Abstract
In this paper we describe a framework towards computing well-localized, robust motion plans through the perception-aware motion planning problem, whereby we seek a low-cost motion plan subject to a separate constraint on perception localization quality. To solve this problem we introduce the Multiobjective Perception-Aware Planning (MPAP) algorithm which explores the state space via a multiobjective search, considering both cost and a perception heuristic. This framework can accommodate a large range of heuristics, allowing those that capture the history dependence of localization drift and represent complex modern perception methods. We present two such heuristics, one derived from a simplified model of robot perception and a second learned from ground-truth sensor error, which we show to be capable of predicting the performance of a state-of-the-art perception system. The solution trajectory from this heuristic-based search is then certified via Monte Carlo methods to be well-localized and robust. The additional computational burden of perception-aware planning is offset by GPU massive parallelization. Through numerical experiments the algorithm is shown to find well-localized, robust solutions in about a second. Finally, we demonstrate MPAP on a quadrotor flying perception-aware and perception-agnostic plans using Google Tango for localization, finding the quadrotor safely executes the perception-aware plan every time, while crashing in over 20% of the perception-agnostic runs due to loss of localization.
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References
Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. (1998)
Kurniawati, H., Hsu, D., Lee, W.S.: SARSOP: efficient point-based POMDP planning by approximating optimally reachable belief spaces. In: Robotics: Science and Systems (2008)
Prentice, S., Roy, N.: The belief roadmap: efficient planning in linear POMDPs by factoring the covariance. Int. J. Robot. Res. (2009)
Bry, A., Roy, N.: Rapidly-exploring random belief trees for motion planning under uncertainty. In: Proceedings of the IEEE Conference on Robotics and Automation (2011)
van den Berg, J., Patil, S., Alterovitz, R.: Motion planning under uncertainty using iterative local optimization in belief space. Int. J. Robot. Res. (2012)
Patil, S., Kahn, G., Laskeym, M., Schulman, J., Goldberg, K., Abbeel, P.: Scaling up gaussian belief space planning through covariance-free trajectory optimization and automatic differentiation. In: Workshop on Algorithmic Foundations of Robotics (2014)
Indelman, V., Carlone, L., Dellaert, F.: Planning in the continuous domain: a generalized belief space approach for autonomous navigation in unknown environments. Int. J. Robot. Res. (2015)
van den Berg, J., Abbeel, P., Goldberg, K.: LQG-MP: optimized path planning for robots with motion uncertainty and imperfect state information. Int. J. Robot. Res. (2011)
Agha-mohammadi, A., Agarwal, S., Chakravorty, S., Amato, N.M.: Simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space. IEEE Trans. Robot. (2016). arXiv:1510.07380 (submitted)
Platt, R., Tedrake, R., Kaelbling, L., Lozano-Perez, T.: Belief space planning assuming maximum likelihood observations. In: Robotics: Science and Systems (2010)
Aloimonos, J., Weiss, I., Brandyopadhyay, A.: Active vision. Int. J. Comput. Vis. (1988)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.: Past, present, and future of simultaneous localization and mapping: towards the robust-perception age. IEEE Trans. Robot. (2016)
Sadat, S.A., Chutskoff, K., Jungic, D., Wawerla, J., Vaughan, R.: Feature-rich path planning for robust navigation of MAVs with Mono-SLAM. In: Proceedings of the IEEE Conference on Robotics and Automation (2014)
Costante, G., Forster, C., Delmerico, J., Valigi, P., Scaramuzza, D.: Perception-aware path planning. IEEE Trans. Robot. (2017). arXiv:1605.04151 (submitted)
Carlone, L., Lyons, D.: Uncertainty-constrained robot exploration: a mixed-integer linear programming approach. In: Proceedings of the IEEE Conference on Robotics and Automation (2014)
Ichter, B., Schmerling, E., Agha-mohammadi, A., Pavone, M.: Real-time stochastic kinodynamic motion planning via multiobjective search on GPUs. In: Proceedings of the IEEE Conference on Robotics and Automation (2017)
Kavraki, L.E., Å vestka, P., Latombe, J.-C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional spaces. IEEE Trans. Robot. Autom. (1996)
Amato, N.M., Dale, L.K.: Probabilistic roadmap methods are embarrassingly parallel. In: Proceedings of the IEEE Conference on Robotics and Automation (1999)
Janson, L., Schmerling, E., Pavone, M.: Monte Carlo motion planning for robot trajectory optimization under uncertainty. In: International Symposium on Robotics Research (2015)
Davison, A.J., Murray, D.W.: Simultaneous localization and map-building using active vision. IEEE Trans. Pattern Anal. Mach. Intell. (2002)
Scaramuzza, D., Fraundorfer, F.: Visual odometry part I: the first 30 years and fundamentals. IEEE Robot. Autom. Mag. (2011)
LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006)
Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3D semantic parsing of large-scale indoor spaces. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
PX4 Development Team: PX4 autopilot. http://px4.io/
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Ichter, B., Landry, B., Schmerling, E., Pavone, M. (2020). Perception-Aware Motion Planning via Multiobjective Search on GPUs. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_61
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DOI: https://doi.org/10.1007/978-3-030-28619-4_61
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