Information-Driven Path Planning for UAV With Limited Autonomy in Large-Scale Field Monitoring

Rosselló, Nicolás Bono; Carpio, Renzo Fabrizio; Gasparri, Andrea; Garone, Emanuele

doi:10.1109/tase.2021.3085365

Cited by 24 publications

(12 citation statements)

References 46 publications

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“…Krause et al first optimized stationary sensor placements by maximizing the mutual information between location candidates to monitor field information (e.g., temperature, humidity) [17]. More recent works have dealt with larger outdoor environments (usually represented as a time-varying Gaussian Random Field), where autonomous robot(s) with onboard sensors need to sequentially take measurements at multiple positions and maintain a field value belief with estimators such as Gaussian Processes [18], [19], Kalman Filters [8], [20], or Proper Orthogonal Decompositions [21]. Yu et al also proposed a variant of the graphbased orienteering problem to monitor a spatio-temporal field with time-invariant spatial correlations [9].…”

Section: Related Workmentioning

confidence: 99%

“…Different from monitoring an underlying vector field of interest [6], [7], which is typically approached as generalized coverage problem or as a variant of the orienteering problem [8], [9], our agent aims to maximize information gain in the vicinity of the targets (i.e., minimizes the uncertainty over the true target positions) [10], [11]. Since this uncertainty grows over time for targets not in direct view of the agent, the agent has to leave some of the targets temporarily untracked to relocate others and then return to those as soon as possible to avoid losing them.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Self-Attention Network for Video Saliency Prediction

Wang

et al. 2023

IEEE Trans. Multimedia

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This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensing range. To keep each target's position estimate as accurate as possible, the robot needs to adaptively plan its path to (re-)visit all the targets and update its belief from measurements collected along the way. In doing so, the main challenge is to strike a balance between exploitation, i.e., re-visiting previously-located targets, and exploration, i.e., finding new targets or re-acquiring lost ones. Encouraged by recent advances in deep reinforcement learning, we introduce an attention-based neural solution to the persistent monitoring problem, where the agent can learn the inter-dependencies between targets, i.e., their spatial and temporal correlations, conditioned on past measurements. This endows the agent with the ability to determine which target, time, and location to attend to across multiple scales, which we show also helps relax the usual limitations of a finite target set. We experimentally demonstrate that our method outperforms other baselines in terms of number of targets visits and average estimation error in complex environments. Finally, we implement and validate our model in a drone-based simulation experiment to monitor mobile ground targets in a high-fidelity simulator.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Self-Attention Network for Video Saliency Prediction

Wang

et al. 2023

IEEE Trans. Multimedia

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“…Mission planning in a Wireless Rechargeable Sensor network [19] generally involves energy supply management of sensor nodes [2], [20], [21], [22], [23], [24] and autonomous vehicles [25], [26], data gathering from sensor nodes [3], [27] and surveillance [28], [29]. Our focus is to solve the optimization problems raised in the scenario of sensor node energy replenishment as a maintenance task.…”

Section: Related Workmentioning

confidence: 99%

Practical Mission Planning for Optimized UAV-Sensor Wireless Recharging

Qian¹,

OKeeffe²,

Wang³

et al. 2022

Preprint

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Recharging Internet of Things devices using autonomous robots is an attractive maintenance solution. Ensuring efficient and reliable performance of autonomous power delivery vehicles is very challenging in dynamic environments. Our work considers a hybrid Travelling Salesman Problem and Orienteering Problem scenario where the optimization objective is to jointly minimize discharged energy of the power delivery vehicle and maximize recharged energy of devices. This is decomposed as an NP-hard nonconvex optimization and nonlinear integer programming problem. Many studies have demonstrated satisfactory performance of heuristic algorithms' ability to solve specific routing problems, however very few studies explore online updating (i.e., mission re-planning 'on the fly') for such hybrid scenarios. In this paper, we present a novel lightweight and reliable mission planner that solves the problem by combining offline search and online reevaluation. We propose Rapid Online Metaheuristic-based Planner, ROMP, a multi-objective offline and online mission planner that can incorporate real-time state information from the power delivery vehicle and its local environment to deliver reliable, up-to-date and near-optimal mission planning. We supplement Guided Local Search (via Google OR-Tools) with a Black Hole-inspired algorithm. Our results show that the proposed solver improves the solution quality offered by Guided Local Search in most of the cases tested. We also demonstrate latency performance improvements by applying a parallelization strategy.

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“…They formulate the problem as a traveling salesman problem (TSP) to deal with the UAV's flight time by optimizing UAV's energy. Rossello et al [25] develop a novel path planning algorithm to cover large-scale areas for precision agriculture. They consider the flying time constraint and maximizing the estimation quality of the system states.…”

Section: Uav-enabled Techniquesmentioning

confidence: 99%

Energy-Sensitive Trajectory Design and Restoration Areas Allocation for UAV-Enabled Grassland Restoration

Jiao¹,

Wang²,

Yang³

et al. 2022

Preprint

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Grassland restoration is a critical means to safeguard grassland ecological degradation. This work considers the maximization restoration areas problem for UAV-enabled grassland restoration method in a restoration process, which is a precise restoration scheme. We first formulate the problem as a multivariable combinatorial optimization problem. By analyzing the characteristics of the optimization problem, it can be decomposed into the two stages: UAV trajectory design and restoration areas allocation. On this basis, the problem can be regarded as a composite problem of traveling salesman problem (TSP) and multidimensional knapsack problem (MKP). Unlike the single combinatorial optimization problem, the coupling relationship between them makes the problem difficult to directly solve by employing the single stage traditional methods. To effectively solve without ignoring the dependence between the two stages, we develop a cooperative optimization algorithm based on heuristic algorithm and population-based incremental learning (PBIL) incorporated with a maximum-residual-energy-based local search (MRELS) strategy, called CHAPBILM, to deal with this problem under constraint conditions of the UAV energy, the total seeds weight, and the number of restored areas. The simulation studies demonstrate that the proposed cooperative optimization method is effective for UAV-enabled grassland restoration problem and can significantly outperform the noncooperative optimization methods, which also verifies the dependency relationship between UAV trajectory design and restoration areas allocation.

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Information-Driven Path Planning for UAV With Limited Autonomy in Large-Scale Field Monitoring

Cited by 24 publications

References 46 publications

Spatio-Temporal Self-Attention Network for Video Saliency Prediction

Spatio-Temporal Self-Attention Network for Video Saliency Prediction

Practical Mission Planning for Optimized UAV-Sensor Wireless Recharging

Energy-Sensitive Trajectory Design and Restoration Areas Allocation for UAV-Enabled Grassland Restoration

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