FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget

Peltzer, Oriana; Bouman, Amanda; Kim, Sung-Kyun; Senanayake, Ransalu; Ott, Joshua; Delecki, Harrison; Sobue, Mamoru; Kochenderfer, Mykel J.; Schwager, Mac; Burdick, Joel W.; Agha–mohammadi, Ali–akbar

doi:10.1109/iros47612.2022.9981271

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…), or using specific task oriented performance criteria as for the coverage path planning issue [63,64]. In [65], even though the authors identify several performance metrics (safety-oriented, trajectory and mission metrics), they also highlight that performance metrics are usually neglected or limited to few ones (mission duration [66,67] or path length/energy [33]).…”

Section: Performance and Mobile Roboticsmentioning

confidence: 99%

Performance Guarantee for Autonomous Robotic Missions using Resource Management: The PANORAMA Approach

Lambert,

Godary-Dejean,

Lapierre

et al. 2024

J Intell Robot Syst

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This paper proposes the PANORAMA approach, which is designed to dynamically and autonomously manage the allocation of a robot’s hardware and software resources during fully autonomous mission. This behavioral autonomy approach guarantees the satisfaction of the mission performance constraints. This article clarifies the concept of performance for autonomous robotic missions and details the different phases of the PANORAMA approach. Finally, it focuses on an experimental implementation on a patrolling mission example.

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Section: Performance and Mobile Roboticsmentioning

confidence: 99%

Performance Guarantee for Autonomous Robotic Missions using Resource Management: The PANORAMA Approach

Lambert,

Godary-Dejean,

Lapierre

et al. 2024

J Intell Robot Syst

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“…where t p (n) is the time required to reach frontier n through p, F is the front-loading function proposed in [24], and where p should verify the time budget constraint. F t p (n); W g r ) is a greedy incentive that encourages gathering reward earlier in time in order to trade off long-term finite-horizon planning with immediate information gain.…”

Section: Hierarchical Solvermentioning

confidence: 99%

Fast and Scalable Signal Inference for Active Robotic Source Seeking

Denniston¹,

Peltzer²,

Ott³

et al. 2023

Preprint

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In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification. This model allows the robot to plan for future measurements in the environment. Traditionally, this model has been in the form of a Gaussian process, which has difficulty scaling and cannot represent obstacles. We propose a global and local factor graph model for active source seeking, which allows the model to scale to a large number of measurements and represent unknown obstacles in the environment. We combine this model with extensions to a highly scalable planner to form a system for large-scale active source seeking. We demonstrate that our approach outperforms baseline methods in both simulated and real robot experiments.

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“…Probabilistic extensions to the OP have also been investigated where the associated reward with each location is stochastic [13], [14]. Both robotic exploration [15] and persistent monitoring problems [16] have been approached using solutions to the OP. However, our problem differs in that the reward is not obtained simply by visiting each location.…”

Section: Related Workmentioning

confidence: 99%

Difficulty-Aware Time-Bounded Planning Under Uncertainty for Large-Scale Robot Missions

Staniaszek,

Brudermüller,

Bhattacharyya

et al. 2023

2023 European Conference on Mobile Robots (ECMR)

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We consider planning problems where a robot must visit a large set of locations to complete a task at each one. Our focus is problems where the difficulty of each task, and thus its duration, can be predicted, but not fully known in advance. We propose a general Markov decision process (MDP) model for difficulty-aware problems, and propose variants on this model which allow adaptation to different robotics domains. Due to the intractability of the general problem, we propose simplifications to allow planning in large domains, the key being constraining navigation using a solution to the travelling salesperson problem (TSP). We build a set of variant models for two domains with different characteristics: UV disinfection, and cleaning, evaluating them on maps generated from realworld environments. We evaluate the effect of model variants and simplifications on performance, and show that our models outperform a rule-based baseline.

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FIG-OP: Exploring Large-Scale Unknown Environments on a Fixed Time Budget

Cited by 11 publications

References 31 publications

Performance Guarantee for Autonomous Robotic Missions using Resource Management: The PANORAMA Approach

Performance Guarantee for Autonomous Robotic Missions using Resource Management: The PANORAMA Approach

Fast and Scalable Signal Inference for Active Robotic Source Seeking

Difficulty-Aware Time-Bounded Planning Under Uncertainty for Large-Scale Robot Missions

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