Optimal Perception Planning with Informed Heuristics Constructed from Visibility Maps
- 7 Downloads
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution when considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to reduce the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic with improvements dominates the base PA* heuristic built on the euclidean distance, and then present the results of the performance increase in terms of node expansion and computation time.
KeywordsPerception planning Heuristic search Visibility maps
Unable to display preview. Download preview PDF.
This work is financed by the ERDF - European Regional Development Fund through the Operational Program for Competitiveness and Internationalization - COMPETE 2020 Program within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013, and also by the FCT grant SFRH/ BD/52158/2013 through the CMU-Portugal program.
- 2.Carlone, L., Ng, M., Du, J., Bona, B., Indri, M.: Rao-Blackwellized particle filters multi robot slam with unknown initial correspondences and limited communication. In: IEEE International Conference on Robotics and Automation (ICRA) (2010)Google Scholar
- 3.Eidenberger, R., Scharinger, J.: Active perception and scene modeling by planning with probabilistic 6D object poses. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010)Google Scholar
- 4.Ekvall, S., Jensfelt, P., Kragic, D.: Integrating active mobile robot object recognition and slam in natural environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2006)Google Scholar
- 5.Fabrizi, E., Saffiotti, A.: Extracting topology-based maps from Gridmaps. In: IEEE International Conference on Robotics and Automation, 2000. Proceedings. ICRA’00. IEEE, vol 3, pp. 2972–2978 (2000)Google Scholar
- 7.Gancet, J., Lacroix, S.: Pg2p: A perception-guided path planning approach for long range autonomous navigation in unknown natural environments. In: 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings (2003)Google Scholar
- 9.Kaelbling, L.P., Lozano-Pérez, T.: Unifying perception, estimation and action for mobile manipulation via belief space planning. In: 2012 IEEE International Conference on Robotics and Automation (ICRA) (2012)Google Scholar
- 11.Lu, D.V., Smart, W.D.: Towards more efficient navigation for robots and humans. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1707–1713. IEEE, Cambridge (2013)Google Scholar
- 12.Pandey, A.K., Alami, R.: Mightability maps: a perceptual level decisional framework for co-operative and competitive human-robot interaction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010)Google Scholar
- 13.Pereira, T., Veloso, M., Moreira, A.: Multi-robot planning using robot-dependent reachability maps. In: Robot 2015: Second Iberian Robotics Conference (2015)Google Scholar
- 14.Pereira, T., Moreira, A.P., Veloso, M.: Improving heuristics of optimal perception planning using visibility maps. In: The IEEE International Conference on Autonomous Robots Systems and Competitions, ICARSC’16 (2016)Google Scholar
- 15.Pereira, T., Veloso, M., Moreira, A.P.: PA*: Optimal path planning for perception tasks. In: The European Conference on Artificial Intelligence, ECAI’16 (2016)Google Scholar
- 16.Pereira, T., Veloso, M., Moreira, A.P.: Visibility maps for any-shape robots. In: The IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS’16 (2016)Google Scholar
- 17.Pereira, T., Moreira, A.P., Veloso, M.: Multi-robot planning for perception of multiple regions of interest. In: The third Iberian Robotics Conference, accepted at ROBOT’2017 (2017)Google Scholar
- 19.Rusu, R.B., Şucan, I.A., Gerkey, B., Chitta, S., Beetz, M., Kavraki, L.E.: Real-time perception-guided motion planning for a personal robot, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2009)Google Scholar
- 20.Velez, J., Hemann, G., Huang, A., Posner, I., Roy, N.: Planning to perceive: Exploiting mobility for robust object detection. Icaps pp. 266–273 (2011)Google Scholar