Adaptive Informative Path Planning in Metric Spaces

Lim, Zhan Wei; Hsu, David; Lee, Wee Sun

doi:10.1007/978-3-319-16595-0_17

Cited by 20 publications

(28 citation statements)

References 13 publications

(12 reference statements)

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“…However, the solution is centralized and does not scale well with the number of robots. A polylogarithmic approximation bound in a metric space is provided by Lim et al 14 A greedy information collection strategy has also been used by Dutta and Dasgupta, 15 where a group of robotic modules forms user-defined configurations while collecting maximal information from an environment. Our work in this article uses a similar greedy informative path planning strategy.…”

Section: Related Workmentioning

confidence: 99%

Multi-robot informative path planning in unknown environments through continuous region partitioning

Dutta

Bhattacharya

Kreidl

et al. 2020

International Journal of Advanced Robotic Systems

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We consider the NP-hard problem of multirobot informative path planning in the presence of communication constraints, where the objective is to collect higher amounts of information of an ambient phenomenon. We propose a novel approach that uses continuous region partitioning into Voronoi components to efficiently divide an initially unknown environment among the robots based on newly discovered obstacles enabling improved load balancing between robots. Simulation results show that our proposed approach is successful in reducing the initial imbalance of the robots’ allocated free regions while ensuring close-to-reality spatial modeling within a reasonable amount of time.

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Section: Related Workmentioning

confidence: 99%

Multi-robot informative path planning in unknown environments through continuous region partitioning

Dutta

Bhattacharya

Kreidl

et al. 2020

International Journal of Advanced Robotic Systems

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“…Girdhar and Dudek have implemented a curiosity‐based visual exploration scheme on an autonomous underwater vehicle (AUV) to collect imagery in shallow waters. Most recently, Lim et al . have presented a novel approach to adaptive IPP, which provides solutions in polynomial‐time with optimality guarantees.…”

Section: Related Workmentioning

confidence: 99%

Adaptive continuous‐space informative path planning for online environmental monitoring

Hitz

Galceran

Garneau

et al. 2017

Journal of Field Robotics

103

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Autonomous mobile robots are increasingly employed to take measurements for environmental monitoring, but planning informative, measurement‐rich paths through large three‐dimensional environments is still challenging. Designing such paths, known as the informative path planning (IPP) problem, has been shown to be NP‐hard. Existing algorithms focus on providing guarantees on suboptimal solutions, but do not scale well to large problems. In this paper, we introduce a novel IPP algorithm that uses an evolutionary strategy to optimize a parameterized path in continuous space, which is subject to various constraints regarding path budgets and motion capabilities of an autonomous mobile robot. Moreover, we introduce a replanning scheme to adapt the planned paths according to the measurements taken in situ during data collection. When compared to two state‐of‐the‐art solutions, our method provides competitive results at significantly lower computation times and memory requirements. The proposed replanning scheme enables to build models with up to 25% lower uncertainty within an initially unknown area of interest. Besides presenting theoretical results, we tailored the proposed algorithms for data collection using an autonomous surface vessel for an ecological study, during which the method was validated through three field deployments on Lake Zurich, Switzerland. Spatiotemporal variations are shown over a period of three months and in an area of 350 m × 350 m × 13 m. Whereas our theoretical solution can be applied to multiple applications, our field results specifically highlight the effectiveness of our planner for monitoring toxic microorganisms in a pre‐alpine lake, and for identifying hot‐spots within their distribution.

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“…Singh et al (2009) replanned every step using a non-adaptive information path-planning algorithm. Inspired by adaptive TSP approaches by Gupta et al (2010), Lim et al (2015Lim et al ( , 2016 proposed recursive coverage algorithms to learn policy trees. However, such methods cannot scale well to large state and observation spaces.…”

Section: Monotonic Improvementmentioning

confidence: 99%

Data-driven planning via imitation learning

Choudhury

Bhardwaj

Arora

et al. 2018

The International Journal of Robotics Research

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Robot planning is the process of selecting a sequence of actions that optimize for a task specific objective. For instance, the objective for a navigation task would be to find collision free paths, while the objective for an exploration task would be to map unknown areas. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time.We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial information based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel datadriven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle -an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial information based policies -informative path planning and search based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms stateof-the-art algorithms. Our framework is able to train policies that achieve upto 39% more reward than state-of-the art information gathering heuristics and a 70x speedup as compared to A* on search based planning problems. Our approach paves the way forward for applying data-driven techniques to other such problem domains under the umbrella of robot planning.

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Adaptive Informative Path Planning in Metric Spaces

Cited by 20 publications

References 13 publications

Multi-robot informative path planning in unknown environments through continuous region partitioning

Multi-robot informative path planning in unknown environments through continuous region partitioning

Adaptive continuous‐space informative path planning for online environmental monitoring

Data-driven planning via imitation learning

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