy, J., Korenberg, M. J., & Noureldin, A. (2011). Low cost two dimension navigation using an augmented Kalman filter/Fast Orthogonal Search module for the integration of reduced inertial sensor system and Global Positioning System. Transportation Research Part C: Emerging Technologies, 19(6), 1111-1132. doi:10.1016/j.trc.2011.01.001Kotecha, J. H., & Djuric, P. M. (2003). Gaussian particle filtering. IEEE Transactions on Signal Processing, 51(10), 2592-2601. doi:10.1109/tsp.2003.816758Seyboth, G. S., Dimarogonas, D. V., & Johansson, K. H. (2013). Event-based broadcasting for multi-agent average consensus. Automatica, 49(1), 245-252. doi:10.1016/j.automatica.2012.08.042Guinaldo, M., Fábregas, E., Farias, G., Dormido-Canto, S., Chaos, D., Sánchez, J., & Dormido, S. (2013). A Mobile Robots Experimental Environment with Event-Based Wireless Communication. Sensors, 13(7), 9396-9413. doi:10.3390/s130709396Meng, X., & Chen, T. (2013). Event based agreement protocols for multi-agent networks. Automatica, 49(7), 2125-2132. doi:10.1016/j.automatica.2013.03.002Campion, G., Bastin, G., & Dandrea-Novel, B. (1996). Structural properties and classification of kinematic and dynamic models of wheeled mobile robots. IEEE Transactions on Robotics and Automation, 12(1), 47-62. doi:10.1109/70.481750Ward, C. C., & Iagnemma, K. (2008). A Dynamic-Model-Based Wheel Slip Detector for Mobile Robots on Outdoor Terrain. IEEE Transactions on Robotics, 24(4), 821-831. doi:10.1109/tro.2008.924945Zohar, I., Ailon, A., & Rabinovici, R. (2011). Mobile robot characterized by dynamic and kinematic equations and actuator dynamics: Trajectory tracking and related application. Robotics and Autonomous Systems, 59(6), 343-353. doi:10.1016/j.robot.2010.12.001De La Cruz, C., & Carelli, R. (2008). Dynamic model based formation control and obstacle avoidance of multi-robot systems. Robotica, 26(3), 345-356. doi:10.1017/s0263574707004092Attia, H. A. (2005). Dynamic model of multi-rigid-body systems based on particle dynamics with recursive approach. Journal of Applied Mathematics, 2005(4), 365-382. doi:10.1155/jam.2005.365LEGO NXT Mindsensorshttp://www.mindsensors.comLEGO NXT HiTechnic Sensorshttp://www.hitechnic.com/sensorsLEGO 9V Technic Motors Compared Characteristicshttp://wwwphilohome.com/motors/motorcomp.htmIG-500N: GPS Aided Miniature INShttp://www.sbg-systems.com/products/ig500n-miniature-ins-gpsIGEPv2 Boardhttp://www.isee.biz/products/igep-processor-boards/igepv2-dm3730EKF/UKF Toolbox for Matlab V1.3http://www.lce.hut.fi/research/mm/ekfukf

System Development of an Unmanned Ground Vehicle and Implementation of an Autonomous Navigation Module in a Mine Environment

There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine the structural integrity of the roof and pillars through the generation of high-fidelity 3D maps. The generation of the maps will allow the miners to rapidly respond to any increasing hazards with proactive measures such as: sending workers to build/rebuild support structure to prevent accidents. The objective of this research is the development, implementation and testing of a robust unmanned ground vehicle (UGV) that will operate in mine environments for extended periods of time. To achieve this, a custom skid-steer four-wheeled UGV is designed to operate in these challenging underground mine environments. To autonomously navigate these environments, the UGV employs the use of a Light Detection and Ranging (LiDAR) and tactical grade inertial measurement unit (IMU) for the localization and mapping through a tightly-coupled LiDAR Inertial Odometry via Smoothing and Mapping framework (LIO-SAM). The autonomous navigation module was implemented based upon the Fast likelihood-based collision avoidance with an extension to human-guided navigation and a terrain traversability analysis framework. In order to successfully operate and generate high-fidelity 3D maps, the system was rigorously tested in different environments and terrain to verify its robustness. To assess the capabilities, several localization, mapping and autonomous navigation missions were carried out in a coal mine environment. These tests allowed for the verification and tuning of the system to be able to successfully autonomously navigate and generate high-fidelity maps