Decentralized multi-robot belief space planning in unknown environments via identification and efficient re-evaluation of impacted paths
- 466 Downloads
In this paper we develop a new approach for decentralized multi-robot belief space planning in high-dimensional state spaces while operating in unknown environments. State of the art approaches often address related problems within a sampling based motion planning paradigm, where robots generate candidate paths and are to choose the best paths according to a given objective function. As exhaustive evaluation of all candidate path combinations from different robots is computationally intractable, a commonly used (sub-optimal) framework is for each robot, at each time epoch, to evaluate its own candidate paths while only considering the best paths announced by other robots. Yet, even this approach can become computationally expensive, especially for high-dimensional state spaces and for numerous candidate paths that need to be evaluated. In particular, upon an update in the announced path from one of the robots, state of the art approaches re-evaluate belief evolution for all candidate paths and do so from scratch. In this work we develop a framework to identify and efficiently update only those paths that are actually impacted as a result of an update in the announced path. Our approach is based on appropriately propagating belief evolution along impacted paths while employing insights from factor graph and incremental smoothing for efficient inference that is required for evaluating the utility of each impacted path. We demonstrate our approach in a synthetic simulation.
KeywordsMulti-robot planning Belief space planning Multi-robot SLAM Active collaborative perception
This work was partially supported by the Technion Autonomous Systems Program (TASP) and by the Ministry of Science & Technology, Israel & the Russian Foundation for Basic Research, the Russian Federation.
- Agha-mohammadi, A. A., Agarwal, S., Chakravorty, S., & Amato, N. M. (2015). Simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space. arXiv:1510.07380.
- Agha-Mohammadi, A. A., Agarwal, S., Mahadevan, A., Chakravorty, S., Tomkins, D., Denny, J., et al. (2014). Robust online belief space planning in changing environments: Application to physical mobile robots. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 149–156).Google Scholar
- Atanasov, N., Le Ny, J., Daniilidis, K., Pappas, G. J. (2015). Decentralized active information acquisition: Theory and application to multi-robot slam. In IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
- Bry, A., Roy, N. (2011). Rapidly-exploring random belief trees for motion planning under uncertainty. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 723–730).Google Scholar
- Chaves, S. M., Kim, A., Eustice, R. M. (2014). Opportunistic sampling-based planning for active visual slam. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3073–3080). IEEE.Google Scholar
- Cunningham, A., Indelman, V., Dellaert, F. (2013). DDF-SAM 2.0: Consistent distributed smoothing and mapping. In IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.Google Scholar
- Dellaert, F. (2012). Factor graphs and GTSAM: A hands-on introduction. Technical Report GT-RIM-CP&R-2012-002, Georgia Institute of Technology.Google Scholar
- Farhi, E. I., Indelman, V. (2017) Towards efficient inference update through planning via jip - joint inference and belief space planning. In IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
- Indelman, V. (2015a). Towards cooperative multi-robot belief space planning in unknown environments. In Proceedings of the International Symposium of Robotics Research (ISRR).Google Scholar
- Indelman, V. (2015b) Towards multi-robot active collaborative state estimation via belief space planning. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
- Indelman, V. (2017). Cooperative multi-robot belief space planning for autonomous navigation in unknown environments. Autonomous Robots. doi: 10.1007/s10514-017-9620-6.
- Kopitkov, D., Indelman, V. (2016). Computationally efficient decision making under uncertainty in high-dimensional state spaces. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
- Kurniawati, H., Hsu, D., Lee, W. S. (2008). Sarsop: Efficient point-based pomdp planning by approximating optimally reachable belief spaces. In Robotics: Science and Systems (RSS) (vol. 2008).Google Scholar
- Pathak, S., Thomas, A., Feniger, A., & Indelman, V. (2016). Robust active perception via data-association aware belief space planning. arXiv:1606.05124.
- Platt, R., Tedrake, R., Kaelbling, L.P., & Lozano-Pérez, T. (2010). Belief space planning assuming maximum likelihood observations. In Robotics: Science and Systems (RSS) (pp. 587–593). Zaragoza, Spain.Google Scholar
- Regev, T., & Indelman, V. (2016). Multi-robot decentralized belief space planning in unknown environments via efficient re-evaluation of impacted paths. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
- Stachniss, C., Grisetti, G., & Burgard, W. (2005). Information gain-based exploration using rao-blackwellized particle filters. In Robotics: Science and Systems (RSS) (pp. 65–72).Google Scholar