Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots
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This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras.
KeywordsCooperative control Target detection Path planning
The authors would like to acknowledge Keith Sullivan for the Sphero computer vision tracking software and Tom Apker and Frank Lagor for discussions pertaining to the design of the control algorithm. This work was performed at the Naval Research Laboratory and was funded by the Office of Naval Research under Grant Number N0001413WX21045, “Mobile Autonomous Navy Teams for Information Surveillance and Search (MANTISS)”, and the Air Force Office of Scientific Research under DDDAS Grant No. FA95501310162. The views, positions and conclusions expressed herein reflect only the authors’ opinions and expressly do not reflect those of the Office of Naval Research, Air Force Office of Scientific Research, or the Naval Research Laboratory.
- Almurib, H., Nathan, P., & Kumar, T. (2011). Control and path planning of quadrotor aerial vehicles for search and rescue. In Proceedings of SICE Annual Conference (SICE), (pp. 700–705).Google Scholar
- Apker, T. B., & Potter, M. A. (2012). Robotic swarms as solids, liquids and gasses. In AAAI Fall Symposium Series: Human Control of Bioinspired Swarms, AAAI Technical Report FS-12-04.Google Scholar
- Blanding, W., Willett, P., & Bar-Shalom, Y. (2007) Multiple target tracking using maximum likelihood probabilistic data association. In 2007 IEEE Aerospace Conference, (pp. 1–12).Google Scholar
- Charrow, B., Liu, S., Kumar, V., & Michael, N. (2015). Information-theoretic mapping using Cauchy–Schwarz quadratic mutual information. In IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
- Darema, F. (2004). Dynamic data driven applications systems: A new paradigm for application simulations and measurements. In Computational Science-ICCS, Vol. 3038 of Lecture Notes in Computer Science, (pp. 662–669).Google Scholar
- Gayle, R., Moss, W., Lin, M. C., & Manocha, D. (2009). Multi-robot coordination using generalized social potential fields. In Proceedings of IEEE Conference on Robotics and Automation, (pp. 106–113).Google Scholar
- Huang, C. C., & Wang, S. J. (2012). A Bayesian hierarchical framework for multitarget labeling and correspondence with ghost suppression over multicamera surveillance system. IEEE Transactions on Automation Science and Engineering, 9(1), 16–30.Google Scholar
- Kim, J., & Kim, Y. (2008) Moving ground target tracking in dense obstacle areas using UAVs. In Proceedings of the 17th IFAC World Congress, (pp. 8552–8557).Google Scholar
- Kreucher, C. M., Kastella, K. D., & Hero III, A. O. (2003). Information-based sensor management for multitarget tracking. In Optical Science and Technology, SPIE’s 48th Annual Meeting, pp. (480–489).Google Scholar
- Macmillan, N. A., & Creelman, C. D. (2008). Detection theory: A user’s guide (2nd ed.). New York: Psychology Press.Google Scholar
- Nán, C. C. L. (2015). cvBlob. http://cvblob.googlecode.com.
- Peot, M. A., Altshuler, T. W., Breiholz, A., Bueker, R. A., Fertig, K. W., Hawkins, A. T., & Reddy, S. (2005). Planning sensing actions for UAVs in urban domains. Proceedings of SPIE 5986, Unmanned/Unattended Sensors and Sensor Networks II, 59860J.Google Scholar
- Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A. (2009). ROS: an open-source robot operating system. ICRA Workshop on Open Source Software 3(3.2).Google Scholar
- Rafi, F., Khan, S., Shafiq, K., & Shah, M. (2006). Autonomous target following by unmanned aerial vehicles. In Proceedings of SPIE 6230, Unmanned Systems Technology VIII, 623010.Google Scholar
- Richards, M. A. (2005). Fundamentals of radar signal processing. New Delhi: McGraw-Hill.Google Scholar
- Yang, H. C., AbouSleiman, R., Sababha, B., Gjoni, E., Korff, D., & Rawashdeh, O. (2009). Implementation of an autonomous surveillance quadrotor system. In Proceedings of AIAA Infotech, AIAA 2009–2047, (pp. 1–7).Google Scholar