Automatic Control and Computer Sciences

, Volume 53, Issue 1, pp 90–95 | Cite as

Mobile Robot Performance at Restricted Energy and Autonomy

  • A. BaumsEmail author


Two methods are proposed to estimate the performance of ground mobile robots. As the performance criterion, the distance the robot is able to cover to reach the event place by using its inner energy source is selected. In the first method, it is assumed that all the distance to the GPS can be overcame by the robot under operator control. In the second method, the distance is divided into operator-controlled and robot autonomous motion ones, and the energy needed in this case is analyzed.


robot performance energy source autonomy adjustable motions 


  1. 1.
    Fisher, M., Dennis, L., and Webste, M., Verifying autonomous systems, Commun. ACM, 2013, vol. 56, pp. 84–93.CrossRefGoogle Scholar
  2. 2.
    Basilico, N., and Amigoni, F., Exploration strategies based on Multi-Criteria Decision for search and an autonomous robots, in Autonomous Robots, Springer, 2011, pp. 99–106.Google Scholar
  3. 3.
    Bruemmer, D.J., Few, D.A., Boring, R.L., Walton, M.C., and Nielsen, C.W., Shared understanding for collaborative control, IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum., 2005, vol. 35, no. 4.Google Scholar
  4. 4. Scholar
  5. 5.
    Mars Exploration Rover Mission: Home. Scholar
  6. 6.
    Witze, A., Nature News, Jan. 26, 2017.Google Scholar
  7. 7.
    Shibli, M. and Anwar, S., A novel generalized nonholonomy criteria and physical interpretation of holonomic/nonholonomic constraints of a free-flying space, Intell. Control Autom., 2011, vol. 2, no. 4, pp. 267–283.CrossRefGoogle Scholar
  8. 8.
    Liu, J., Chou, P.H., Bagherzadeh, N., and Kurdahi, F., Power-aware scheduling under timing constraints for mission-critical embedded systems, Design Automation Conference, Proceedings, 2001, pp. 494–504.Google Scholar
  9. 9.
    Schreckenghost, D. and Milam, T., Measuring performance in real time during remote human-robot operation with adjustable autonomy, IEEE Intell. Syst., 2010, pp. 36–44.Google Scholar
  10. 10.
    Hexmoor, H., Adjusting autonomy by introspection, Proceedings of AAAI Spring Symposium on Agents with Adjustable Autonomy, 1999, pp. 61–64.Google Scholar
  11. 11.
    Barber, K., Goel, A., and Martin, C., Dynamic adaptive autonomy in multi-agent systems, J. Exp. Theor. Artif. Intell., 2000, vol. 12, no. 2, pp. 129–148.CrossRefzbMATHGoogle Scholar
  12. 12.
    Schäfer, H.B., Luksch, T., and Berns, K., Obstacle detection and avoidance for mobile outdoor robotics, IEEE International Conference on Robotics and Automation Pasadena, CA, USA, 2008, pp. 19–23.Google Scholar
  13. 13.
    Schenker, P.S., et al., Reconfigurable robots for all terrain exploration, Proceedings of the SPIE Symposium on Sensor Fusion and Decentralized Control in Robotic Systems III, 2000, vol. 4196, pp. 419–434.Google Scholar
  14. 14.
    Cobano, J.A., Estremera, P., and De Santos, G., Accurate tracking of legged robots on natural terrain, Auton. Rob., 2010, vol. 28, no. 2, pp. 231–244.CrossRefGoogle Scholar
  15. 15.
    Baums, A. and Gordyusin, A., An evaluation the motion of a reconfigurable mobile robot over a rough terrain, Autom. Control Comput. Sci., 2015, vol. 49, no. 5, pp. 39–45.Google Scholar
  16. 16.
    Gonzalez, P., Garcia, E., Estremera, J., Armad, M., and Jimeenez, M., DYLEMA: Using walking robots for landmine detection and location, 2000 IEEE Robot Automat. Mag., 2000, pp. 35–43.Google Scholar
  17. 17.
    Rong, P. and Pedram, M., Battery aware power management based on Markovian decision processes, Proceedings of the IEEE/ACM International Conference on Computer aided Design, 2002, pp. 707–713.Google Scholar
  18. 18.
    Rao, R., Vrudhula, S., and Rakhmatov, D., Battery modeling for energy-aware system design, Computer, 2003, vol. 36, no. 12, pp. 77–87.CrossRefGoogle Scholar
  19. 19.
    Vrudhula, S. and Rakhmatov, D., Energy management for battery powered embedded systems, ACM Trans. Embedded Comput. Syst., 2003, vol. 2, no. 3, pp. 277–324.CrossRefGoogle Scholar
  20. 20.
    Rao, V., Singhal, G., Kumar, A., and Navet, N., Battery model for embedded systems, Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design (VLSID’05), 2005.Google Scholar
  21. 21.
    Casas, R. and Casas, O., Battery sensing for energy-aware system design, Computer, 2005, vol. 38, no. 11, pp. 48–54.CrossRefGoogle Scholar
  22. 22.
    Choset, H. and Pignon, P., Path planning: The Boustrophedon cellular decomposition, Proceedings of the International Conference on Field and Service Robotics, 1997, pp. 1311–1320.Google Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.Institute of Electronics and Computer ScienceRigaLatvia

Personalised recommendations