Experimental Analysis of Receding Horizon Planning Algorithms for Marine Monitoring

Yoo, Soo-Hyun; Stuntz, Andrew; Zhang, Yawei; Rothschild, R.; Hollinger, Geoffrey A.; Smith, Ryan N.

doi:10.1007/978-3-319-27702-8_3

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Some of the previous works have tried to solve the IPP problem using a receding horizon approach. The work by [13] proposed an adaptive receding horizon controller for marine monitoring that modifies the lookahead step size to not get stuck in a local optima. The work by [3] compares finite horizon tree search, locally optimal mypopic search, Monte Carlo tree search, and their own versions of cluster tree search when searching for a target while gliding between thermals.…”

Section: Related Workmentioning

confidence: 99%

TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning

Moon¹,

Chatterjee²,

Scherer³

2022

Preprint

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Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces.[Video] a

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Section: Related Workmentioning

confidence: 99%

TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning

Moon¹,

Chatterjee²,

Scherer³

2022

Preprint

View full text Add to dashboard Cite

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“…This process of plan‐act‐replan‐act‐replan is repeated until the mission is complete. Receding horizon‐based planners have been shown to perform well in dynamic (Yoo et al, 2016) and partially observable (Watterson & Kumar, 2015) environments.…”

Section: Autonomy Algorithmmentioning

confidence: 99%

Ocean front detection and tracking using a team of heterogeneous marine vehicles

McCammon

Santos

Frantz

et al. 2021

Journal of Field Robotics

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Ocean monitoring is an expensive and time consuming endeavor, but it can be made more efficient through the use of teams of autonomous robots. In this paper, we present a system for the autonomous identification and tracking of ocean fronts by coordinating the sampling efforts of a heterogeneous team of autonomous surface vehicles (ASVs) and autonomous underwater vehicles (AUVs). The primary contributions of this study are (1) our algorithm for performing autonomous coordination using general autonomy principles: Sequential Allocation Monte Carlo Tree Search (SA‐MCTS) which incorporates domain knowledge into the environmental estimation through both augmenting a standard Gaussian process with a nearest neighbors prior and planning in a drifting reference frame, (2) our decision support user interface to help human operators oversee the autonomous system, and (3) the demonstration of the system's operation in a 2‐week long deployment in the Gulf of Mexico using a heterogeneous team of four Slocum gliders and two robotic ocean surface samplers. With these contributions, we aim to bridge the gap between state of the art autonomy algorithms and marine vehicle planning methods that have been tested in large‐scale field trials. This paper presents the first deployment of a general, heuristic‐based, multi‐robot coordination algorithm for an extended sampling mission.

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“…Several efforts (Alam, Reis, Bobadilla, & Smith, ; Das et al., ; Hollinger and Sukhatme, ; Lawrance, Chung, & Hollinger, ; Ma, Liu, Heidarsson, & Sukhatme, ; Yoo et al., ) use an information quality metric (e.g., information gain or variance reduction) along with system constraints (e.g., battery) to plan motion during environmental monitoring in aqueous environments, mostly with AUVs and ASVs. These works plan a path and choose a waypoint (or depth) based on a data‐driven metric, whereas our work assumes the target depth has already been chosen.…”

Section: Related Workmentioning

confidence: 99%

Sensing water properties at precise depths from the air

Ore

Detweiler

2018

Journal of Field Robotics

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Water properties are critical to our understanding and managing of freshwater systems that change rapidly with depth. This work presents an unmanned aerial vehicle (UAV) -based method of keeping a submerged, cable-suspended sensor payload at a precise depth, with 95% of sensor readings within ±8.4 cm of the target depth. We use a depth altimeter attached at the terminus of a 3.5 m semirigid cable as the sole input to a depth controller actuated by the UAV's motors.First, we simulate the UAV-cable-payload system with disturbances of wind, water, signal delay, and GPS drift and then use parameters discovered during simulation to guide implementation. We characterize the depth altimeter during translation and find that 95% of the readings are within ±7 mm of ground truth, with a steady-state error of ±3 mm. In field experiments, we compare the depth precision of our new method to previous methods that used the UAV's altitude as a proxy for submerged sensor depth, specifically 1) only using the UAV's air pressure altimeter; and 2) fusing UAV-mounted ultrasonic sensors with the air pressure altimeter. Our new method reduces the standard deviation of depth readings from 16.1 to 4.2 cm in winds up to 8.0 ms −1 . We show the step response of the depth-altimeter method when transitioning between target depths. Finally, we explore a longer, 8.0 m cable and show that our depth controller still outperforms air altimeter and ultrasonic methods and allows scientists to increase the spatiotemporal resolution of water property datasets.

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Experimental Analysis of Receding Horizon Planning Algorithms for Marine Monitoring

Cited by 8 publications

References 9 publications

TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning

TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning

Ocean front detection and tracking using a team of heterogeneous marine vehicles

Sensing water properties at precise depths from the air

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