Tokekar-Research.pdf

Transcript

1. Algorithms for Autonomous Near Earth Sensing with Robot Teams Pratap Tokekar Assistant Professor Computer Science University of Maryland http://tokekar.com/ 1
2. Data-Driven Science Facebook, Twitter, Wikipedia, Cellphones, etc. Ecology, Agriculture, Climate Science, etc. Source: wikipedia.org Source: wikipedia.org ‣ Data for understanding complex phenomena ‣ Emergence of new data analytics and management techniques ‣ Where does the data come from? 2
3. The 3D Problem of Data Collection ‣ Dull, dirty, and dangerous ‣ Need high resolution data collected frequently over long periods of time 3 Source: USGS.gov Source: spanengineering.com
4. Future of Infrastructure Inspection
5. 3D Reconstruction using Onboard Images Future of Infrastructure Inspection George P. Coleman Memorial Bridge Flights (April 2018)
6. Autonomous flight at VTTI smart road bridge 175ft tall, 2000ft long, 5 span concrete box girder [Shanthakumar et al., ISER ‘18]
7. RAAS Lab Research 7 ‣ How should a team of heterogeneous robots plan their paths to gather interesting data efficiently? SYSTEMS & APPLICATIONS ENVIRONMENTAL MONITORING PRECISION AGRICULTURE INFRASTRUCTURE INSPECTION ALGORITHMS
8. Plan efficient paths for robots to… 1. find interesting regions in the environment 2. learn an accurate model of the environment 3. find the hotspot in the environment 8
9. X: measurement locations Y: measurements X*: test locations K: kernel function Gaussian Process GP is a collection of of random variables, any finite subset of which have a joint Gaussian distribution. Non-linear, non-parametric Bayesian regression ⌃⇤ = K(X⇤, X⇤) K(X⇤, X) ⇥ K(X, X) + 2 n I ⇤ 1 K(X, X⇤) <latexit sha1_base64="R5h8Ip/Tik/mm7GCeAICjaXiOLI=">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</latexit> µ⇤ = K(X⇤, X) ⇥ K(X, X) + 2 n I ⇤ 1 Y <latexit sha1_base64="x+e3mbouvkHqDZnWnI94mVhB5/0=">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</latexit>
10. Temperature prediction using stationary sensors [Krause et al. ’07]
11. Temperature prediction using stationary sensors [Krause et al. ’07] Probability of occupancy for mapping [O’Callaghan et al. ’12]
12. Temperature prediction using stationary sensors [Krause et al. ’07] Probability of occupancy for mapping [O’Callaghan et al. ’12] Chlorophyll density mapping with USVs [Manjanna et al. ’18]
13. Precision Agriculture 13 [Tokekar et al., T-RO 2016, Das et al., CASE 2015, Dhami et al., DGRS 2019]
14. Plan efficient paths for robots to… 1. find interesting regions in the environment 2. learn an accurate model of the environment 3. find the hotspot in the environment 14
15. Environment Classification Use GP regression to estimate a dense prior map from sparse prior measurements
16. Environment Classification Classify points into high, medium, low categories Use GP regression to estimate a dense prior map from sparse prior measurements
17. Environment Classification Compute probability of misclassification for each point Classify points into high, medium, low categories Use GP regression to estimate a dense prior map from sparse prior measurements
18. Environment Classification Obtain additional measurements at points with high probability of misclassification Compute probability of misclassification for each point Classify points into high, medium, low categories Use GP regression to estimate a dense prior map from sparse prior measurements
19. Traveling Salesperson Problem (TSP) ‣ Given a graph, find the minimum cost tour that visits each vertex exactly once. https://www.ibm.com/developerworks/community/blogs/jfp/entry/no_the_tsp_isn_t_np_complete?lang=en optimal solution for a tour of all 13,509 cities and towns in the US that have more than 500 residents.
20. Orienteering: dual of TSP ‣ UAV has limited battery life ‣ May not be able to visit all points. Visit most number of points. ‣ Orienteering: find a tour visiting most number of vertices, subject to a time budget
21. ‣ UAV can land on UGV. UGV carries UAV between deployment locations. How to plan such paths?
22. Air + Ground Symbiotic Planning ‣ We show to how to model the UAV+UGV system as an orienteering instance on a metric graph ‣ Guaranteed to cover at least 25% of the points as optimal
23. UGV as a Mobile Recharging Station [Yu et al. ICRA ’18, JFR ‘19]
24. Coverage with UAV+UGV 24 [Yu, O’Kane, and Tokekar ICRA ’19]
25. Plan efficient paths for robots to… 1. find interesting regions in the environment 2. learn an accurate model of the environment 3. find the hotspot in the environment 25
26. Learning a Spatial Field ‣ Harder problem: accurately predict the function value at each point ‣ Naïve solution: take infinitely many measurements at every single point ‣ Adaptive algorithms: mutual information, entropy Result: a non-adaptive algorithm yields a constant-factor approximation [Suryan and Tokekar, WAFR ’18, T-RO under review]
27. Contributions ‣ Goal: Ensure that the mean square error for every point in the environment is below a desired threshold ‣ assuming SE kernel with known hyperparameters ‣ Minimize total time = travel time + measurement time ‣ Constant-factor approximation for: ‣ Stationary sensor placement ‣ Single robot ‣ Multiple robots
28. Necessary condition No point can be more than "#$ away from its nearest measurement location 28 "#$ = −log 1 − Δ . / threshold : Length scale which controls the smoothness . /: Signal variance which controls the range
29. Sufficient condition measurements inside a smaller disk / suffice to satisfy threshold within that disk 29 1 "#$ = −log 1 + / . / 1 − Δ . / 8 ≥ / . / 1 1 − Δ . / : 8;<: − 1
30. ‣ Cover the environment with smaller disks ‣ Find a path that visits each disk in the least amount of time ‣ TSP with disk neighborhoods
31. Comparisons with Baseline • In practice, comparable to entropy and MI strategies • In theory, we give guarantees on mean square error and path length whereas baselines do not 31
32. Plan efficient paths for robots to… 1. find interesting regions in the environment 2. learn an accurate model of the environment 3. find the hotspot in the environment 32
33. Monitoring Marine Environments NRI: Coordinated Detection and Tracking of Hazardous Agents with Aerial and Aquatic Robots to Inform Emergency Responders [Sung and Tokekar, ICRA ’19; Sung, Dixit, Tokekar ICRA ‘20, T-RO under review]
34. Hotspot Identification ‣ Higher altitude à more noise ‣ We don’t want to learn the entire field; only the point of maxima ‣ Exploration vs. Exploitation ‣ Given limited budget for the UAV, where to take measurements from?
35. New 3D GP Multi-Armed Bandit ‣ 3D arm locations with varying sensor noise ‣ Standard algorithms are for 2D à do not take varying sensor quality into account ‣ Present a new UCB rule ‣ ~10% better performance over baseline [Sung et al., arXiv:1909.08483]
38. Bridging Algorithmic and Field Robotics We want algorithms that ‣ are resilient --- can withstand catastrophic failures or adversarial attacks ‣ are reliable --- work consistently well even in the presence of uncertainty
39. Resiliency via Teaming ‣ Multi-robot data gathering ‣ Coordination leads to better performance – submodular objectives ‣ Can we guarantee good performance when some robots are under attack and/or experience malfunction?
40. Submodular Maximization ‣ Well-used framework applied to a variety of domains ‣ Sensor placement, active perception, information-theoretic planning, task assignment, welfare maximization, portfolio optimization, … ‣ NP-complete ‣ Greedy algorithm yields [Nemhauser ‘78] ‣ (1-1/e) approximation under uniform matroid ‣ ½ approximation under partition matroid 40
41. 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42. Maximization with Worst-Case Attack 42 ‣ robots, attacks ‣ We pick a strategy for each of the N robots ‣ Adversary attacks a subset A of our N robots ‣ But we don’t know which A robots max S✓X min A✓S f(S \ A) S 2 I; |A| = ↵  N <latexit sha1_base64="uhNPlL3wR6fCbQbgdUNR3zTcvVc=">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</latexit> <latexit 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sha1_base64="1geheZ8OEVc4P/xyjJq8gn3vuMI=">AAAB6HicbZBNS8NAEIYn9avWr6pHL4tF8FQSEfRY9OJJWrAf0Iay2U7atZtN2N0IJfQXePGgiFd/kjf/jds2B219YeHhnRl25g0SwbVx3W+nsLa+sblV3C7t7O7tH5QPj1o6ThXDJotFrDoB1Si4xKbhRmAnUUijQGA7GN/O6u0nVJrH8sFMEvQjOpQ85IwaazXu++WKW3XnIqvg5VCBXPV++as3iFkaoTRMUK27npsYP6PKcCZwWuqlGhPKxnSIXYuSRqj9bL7olJxZZ0DCWNknDZm7vycyGmk9iQLbGVEz0su1mflfrZua8NrPuExSg5ItPgpTQUxMZleTAVfIjJhYoExxuythI6ooMzabkg3BWz55FVoXVc9y47JSu8njKMIJnMI5eHAFNbiDOjSBAcIzvMKb8+i8OO/Ox6K14OQzx/BHzucPppOM0g==</latexit> ↵ <latexit sha1_base64="6EtSdgpKG2qhZbjGZ4j1QLtQzns=">AAAB7XicbZBNSwMxEIZn61etX1WPXoJF8FR2RdBj0YvHCvYD2qXMpmkbm02WJCuUpf/BiwdFvPp/vPlvTNs9aOsLgYd3ZsjMGyWCG+v7315hbX1jc6u4XdrZ3ds/KB8eNY1KNWUNqoTS7QgNE1yyhuVWsHaiGcaRYK1ofDurt56YNlzJBztJWBjjUPIBp2id1eyiSEbYK1f8qj8XWYUghwrkqvfKX92+omnMpKUCjekEfmLDDLXlVLBpqZsaliAd45B1HEqMmQmz+bZTcuacPhko7Z60ZO7+nsgwNmYSR64zRjsyy7WZ+V+tk9rBdZhxmaSWSbr4aJAKYhWZnU76XDNqxcQBUs3droSOUCO1LqCSCyFYPnkVmhfVwPH9ZaV2k8dRhBM4hXMI4ApqcAd1aACFR3iGV3jzlPfivXsfi9aCl88cwx95nz+Lg48Y</latexit> <latexit sha1_base64="6EtSdgpKG2qhZbjGZ4j1QLtQzns=">AAAB7XicbZBNSwMxEIZn61etX1WPXoJF8FR2RdBj0YvHCvYD2qXMpmkbm02WJCuUpf/BiwdFvPp/vPlvTNs9aOsLgYd3ZsjMGyWCG+v7315hbX1jc6u4XdrZ3ds/KB8eNY1KNWUNqoTS7QgNE1yyhuVWsHaiGcaRYK1ofDurt56YNlzJBztJWBjjUPIBp2id1eyiSEbYK1f8qj8XWYUghwrkqvfKX92+omnMpKUCjekEfmLDDLXlVLBpqZsaliAd45B1HEqMmQmz+bZTcuacPhko7Z60ZO7+nsgwNmYSR64zRjsyy7WZ+V+tk9rBdZhxmaSWSbr4aJAKYhWZnU76XDNqxcQBUs3droSOUCO1LqCSCyFYPnkVmhfVwPH9ZaV2k8dRhBM4hXMI4ApqcAd1aACFR3iGV3jzlPfivXsfi9aCl88cwx95nz+Lg48Y</latexit> <latexit sha1_base64="6EtSdgpKG2qhZbjGZ4j1QLtQzns=">AAAB7XicbZBNSwMxEIZn61etX1WPXoJF8FR2RdBj0YvHCvYD2qXMpmkbm02WJCuUpf/BiwdFvPp/vPlvTNs9aOsLgYd3ZsjMGyWCG+v7315hbX1jc6u4XdrZ3ds/KB8eNY1KNWUNqoTS7QgNE1yyhuVWsHaiGcaRYK1ofDurt56YNlzJBztJWBjjUPIBp2id1eyiSEbYK1f8qj8XWYUghwrkqvfKX92+omnMpKUCjekEfmLDDLXlVLBpqZsaliAd45B1HEqMmQmz+bZTcuacPhko7Z60ZO7+nsgwNmYSR64zRjsyy7WZ+V+tk9rBdZhxmaSWSbr4aJAKYhWZnU76XDNqxcQBUs3droSOUCO1LqCSCyFYPnkVmhfVwPH9ZaV2k8dRhBM4hXMI4ApqcAd1aACFR3iGV3jzlPfivXsfi9aCl88cwx95nz+Lg48Y</latexit> <latexit sha1_base64="6EtSdgpKG2qhZbjGZ4j1QLtQzns=">AAAB7XicbZBNSwMxEIZn61etX1WPXoJF8FR2RdBj0YvHCvYD2qXMpmkbm02WJCuUpf/BiwdFvPp/vPlvTNs9aOsLgYd3ZsjMGyWCG+v7315hbX1jc6u4XdrZ3ds/KB8eNY1KNWUNqoTS7QgNE1yyhuVWsHaiGcaRYK1ofDurt56YNlzJBztJWBjjUPIBp2id1eyiSEbYK1f8qj8XWYUghwrkqvfKX92+omnMpKUCjekEfmLDDLXlVLBpqZsaliAd45B1HEqMmQmz+bZTcuacPhko7Z60ZO7+nsgwNmYSR64zRjsyy7WZ+V+tk9rBdZhxmaSWSbr4aJAKYhWZnU76XDNqxcQBUs3droSOUCO1LqCSCyFYPnkVmhfVwPH9ZaV2k8dRhBM4hXMI4ApqcAd1aACFR3iGV3jzlPfivXsfi9aCl88cwx95nz+Lg48Y</latexit> s.t. |A| = ↵  N <latexit sha1_base64="to9sV7otDo89O/1Z9DQMIWeOe2E=">AAACDXicbZC7SgNBFIZn4y3GW9TSZjAKVmFXBG2EqI2VRDAXyIZwdjJJhszOrjOzQtgkD2Djq9hYKGJrb+fbOJtsoYk/DHz85xzmnN8LOVPatr+tzMLi0vJKdjW3tr6xuZXf3qmqIJKEVkjAA1n3QFHOBK1opjmth5KC73Fa8/pXSb32QKVigbjTg5A2fegK1mEEtLFa+QNV1EU8Hg9dH3SPAI8vRkN8jl3gYQ+wy+k9vmnlC3bRngjPg5NCAaUqt/JfbjsgkU+FJhyUajh2qJsxSM0Ip6OcGykaAulDlzYMCvCpasaTa0b40Dht3AmkeULjift7IgZfqYHvmc5kZzVbS8z/ao1Id86aMRNhpKkg0486Ecc6wEk0uM0kJZoPDACRzOyKSQ8kEG0CzJkQnNmT56F6XHQM354USpdpHFm0h/bREXLQKSqha1RGFUTQI3pGr+jNerJerHfrY9qasdKZXfRH1ucPDqWa5A==</latexit> <latexit 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43. Contributions 43 ‣ We present a Resilient Greedy algorithm ‣ Yields a constant-factor approximation and runs in O(N2D2) time f(S \ A?(S)) f? 1 2 max  1 kf , 1 (1 + ↵) , 1 (N ↵) <latexit sha1_base64="AnGFAg9ZWO0EWnRVAnStA00bbtM=">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</latexit> <latexit sha1_base64="AnGFAg9ZWO0EWnRVAnStA00bbtM=">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</latexit> <latexit sha1_base64="AnGFAg9ZWO0EWnRVAnStA00bbtM=">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</latexit> <latexit sha1_base64="AnGFAg9ZWO0EWnRVAnStA00bbtM=">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</latexit> ‣ If there are no attacks, same performance as regular greedy curvature kf 2 [0, 1] <latexit sha1_base64="vhMTxvJt6vTjPgnwRDXhD++dqe8=">AAAB83icbZBNS8NAEIYn9avWr6pHL4tF8CAlEcEeC148VrAf0ISy2W7apZtN2J0IJfRvePGgiFf/jDf/jds2B219YeHhnRlm9g1TKQy67rdT2tjc2t4p71b29g8Oj6rHJx2TZJrxNktkonshNVwKxdsoUPJeqjmNQ8m74eRuXu8+cW1Eoh5xmvIgpiMlIsEoWsufDCJfqL57RbxgUK25dXchsg5eATUo1BpUv/xhwrKYK2SSGtP33BSDnGoUTPJZxc8MTymb0BHvW1Q05ibIFzfPyIV1hiRKtH0KycL9PZHT2JhpHNrOmOLYrNbm5n+1foZRI8iFSjPkii0XRZkkmJB5AGQoNGcopxYo08LeStiYasrQxlSxIXirX16HznXds/xwU2s2ijjKcAbncAke3EIT7qEFbWCQwjO8wpuTOS/Ou/OxbC05xcwp/JHz+QOW0JCw</latexit> <latexit sha1_base64="vhMTxvJt6vTjPgnwRDXhD++dqe8=">AAAB83icbZBNS8NAEIYn9avWr6pHL4tF8CAlEcEeC148VrAf0ISy2W7apZtN2J0IJfRvePGgiFf/jDf/jds2B219YeHhnRlm9g1TKQy67rdT2tjc2t4p71b29g8Oj6rHJx2TZJrxNktkonshNVwKxdsoUPJeqjmNQ8m74eRuXu8+cW1Eoh5xmvIgpiMlIsEoWsufDCJfqL57RbxgUK25dXchsg5eATUo1BpUv/xhwrKYK2SSGtP33BSDnGoUTPJZxc8MTymb0BHvW1Q05ibIFzfPyIV1hiRKtH0KycL9PZHT2JhpHNrOmOLYrNbm5n+1foZRI8iFSjPkii0XRZkkmJB5AGQoNGcopxYo08LeStiYasrQxlSxIXirX16HznXds/xwU2s2ijjKcAbncAke3EIT7qEFbWCQwjO8wpuTOS/Ou/OxbC05xcwp/JHz+QOW0JCw</latexit> <latexit sha1_base64="vhMTxvJt6vTjPgnwRDXhD++dqe8=">AAAB83icbZBNS8NAEIYn9avWr6pHL4tF8CAlEcEeC148VrAf0ISy2W7apZtN2J0IJfRvePGgiFf/jDf/jds2B219YeHhnRlm9g1TKQy67rdT2tjc2t4p71b29g8Oj6rHJx2TZJrxNktkonshNVwKxdsoUPJeqjmNQ8m74eRuXu8+cW1Eoh5xmvIgpiMlIsEoWsufDCJfqL57RbxgUK25dXchsg5eATUo1BpUv/xhwrKYK2SSGtP33BSDnGoUTPJZxc8MTymb0BHvW1Q05ibIFzfPyIV1hiRKtH0KycL9PZHT2JhpHNrOmOLYrNbm5n+1foZRI8iFSjPkii0XRZkkmJB5AGQoNGcopxYo08LeStiYasrQxlSxIXirX16HznXds/xwU2s2ijjKcAbncAke3EIT7qEFbWCQwjO8wpuTOS/Ou/OxbC05xcwp/JHz+QOW0JCw</latexit> <latexit sha1_base64="vhMTxvJt6vTjPgnwRDXhD++dqe8=">AAAB83icbZBNS8NAEIYn9avWr6pHL4tF8CAlEcEeC148VrAf0ISy2W7apZtN2J0IJfRvePGgiFf/jDf/jds2B219YeHhnRlm9g1TKQy67rdT2tjc2t4p71b29g8Oj6rHJx2TZJrxNktkonshNVwKxdsoUPJeqjmNQ8m74eRuXu8+cW1Eoh5xmvIgpiMlIsEoWsufDCJfqL57RbxgUK25dXchsg5eATUo1BpUv/xhwrKYK2SSGtP33BSDnGoUTPJZxc8MTymb0BHvW1Q05ibIFzfPyIV1hiRKtH0KycL9PZHT2JhpHNrOmOLYrNbm5n+1foZRI8iFSjPkii0XRZkkmJB5AGQoNGcopxYo08LeStiYasrQxlSxIXirX16HznXds/xwU2s2ijjKcAbncAke3EIT7qEFbWCQwjO8wpuTOS/Ou/OxbC05xcwp/JHz+QOW0JCw</latexit> [Zhou, Tzoumas, Pappas, and Tokekar. ICRA ’19, RAL ‘19]
44. Bridging Algorithmic and Field Robotics We want algorithms that ‣ are resilient --- can withstand adversarial attacks or catastrophic failures ‣ are reliable --- work consistently well even in the presence of uncertainty
45. Bayesian Semantic Segmentation ‣ Semantic segmentation with deep neural networks ‣ Extract uncertainty from NNs à Bayesian approximation ‣ Use uncertainty for planning A. Kendall, V. Badrinarayanan, and R. Cipoll, “Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding,” arXiv preprint arXiv:1511.02680, 2015.
46. DL model segmentation DL model segmentation with uncertainty Handcrafted ground truth segmentation start goal start goal start goal [Toubeh and Tokekar, AAAI FSS ’18, arXiv:1910.00101]
47. 47 ‣ 40+ faculty members ‣ Masters of Engineering in Robotics ~ 180 students ‣ New robotics minor for undergrads ‣ Post-doctoral fellowships ‣ Fearless Flight Facility – 300 x 100 x 50 feet netted area
48. Thank you! tokekar@umd.edu http://tokekar.com/ 48