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Active target search for high dimensional robotic systems

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Abstract

When a robotic visual servoing/tracking system loses sight of the target, the servo fails due to loss of input. To resolve this problem a search method, namely a lost target search (LTS) which will generate efficient actions to bring the target back into the camera field of view (FoV) as soon as possible, is required. For high dimensional platforms, like a camera-mounted manipulator or an eye-in-hand system, such a search must address the difficult challenge of generating efficient actions in an online manner while avoiding kinematic constraints. In this work, we utilize the latest available information from the target just prior to leaving the FoV to initiate an optimal online search. We explain various features of our overall LTS algorithm and provide simulation comparisons with common methods existing in the literature. Finally, we implement and demonstrate the capabilities of our general algorithm on a laboratory scale 7 degree of freedom (DoF) eye-in-hand system tracking a fast moving target.

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References

  • Andreopoulos, A. & Tsotsos, J. K. (2009). A theory of active object localization. In 12th IEEE international conference on computer vision (pp. 903–910).

  • Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.

    Article  Google Scholar 

  • Aydemir, A., Pronobis, A., Moritz, G., & Jensfelt, P. (2013). Active visual object search in unknown environments using uncertain semantics. IEEE Transactions on Robotics, 29(4), 986–1002.

    Article  Google Scholar 

  • Bertuccelli, L. F., & How, J. P. (2006). Search for dynamic targets with uncertain probability maps. In American control conference (pp. 737–742).

  • Binney, J. & Sukhatme, G. S. (2012). Branch and bound for informative path planning. In IEEE international conference on robotics and automation (pp. 2147–2154).

  • Bourgault, F., Göktogan, A., Furukawa, T., & Durrant-whyte, H. F. (2004). Coordinated search for a lost target in a Bayesian world. Advanced Robotics, 18(10), 979–1000.

    Article  Google Scholar 

  • Cabanillas, J., Morales, E. F. & Sucar, L. E. (2010). An efficient strategy for fast object search considering the robot’s perceptual limitations. In Advances in artificial intelligence—IBERAMIA 2010. Lecture notes computer science (pp. 552–561).

  • Cen, G., Matsuhira, N., Hirokawa, J., Ogawa, H., & Hagiwara, I. (2009). New entropy-based adaptive particle filter for mobile robot localization. Advanced Robotics, 23(12–13), 1761–1778.

    Article  Google Scholar 

  • Chesi, G., Hashimoto, K., Prattichizzo, D. and Vicino, A. (2003). A switching control law for keeping features in the field of view in eye-in-hand visual servoing. In IEEE International conference on robotics and automation (pp. 3929–3934).

  • Chung, T. H., & Burdick, J. W. (2012). Analysis of search decision making using probabilistic search strategies. IEEE Transactions on Robotics, 28(1), 132–144.

    Article  Google Scholar 

  • Chung, C. F., & Furukawa, T. (2009). Coordinated Pursuer Control using Particle Filters for Autonomous Search-and-Capture. Rob. Auton. Syst., 57(6–7), 700–711.

    Article  Google Scholar 

  • Chung, T. H., Hollinger, Ga, & Isler, V. (2011). Search and pursuit-evasion in mobile robotics. Autonomous Robots, 31(4), 299–316.

    Article  Google Scholar 

  • Dickinson, S. J., Christensen, H. I., Tsotsos, J., & Olofsson, G. (1994). Active object recognition integrating attention and viewpoint control. In European conference on computer vision (pp. 2–14).

  • Doucet, A., de Freitas, N., & Gordon, N. (2001). Sequential Monte Carlo methods in practice. Berlin: Springer.

    Book  MATH  Google Scholar 

  • Espinoza, J., Sarmiento, A., Murrieta-Cid, R., & Hutchinson, S. (2011). Motion planning strategy for finding an object with a mobile manipulator in three-dimensional environments. Advanced Robotics, 25(13–14), 1627–1650.

    Article  Google Scholar 

  • Gerkey, B. P., Gordon, G., & Thrun, S. (2006). Visibility-based pursuit-evasion with limited field of view. The International Journal of Robotics Research, 25(4), 299–315.

    Article  Google Scholar 

  • Geyer, C. (2008). Active target search from UAVs in urban environments. In IEEE International conference on robotics and automation (pp. 2366–2371).

  • Hollinger, G., Kehagias, A., & Singh, S. (2010). GSST: Anytime Guaranteed Search. Auton. Robots, 29(1), 99–118.

    Article  Google Scholar 

  • Hollinger, G., Singh, S., Djugash, J., & Kehagias, A. (2009). Efficient multi-robot search for a moving target. The International Journal of Robotics Research, 28(2), 201–219.

    Article  Google Scholar 

  • Hollinger, G. A., & Sukhatme, G. S. (2014). Sampling-based robotic information gathering algorithms. The International Journal of Robotics Research, 33(9), 1271–1287.

    Article  Google Scholar 

  • Kobilarov, M., Marsden, J. E., & Sukhatme, G. S. (2011). Global estimation in constrained environments. The International Journal of Robotics Research, 31(1), 24–41.

    Article  Google Scholar 

  • Kolling, A., Kleiner, A., Sycara, K. & Lewis, M. (2010). Pursuit-evasion in 2.5D based on team-visibility. In IEEE/RSJ international conference on intelligent robots and systems (pp. 4610–4616).

  • Koopman, B. O. (1979). Search and its optimization. American Mathematical Monthly, 86(7), 527–540.

    Article  MATH  MathSciNet  Google Scholar 

  • Kragi, D. (2001). Visual servoing for manipulation: Robustness and integration issues. PhD Thesis, Numerical Analysis and Computer Science, Royal Institute of Technology.

  • Kragic, D., & Vincze, M. (2009). Vision for robotics. Foundations and Trends in Robotics, 1(1), 1–78.

    Article  Google Scholar 

  • Lau, H., Huang, S., & Dissanayake, G. (2005). Optimal search for multiple targets in a built environment. In IEEE/RSJ international conference on intelligent robots and systems (pp. 3740–3745).

  • Lau, H., Huang, S., & Dissanayake, G. (2006). Probabilistic search for a moving target in an indoor environment. In IEEE/RSJ international conference on intelligent robots and systems (pp. 3393–3398).

  • Lavis, B., Furukawa, T., & Durrant Whyte, H. F. (2008). Dynamic space reconfiguration for Bayesian search and tracking with moving targets. Autonomous Robots, 24(4), 387–399.

    Article  Google Scholar 

  • Lin, L., Goodrich, M. A., & Member, S. (2014). Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning. IEEE Transactions on Cybernetics, 44(12), 2532–2544.

    Article  Google Scholar 

  • Ma, J., Chung, T. H., & Burdick, J. (2011). A probabilistic framework for object search with 6-DOF pose estimation. The International Journal of Robotics Research, 30(10), 1209–1228.

    Article  Google Scholar 

  • Martinez-Cantin, R., de Freitas, N., Brochu, E., Castellanos, J., & Doucet, A. (2009). A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Autonomous Robots, 27(2), 93–103.

    Article  Google Scholar 

  • Mattingley, J., Wang, Y., & Boyd, S. (2010). Code generation for receding horizon control. In IEEE international symposium on computer-aided control system design (pp. 985–992).

  • Murrieta-Cid, R., Tovar, B., & Hutchinson, S. (2005). A sampling-based motion planning approach to maintain visibility of unpredictable targets. Autonomous Robots, 19(3), 285–300.

    Article  Google Scholar 

  • Nelson, B. J., & Khosla, P. K. (1995). Strategies for increasing the tracking region of an eye-in-hand system by singularity and joint limit avoidance. The International Journal of Robotics Research, 14(3), 255–269.

    Article  Google Scholar 

  • Niedfeldt, P., Beard, R., Morse, B., & Pledgie, S. (2010). Integrated sensor guidance using probability of object identification. In American control conference (pp. 788–793).

  • Panagou, D., & Kumar, V. (2014). Cooperative visibility maintenance for leader-follower formations in obstacle environments. IEEE Transactions on Robotics, 30(4), 831–844.

    Article  Google Scholar 

  • Radmard, S. & Croft, E. A. (2011). Approximate recursive bayesian filtering methods for robot visual search. In IEEE international conference on robotics and biomimetics (pp. 2067–2072).

  • Ramirez, J. P., Doucette, E. A., Curtis, J. W., & Gans, N. (2014). Moving target acquisition through state uncertainty minimization. In American control conference (pp. 3425–3430).

  • Ryan, A., & Hedrick, J. K. (2010). Particle filter based information-theoretic active sensing. Robotics and Autonomous Systems, 58(5), 574–584.

    Article  Google Scholar 

  • Shubina, K., & Tsotsos, J. K. (2010). Visual search for an object in a 3D environment using a mobile robot. Computer Vision and Image Understanding, 114(5), 535–547.

    Article  Google Scholar 

  • Simon, D. J. (2006). Optimal state estimation: Kalman, H infinity, and nonlinear approaches. London: Wiley.

    Book  Google Scholar 

  • Sommerlade, E. (2010). Active visual scene exploration. PhD Thesis, Department of Engineering Science, University of Oxford.

  • Stone, L. D. (1975). Theory of optimal search. London: Academic Press.

    MATH  Google Scholar 

  • Tian, X., Bar-Shalom, Y., & Pattipati, K. R. (2008). Multi-step look-ahead policy for autonomous cooperative surveillance by UAVs in hostile environments. In 47th IEEE conference on decision and control (pp. 2438–2443).

  • Tisdale, J., Kim, Z., & Hedrick, J. K. (2009). Autonomous UAV path planning and estimation. IEEE Robotics and Automation Magazine, 16(2), 35–42.

    Article  Google Scholar 

  • Torabi, L., & Gupta, K. (2012). An autonomous six-DOF eye-in-hand system for in situ 3D object modeling. The International Journal of Robotics Research, 31(1), 82–100.

    Article  Google Scholar 

  • Tsai, C. Y., Song, K. T., Dutoit, X., Van Brussel, H., & Nuttin, M. (2009). Robust visual tracking control system of a mobile robot based on a dual-Jacobian visual interaction model. Robotics and Autonomous Systems, 57(6–7), 652–664.

    Article  Google Scholar 

  • Vander Hook, J., Tokekar, P., & Isler, V. (2014). Cautious greedy strategy for bearing-only active localization: Analysis and field experiments. Journal of Field Robotics, 31(2), 296–318.

    Article  Google Scholar 

  • Webb, S., & Furukawa, T. (2008). Belief-driven manipulator visual servoing for less controlled environments. Advanced Robotics, 22(5), 547–572.

    Google Scholar 

  • Wilkes, D. & Tsotsos, J. K. (1992). Active object recognition. In IEEE conference on computer vision and pattern recognition (pp. 136–141).

  • Wong, E.-M., Bourgault, F., & Furukawa, T. (2005). Multi-vehicle Bayesian search for multiple lost targets. In IEEE international conference on robotics and automation (pp. 3169–3174).

  • Ye, Y., & Tsotsos, J. K. (1999). Sensor planning for 3D object search. Computer Vision and Image Understanding, 73(2), 145–168.

    Article  Google Scholar 

  • Ye, Y., Tsotsos, J. K., Watson, I. B. M. T. J., Heights, Y., & York, N. (2001). A complexity-level analysis of the sensor planning task for object search. Computational Intelligence, 17(4), 605–620.

    Article  MathSciNet  Google Scholar 

  • Zhang, Y., Shen, J., & Gans, N. (2014). Real-time optimization for eye-in-hand visual search. IEEE Transactions on Robotics, 30(2), 325–339.

    Article  Google Scholar 

  • Zhou, K., Member, S., & Roumeliotis, S. I. (2008). Optimal motion strategies for range-only constrained multisensor target tracking. IEEE Transactions on Robotics, 24(5), 1168–1185.

    Article  Google Scholar 

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Acknowledgments

This work was supported by NSERC/Discovery Grants Program, RGPIN 181032-12.

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Authors and Affiliations

  1. CARIS Laboratory, Department of Mechanical Engineering, University of British Columbia, Vancouver, Canada

    Sina Radmard & Elizabeth A. Croft

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  1. Sina Radmard
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  2. Elizabeth A. Croft
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Correspondence to Sina Radmard.

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Radmard, S., Croft, E.A. Active target search for high dimensional robotic systems. Auton Robot 41, 163–180 (2017). https://doi.org/10.1007/s10514-015-9539-8

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