Autonomous Target Search with Multiple Coordinated UAVs

Piacentini, Chiara; Bernardini, Sara; Beck, J. Christopher

doi:10.1613/jair.1.11635

Cited by 14 publications

(14 citation statements)

References 69 publications

(87 reference statements)

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“…The task of search-and-tracking [6,34] is to use a UAV to detect a moving target by executing a sequence of candidate flight search patterns. In particular, it is to select a subset from the set V of all candidate patterns and perform the selected patterns in some specific order, such that the detection time is minimized.…”

Section: Search-and-trackingmentioning

confidence: 99%

Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences

Qian

Liu

Feng

et al. 2021

Preprint

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Evolutionary algorithms (EAs) are general-purpose optimization algorithms, inspired by natural evolution. Recent theoretical studies have shown that EAs can achieve good approximation guarantees for solving the problem classes of submodular optimization, which have a wide range of applications, such as maximum coverage, sparse regression, influence maximization, document summarization and sensor placement, just to name a few. Though they have provided some theoretical explanation for the general-purpose nature of EAs, the considered submodular objective functions are defined only over sets or multisets. To complement this line of research, this paper studies the problem class of maximizing monotone submodular functions over sequences, where the objective function depends on the order of items. We prove that for each kind of previously studied monotone submodular objective functions over sequences, i.e., prefix monotone submodular functions, weakly monotone and strongly submodular functions, and DAG monotone submodular functions, a simple multi-objective EA, i.e., GSEMO, can always reach or improve the best known approximation guarantee after running polynomial time in expectation. Note that these best-known approximation guarantees can be obtained only by different greedy-style algorithms before. Empirical studies on various applications, e.g., accomplishing tasks, maximizing information gain, searchand-tracking and recommender systems, show the excellent performance of the GSEMO.

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Section: Search-and-trackingmentioning

confidence: 99%

Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences

Qian

Liu

Feng

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…3) Application to a multi-robot multi-target tracking problem: Finally, we apply the analysis to develop a planner 5 for multi-robot multi-target tracking with a mutual information objective and demonstrate that a distributed RSP planner running in constant time can guarantee suboptimality approaching that of fully sequential planning. Then, additional simulation results confirm that RSP maintains consistent solution quality for up to 96 robots.…”

Section: A Contributionsmentioning

confidence: 99%

“…mcorah@jpl.nasa.gov, nmichael@cmu.edu sensing utility [3,4]. Likewise, realistic environments induce complexity by constraining target motion such as for search on a road network [5] or in an indoor office space [6]. Additionally, deep learning methods for visual object tracking enable systems to track wide varieties of objects, and these objects may have similarly varied states and dynamics [7].…”

Section: Introductionmentioning

confidence: 99%

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Scalable Distributed Planning for Multi-Robot, Multi-Target Tracking

Corah¹,

Michael²

2021

Preprint

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In multi-robot multi-target tracking, robots coordinate to monitor groups of targets moving about an environment. We approach planning for such scenarios by formulating a receding-horizon, multi-robot sensing problem with a mutual information objective. Such problems are NP-Hard in general. Yet, our objective is submodular which enables certain greedy planners to guarantee constant-factor suboptimality. However, these greedy planners require robots to plan their actions in sequence, one robot at a time, so planning time is at least proportional to the number of robots. Solving these problems becomes intractable for large teams, even for distributed implementations. Our prior work proposed a distributed planner (RSP) which reduces this number of sequential steps to a constant, even for large numbers of robots, by allowing robots to plan in parallel while ignoring some of each others' decisions. Although that analysis is not applicable to target tracking, we prove a similar guarantee, that RSP planning approaches performance guarantees for fully sequential planners, by employing a novel bound which takes advantage of the independence of target motions to quantify effective redundancy between robots' observations and actions. Further, we present analysis that explicitly accounts for features of practical implementations including approximations to the objective and anytime planning. Simulation results-available via open source release-for target tracking with ranging sensors demonstrate that our planners consistently approach the performance of sequential planning (in terms of position uncertainty) given only 2-8 planning steps and for as many as 96 robots with a 24x reduction in the number of sequential steps in planning. Thus, this work makes planning for multi-robot target tracking tractable at much larger scales than before, for practical planners and general tracking problems.

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“…Even simple target tracking problems, such as with noisy range sensors, produce multimodal posterior distributions that do not have closed-form solutions; planning and tracking systems may then approximate both the posterior and sensing utility [34,35]. Realistic environments can also induce complex motion models which arise in search on road networks [145] and in indoor environments [89]. This motivates selection of sufficiently general objectives and path planners to capture the degree of complexity in these problems.…”

Section: Introduction For Target Trackingmentioning

confidence: 99%

Sensor Planning for Large Numbers of Robots

Corah

2021

Preprint

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For my parents, David and Barbara, who supported and encouraged my interest in robotics at every step of this journey. I also have many to thank in the Resilient Intelligent Systems Lab. My officemates, Ellen and Derek were ever-present companions. Ellen, I believe our fish tank set new standards for office decor as well as undergraduate achievement. Derek, aside from being my office compatriot, you were also an early mentor when I was still an intern. Thank you for that time. And, Vibhav, you were my replacement, but I preferred being students together. Kshitij and Cormac, we collaborated on some of the best work that is not in this thesis. Thank you Curt for keeping my robots alive before I learned to avoid them. And, thanks Wennie for doing the hard work to develop systems for exploration and mapping. Vishnu, thank you for demonstrating the first feasible solution to the graduation problem. Your leadership was invaluable. Shaurya, thank you for being someone who I can talk to about research and everything else. Also, John, the pandemic has taken our pizza, and I want it back. Thanks to everyone else in the lab who has made my time here brighter, including Alex, Xuning, Aditya, Arjav, Mosam, Moses, Tim, Matt, Mike, Erik, and anyone else who I may have forgotten. Also, Karen Widmaier, you put in a good deal of hard work for our lab, and you make our hearts warmer.Many thanks to my friends in the Robotics Institute. Achal and Radhika, Dhruv and Cara, and now I am compelled to mention Xuning again, thank you for the adventures that brought so much joy to this time. Shushman, you are a caring friend. I did not introduce you to my family; I introduced my family to you. Thank you Reuben, you speak softly, but your words carry great weight. Nick, you have done great things for the RI, but you also do great things for frogs.Thanks to everyone else who contributed to my time here. Rachel Burcin, I am only one of a vast number of people who have been significantly impacted by your tireless enthusiasm administrating RISS. I could not keep up with you. Reid, you welcomed me to CMU twice, and I hope to someday become more like you. Thank you Michael Erdmann, you allowed me to babble about submodular functions in front of 16-811. Thanks Sankalp, your words in front of same class introduced me to same topic. My thanks also to Roie for validating my interest in 3-increasing functions and for our too infrequent exchanges on the topic.Thank you Nate for guiding my journey in robotics at CMU. You accepted my stubbornness and pushed me to excellence. Thanks also to the rest of my committee, Anupam, Katia, and Mac for direction and advice. I hope these first few pages are to your liking; for the next hundred, well I am not Asimov.

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Autonomous Target Search with Multiple Coordinated UAVs

Cited by 14 publications

References 69 publications

Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences

Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences

Scalable Distributed Planning for Multi-Robot, Multi-Target Tracking

Sensor Planning for Large Numbers of Robots

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