Assisting 3D Indoor Positioning for Robot Navigation

With the increasing employment of mobile robots to achieve different tasks in various applications, the need for localization and body position for these robots is increasing rapidly. Many techniques are proposed to calculate the precise coordinates of a robot based on the distances measured between the robot and a set of reference points. Also, internal sensors, such as accelerometers and gyroscopes, are used to detect the body position and the direction of the robot. However, the effect of obstacles in an indoor environment and sensor drifts still limit the applicability of such systems. Thus, in this study, a novel technique that uses one or more robots to compensate for the missing stationary points is proposed. The robots in the proposed technique collaborate to improve the positioning accuracy, by providing reference points to each other. Per each movement execution of one robot, the remaining robots remain stationary, to provide the required reference points. When the robot finishes the movement execution, its position is updated based on the signals collected from the other robots, in addition to the position calculated by the onboard sensors. Then, another robot is selected to execute its movement.. The results show that the proposed method has been able to improve the positioning accuracy, by increasing the number of collaborating robots, when the median function if used to select the coordinates of the robot, among the candidate positions.

___

R. Momose, T. Nitta, M. Yanagisawa, N. Togawa, “An accurate indoor positioning algorithm using particle filter based on the proximity of bluetooth beacons”, in Consumer Electronics (GCCE), 2017 IEEE 6th Global Conference, pp. 1-5, 2017. [CrossRef]

S. He, S. H. G. Chan, “Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons”, IEEE Communications Surveys & Tutorials, vol. 18, pp. 466-490, 2016. [CrossRef]

A. Alarifi, A. Al-Salman, M. Alsaleh, A. Alnafessah, S. Al-Hadhrami, M. A. Al-Ammar, eAl-Khalifa HS, “Ultra wideband indoor positioning technologies: Analysis and recent advances”, Sensors, vol. 16, p. 707, 2016. [CrossRef]

P. Nazemzadeh, F. Moro, D. Fontanelli, D. Macii, L. Palopoli, “Indoor positioning of a robotic walking assistant for large public environments”, IEEE Transactions on Instrumentation and Measurement, vol. 64, pp. 2965-2976, 2015. [CrossRef]

W. Zhang, M. S. Chowdhury, M. Kavehrad, “Asynchronous indoor positioning system based on visible light communications”, Optical Engineering, vol. 53, p. 045105, 2014. [CrossRef]

J. Wang, A. Hu, C. Liu, X. Li, “A floor-map-aided WiFi/pseudo-odometry integration algorithm for an indoor positioning system”, Sensors, vol. 15, pp. 7096-7124, 2015. [CrossRef]

X.-Y. Lin, T.-W. Ho, C.-C. Fang, Z.-S. Yen, B.-J. Yang, F. Lai, “A mobile indoor positioning system based on iBeacon technology”, in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 4970-4973, 2015.

L. P. N. Sekar, A. Santos, O. Beltramello, “IMU Drift Reduction for Augmented Reality Applications”, in International Conference on Augmented and Virtual Reality, pp. 188-196, 2015.

M. Narasimhappa, A. D. Mahindrakar, V. C. Guizilini, M. H. Terra, S. L. Sabat, “An improved Sage Husa adaptive robust Kalman Filter for de-noising the MEMS IMU drift signal”, in Indian Control Conference (ICC). 2018, pp. 229-234, 2018. [CrossRef]