Informative path planning as a maximum traveling salesman problem with submodular rewards

Jawaid, Syed Talha; Smith, Stephen L.

doi:10.1016/j.dam.2015.01.004

Cited by 16 publications

(8 citation statements)

References 24 publications

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“…while provable worst-case bounds on the suboptimality can be established [39], a detailed mathematical analysis is beyond the scope of this paper and is not reported here.…”

Section: Next-best-view Planningmentioning

confidence: 99%

“…The TSGA algorithm greedily assigns a cluster to a UAV, when it locally maximizes the overall utility of the fleet of N robots. Unlike classical insertion methods where a cluster is added to a robot's path [39], the maxATSP problem is solved for the extended cluster set U i ∪ v, which results in a more efficient path for UAV i. Once the clusters have been assigned and the corresponding paths…”

Section: Next-best-view Planningmentioning

confidence: 99%

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Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

Hardouin

Moras

Morbidi

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we propose a novel cluster-based Next-Best-View path planning algorithm to simultaneously explore and inspect large-scale unknown environments with multiple Unmanned Aerial Vehicles (UAVs). In the majority of existing informative path-planning methods, a volumetric criterion is used for the exploration of unknown areas, and the presence of surfaces is only taken into account indirectly. Unfortunately, this approach may lead to inaccurate 3D models, with no guarantee of global surface coverage. To perform accurate 3D reconstructions and minimize runtime, we extend our previous online planner based on TSDF (Truncated Signed Distance Function) mapping, to a fleet of UAVs. Sensor configurations to be visited are directly extracted from the map and assigned greedily to the aerial vehicles, in order to maximize the global utility at the fleet level. The performances of the proposed TSGA (TSP-Greedy Allocation) planner and of a nearest neighbor planner have been compared via realistic numerical experiments in two challenging environments (a power plant and the Statue of Liberty) with up to five quadrotor UAVs equipped with stereo cameras.

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“…while provable worst-case bounds on the suboptimality can be established [39], a detailed mathematical analysis is beyond the scope of this paper and is not reported here.…”

Section: Next-best-view Planningmentioning

confidence: 99%

Section: Next-best-view Planningmentioning

confidence: 99%

Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

Hardouin

Moras

Morbidi

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…II-A1. There are also application specific approaches in literature but they either do not generalize to unknown environments or cannot plan for multiple sensing modalities without additional heuristics [55].…”

Section: Informative Path Planningmentioning

confidence: 99%

Multi-modal active perception for information gathering in science missions

et al. 2019

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Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots' reliance on human supervision and improve science productivity by encoding scientists' domain knowledge and decision making process on-board. We use Bayesian networks to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition.

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“…Other traditional constraints that have been considered for submodular maximization include knapsack [15], budget [6] and matroid constraints [16], [17]. Submodular maximization has also been studied in the context of robotics and controls, being used in applications such as sensor coverage [13], sensor selection for Kalman filtering [18], [19], [20], multirobot exploration objectives [21], voltage control [1], multiagent target tracking [22], informative path planning [23], and control input selection [24].…”

Section: Introductionmentioning

confidence: 99%

Submodular Maximization with Limited Function Access

Downie¹,

Smith²,

Smith³

2022

Preprint

Self Cite

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We consider a class of submodular maximization problems in which decision-makers have limited access to the objective function. We explore scenarios where the decisionmaker can observe only pairwise information, i.e., can evaluate the objective function on sets of size two. We begin with a negative result that no algorithm using only k-wise information can guarantee performance better than k/n. We present two algorithms that utilize only pairwise information about the function and characterize their performance relative to the optimal, which depends on the curvature of the submodular function. Additionally, if the submodular function possess a property called supermodularity of conditioning, then we can provide a method to bound the performance based purely on pairwise information. The proposed algorithms offer significant computational speedups over a traditional greedy strategy. A by-product of our study is the introduction of two new notions of curvature, the k-Marginal Curvature and the k-Cardinality Curvature. Finally, we present experiments highlighting the performance of our proposed algorithms in terms of approximation and time complexity.

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Informative path planning as a maximum traveling salesman problem with submodular rewards

Cited by 16 publications

References 24 publications

Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

Next-Best-View planning for surface reconstruction of large-scale 3D environments with multiple UAVs

Multi-modal active perception for information gathering in science missions

Submodular Maximization with Limited Function Access

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