Simultaneous mobile sink allocation in home environments with applications in mobile consumer robotics

Article English OPEN
Chanak, Prasenjit ; Banerjee, Indrajit ; Sherratt, R. Simon (2015)

This paper presents a novel mobile sink area allocation scheme for consumer based mobile robotic devices with a proven application to robotic vacuum cleaners. In the home or office environment, rooms are physically separated by walls and an automated robotic cleaner cannot make a decision about which room to move to and perform the cleaning task. Likewise, state of the art cleaning robots do not move to other rooms without direct human interference. In a smart home monitoring system, sensor nodes may be deployed to monitor each separate room.\ud In this work, a quad tree based data gathering scheme is proposed whereby the mobile sink physically moves through every room and logically links all separated sub-networks together. The proposed scheme sequentially collects data from the monitoring environment and transmits the information back to a base station. According to the sensor nodes information, the base station can command a cleaning robot to move to a specific location in the home environment. The quad tree based data gathering scheme minimizes the data gathering tour length and time through the efficient allocation of data gathering areas. A calculated shortest path data gathering tour can efficiently be allocated to the robotic cleaner to complete the cleaning task within a minimum time period. Simulation results show that the proposed scheme can effectively allocate and control the cleaning area to the robot vacuum cleaner without any direct interference from the consumer. The performance of the proposed scheme is then validated with a set of practical sequential data gathering tours in a typical office/home environment.
  • References (15)
    15 references, page 1 of 2

    [1] S. Kim, J.-Y. Sim, and S. Yang, “Vision-based cleaning area control for cleaning robots,” IEEE Trans. Consum. Electron. vol. 58, no. 2, pp. 685-690, May 2012.

    [2] J. Wang, Y. Yin, J. Zhang, S. Lee, and R. S. Sherratt, “Mobility based energy efficient and multi-sink algorithms for consumer home networks,” IEEE Trans. Consum. Electron. vol. 59, no. 1, pp. 77-84, Feb. 2013.

    [3] P. Chanak, I. Banerjee, J. Wang, and R. S. Sherratt, “Obstacle avoidance routing scheme through optimal sink movement for home monitoring and mobile robotic consumer devices,” IEEE Trans. on Consum. Electron., vol. 60, no. 4, pp. 596-606, Nov. 2014

    [4] I. A. Zualkernan, A. R. Al-Ali, M. A. Jabbar, I. Zabalawi, and A. Wasfy, “InfoPods: Zigbee-based remote information monitoring devices for smart-homes,” IEEE Trans. Consum. Electron., vol. 55, no. 3, pp. 1221- 1226, Aug. 2009.

    [5] N. L. Doh, C. Kim, and W. K. Chung, “A practical path planner for the robotic vacuum cleaner in rectilinear environments,” IEEE Trans. Consum. Electron., vol. 53, no. 2, pp. 519-527, Nov. 2007.

    [6] J. S. Oh, Y. H. Choi, J. B. Park, and Y. F. Zheng, “Complete coverage navigation of cleaning robots using triangular-cell-based map,” IEEE Trans. Ind. Electron., vol.51, No. 3, pp. 718 - 726, Jun. 2004.

    [7] M.-C. Kang, K.-S. Kim, D.-K. Noh, J.-W. Han, and S.-J. Ko, “A robust obstacle detection method for robotic vacuum cleaners,” IEEE Trans. Consum. Electron., vol. 60, no. 4, pp. 587 - 595, Nov. 2014.

    [8] C.-H. Kuo, H.-C. Chou, and S.-Y. Tasi, “Pneumatic sensor: a complete coverage improvement approach for robotic cleaners,” IEEE Trans. Instrum. Meas., vol. 60, no. 4, pp. 1237-1256, Apr. 2011.

    [9] Y.-W. Bai and M.-F. Hsueh, “Using an adaptive iterative learning algorithm for planning of the path of an autonomous robotic vacuum cleaner,” in Proc. IEEE Global Conf. Consum. Electron., Tokyo, Japan, pp. 401-405, Oct. 2012.

    [10] I. Banerjee, P. Chanak, H. Rahaman, and T. Samanta, “Effective fault detection and routing scheme for wireless sensor networks,” Computers & Electrical Engineering, Elsevier, vol. 40, no. 2, pp. 291-306, Feb. 2014.

  • Metrics
    0
    views in OpenAIRE
    0
    views in local repository
    68
    downloads in local repository

    The information is available from the following content providers:

    From Number Of Views Number Of Downloads
    Central Archive at the University of Reading - IRUS-UK 0 68
Share - Bookmark