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Persistent ocean monitoring with underwater gliders: Towards accurate reconstruction of dynamic ocean processes
 In Proceedings of the International Conference on Robotics and Automation
, 2011
"... Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. ..."
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Cited by 33 (16 self)
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Ocean processes are dynamic and complex and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect highvalue data. More specifically, this paper proposes a path planning algorithm and aspeedcontrolalgorithmforunderwatergliders,whichtogethergiveinformativetrajectoriesfortheglider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed
Planning periodic persistent monitoring trajectories for sensing robots in gaussian random fields
 In Robotics and Automation (ICRA), 2013 IEEE International Conference on
, 2013
"... Abstract — This paper considers the problem of planning a trajectory for a sensing robot to best estimate a timechanging Gaussian Random Field in its environment. The robot uses a Kalman filter to maintain an estimate of the field value, and to compute the error covariance matrix of the estimate. A ..."
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Cited by 11 (3 self)
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Abstract — This paper considers the problem of planning a trajectory for a sensing robot to best estimate a timechanging Gaussian Random Field in its environment. The robot uses a Kalman filter to maintain an estimate of the field value, and to compute the error covariance matrix of the estimate. A new randomized path planning algorithm is proposed to find a periodic trajectory for the sensing robot that tries to minimize the largest eigenvalue of the error covariance matrix over an infinite horizon. The algorithm is proven to find the minimum infinite horizon cost cycle in a graph, which grows by successively adding random points. The algorithm leverages recently developed methods for periodic Riccati recursions to efficiently compute the infinite horizon cost of the cycles, and it uses the monotonicity property of the Riccati recursion to efficiently compare the cost of different cycles without explicitly computing their costs. The performance of the algorithm is demonstrated in numerical simulations. I.
Cassandras, “An optimal control approach to the multiagent persistent monitoring problem in twodimensional spaces
 in Proc. of 52nd IEEE Conf. Decision and Control, 2013
"... AbstractWe present an optimal control framework for persistent monitoring problems where the objective is to control the movement of multiple cooperating agents to minimize an uncertainty metric in a given mission space. In a onedimensional mission space, we show that the optimal solution is for ..."
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Cited by 9 (1 self)
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AbstractWe present an optimal control framework for persistent monitoring problems where the objective is to control the movement of multiple cooperating agents to minimize an uncertainty metric in a given mission space. In a onedimensional mission space, we show that the optimal solution is for each agent to move at maximal speed from one switching point to the next, possibly waiting some time at each point before reversing its direction. Thus, the solution is reduced to a simpler parametric optimization problem: determining a sequence of switching locations and associated waiting times at these switching points for each agent. This amounts to a hybrid system which we analyze using Infinitesimal Perturbation Analysis (IPA) to obtain a complete online solution through a gradientbased algorithm. We also show that the solution is robust with respect to the uncertainty model used. This establishes the basis for extending this approach to a twodimensional mission space.
1 Cooperative Patrolling via Weighted Tours: Performance Analysis and Distributed Algorithms
"... Abstract—This work focuses on the problem of patrolling an environment with a team of autonomous agents. Given a set of strategically important locations (viewpoints) with different priorities, our patrolling strategy consists of (i) constructing a tour through the viewpoints, and (ii) driving the r ..."
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Cited by 8 (1 self)
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Abstract—This work focuses on the problem of patrolling an environment with a team of autonomous agents. Given a set of strategically important locations (viewpoints) with different priorities, our patrolling strategy consists of (i) constructing a tour through the viewpoints, and (ii) driving the robots along the tour in a coordinated way. As performance criteria, we consider the weighted refresh time, i.e., the longest time interval between any two visits of a viewpoint, weighted by the viewpoint’s priority. We consider the design of both optimal trajectories and distributed control laws for the robots to converge to optimal trajectories. First, we propose a patrolling strategy and we characterize its performance as a function of the environment and the viewpoints priorities. Second, we restrict our attention to the problem of patrolling a nonintersecting tour, and we describe a team trajectory with minimum weighted refresh time. Third, for the tour patrolling problem and for two distinct communication scenarios, namely the Passing and the NeighborBroadcast communication models, we develop distributed algorithms to steer the robots towards a minimum weighted refresh time team trajectory. Finally, we show the effectiveness and robustness of our control algorithms via simulations and experiments. Fig. 1. This figure represents a part of the UCSB campus. For the surveillance of the buildings in the map by a team of autonomous robots, a set of 35 important locations (viewpoints) has been identified, and a tour through the viewpoints has been computed. The robots repeatedly patrol the tour to guarantee complete and persistent surveillance of the buildings. We propose the EqualTimeSpacing trajectory, which minimizes the longest priorityweighted time gap between any two visits of the same viewpoint. I.
Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations,”
 The International Journal of Robotics Research,
, 2014
"... Abstract In this paper, we consider the problem of planning a path for a robot to monitor a known set of features of interest in an environment. We represent the environment as a graph with vertex weights and edge lengths. The vertices represent regions of interest, edge lengths give travel times b ..."
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Cited by 7 (0 self)
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Abstract In this paper, we consider the problem of planning a path for a robot to monitor a known set of features of interest in an environment. We represent the environment as a graph with vertex weights and edge lengths. The vertices represent regions of interest, edge lengths give travel times between regions, and the vertex weights give the importance of each region. As the robot repeatedly performs a closed walk on the graph, we define the weighted latency of a vertex to be the maximum time between visits to that vertex, weighted by the importance (vertex weight) of that vertex. Our goal is to find a closed walk that minimizes the maximum weighted latency of any vertex. We show that there does not exist a polynomial time algorithm for the problem. We then provide two approximation algorithms; an O(log n)approximation algorithm and an O(log ρ G )approximation algorithm, where ρ G is the ratio between the maximum and minimum vertex weights. We provide simulation results which demonstrate that our algorithms can be applied to problems consisting of thousands of vertices, and a case study for patrolling a city for crime.
CoOptimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments
"... Abstract—We consider the scenario where a robot is tasked with sending a fixed number of given bits of information to a remote station, in a limited operation time, as it travels along a predefined trajectory, and while minimizing its motion and communication energy costs. We propose a cooptimizat ..."
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Cited by 7 (1 self)
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Abstract—We consider the scenario where a robot is tasked with sending a fixed number of given bits of information to a remote station, in a limited operation time, as it travels along a predefined trajectory, and while minimizing its motion and communication energy costs. We propose a cooptimization framework that allows the robot to plan its motion speed, transmission rate and stop time, based on its probabilistic prediction of the channel quality along the trajectory. We show that in order to save energy, the robot should move faster (slower) and send less (more) bits at the locations that have worse (better) predicted channel qualities. We furthermore prove that if the robot must stop, it should then stop only once and at the location with the best predicted channel quality. We also prove some properties for two special scenarios: the heavytask load and the lighttask load cases. We also propose an additional stoptime online adaptation strategy to further fine tune the stop location as the robot moves along its trajectory and measures the true value of the channel. Finally, our simulation results show that our proposed framework results in a considerable performance improvement. Index Terms—Communication and motion cooptimization, realistic communication channels, energy optimization, probabilistic channel predication. I.
Stochastic surveillance strategies for spatial quickest detection
 In IEEE Conf. on Decision and Control and European Control Conference
, 2011
"... Abstract—We present stochastic vehicle routing policies for detection of any number of anomalies in a set of regions of interest. The autonomous vehicle collects information from a set of regions and sends it to a fusion center. The vehicle follows a randomized region selection policy at each iterat ..."
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Cited by 5 (5 self)
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Abstract—We present stochastic vehicle routing policies for detection of any number of anomalies in a set of regions of interest. The autonomous vehicle collects information from a set of regions and sends it to a fusion center. The vehicle follows a randomized region selection policy at each iteration. Using the collected information, the fusion center runs an ensemble of cumulative sum (CUSUM) algorithms in order to detect the presence of an anomaly in any region. We first determine optimal stationary policies that result in quickest detection of all anomalies. We then study an adaptive policy that assigns higher selection probability to a region with higher likelihood of an anomaly. We provide a comparative study of these policies. I.
Persistent Monitoring of Events with Stochastic Arrivals at Multiple Stations
"... Abstract—This paper is concerned with a novel mobile sensor scheduling problem, involving a single robot tasked with monitoring several events of interest that occur at different locations. Of particular interest is the monitoring of events that can not be easily forecast. Prominent examples range ..."
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Cited by 5 (4 self)
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Abstract—This paper is concerned with a novel mobile sensor scheduling problem, involving a single robot tasked with monitoring several events of interest that occur at different locations. Of particular interest is the monitoring of events that can not be easily forecast. Prominent examples range from natural phenomena (e.g., monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities (e.g., monitoring early formations of traffic congestion in the Boston area using an aerial robot). Motivated by these examples, this paper focuses on problems where the precise occurrence time of the events is not known a priori, but some statistics for their interarrival times are available from past observations. The robot’s task is to monitor the events to optimize the following two objectives: (i) maximize the number of events observed and (ii) minimize the delay between two consecutive observations of events occurring at the same location. Provided with only one robot, it is crucial to optimize these objectives in a balanced way, so that they are optimized at each station simultaneously. Our main theoretical result is that this complex mobile sensor scheduling problem can be reduced to a quasiconvex program, which can be solved in polynomial time. In other words, a globally optimal solution can be computed in time that is polynomial in the number of locations. We also provide computational experiments that validate our theoretical results. I.
Collision Avoidance for Persistent Monitoring in MultiRobot Systems with Intersecting Trajectories
"... Abstract — Persistent robot tasks such as monitoring and cleaning are concerned with controlling mobile robots to act in a changing environment in a way that guarantees that the uncertainty in the system (due to change and to the actions of the robot) remains bounded for all time. Prior work in pers ..."
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Cited by 4 (1 self)
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Abstract — Persistent robot tasks such as monitoring and cleaning are concerned with controlling mobile robots to act in a changing environment in a way that guarantees that the uncertainty in the system (due to change and to the actions of the robot) remains bounded for all time. Prior work in persistent robot tasks considered only robot systems with collisionfree paths that move following speed controllers. In this paper we describe a solution to multirobot persistent monitoring, where robots have intersecting trajectories. We develop collision and deadlock avoidance algorithms that are based on stopping policies, and quantify the impact of the stopping times on the overall stability of the speed controllers. I.
Generating Informative Paths for Persistent Sensing in Unknown Environments
"... Abstract — We present an online algorithm for a robot to shape its path to a locally optimal configuration for collecting information in an unknown dynamic environment. As the robot travels along its path, it identifies both where the environment is changing, and how fast it is changing. The algorit ..."
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Cited by 3 (2 self)
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Abstract — We present an online algorithm for a robot to shape its path to a locally optimal configuration for collecting information in an unknown dynamic environment. As the robot travels along its path, it identifies both where the environment is changing, and how fast it is changing. The algorithm then morphs the robot’s path online to concentrate on the dynamic areas in the environment in proportion to their rate of change. A Lyapunovlike stability proof is used to show that, under our proposed path shaping algorithm, the path converges to a locally optimal configuration according to a Voronoibased coverage criterion. The path shaping algorithm is then combined with a previously introduced speed controller to produce guaranteed persistent monitoring trajectories for a robot in an unknown dynamic environment. Simulation and experimental results with a quadrotor robot support the proposed approach. I.