2012 IEEE International Conference on Robotics and Automation 2012
DOI: 10.1109/icra.2012.6224902
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Branch and bound for informative path planning

Abstract: Abstract-We present an optimal algorithm for informative path planning (IPP), using a branch and bound method inspired by feature selection algorithms. The algorithm uses the monotonicity of the objective function to give an objective functiondependent speedup versus brute force search. We present results which suggest that when maximizing variance reduction in a Gaussian process model, the speedup is significant.

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Cited by 114 publications
(96 citation statements)
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“…The basic observation model, commonly used in motion planning, e.g., [2], involves only the current robot state x i at each time t i and is a particular case of the above general model (3). In general Z i may include several observations: in the next section we consider the case in which Z i = {z i,1 , .…”
Section: Generalized Belief Space a Notation And Probabilistic Fmentioning
confidence: 99%
See 1 more Smart Citation
“…The basic observation model, commonly used in motion planning, e.g., [2], involves only the current robot state x i at each time t i and is a particular case of the above general model (3). In general Z i may include several observations: in the next section we consider the case in which Z i = {z i,1 , .…”
Section: Generalized Belief Space a Notation And Probabilistic Fmentioning
confidence: 99%
“…A related problem is also the so-called informative path planning (although in these problems the estimation aspects are often neglected). A greedy strategy for informative path planning is proposed by Singh et al [29] while a branch and bound approach is proposed by Binney et al in [3]. More recently, Hollinger et.…”
Section: Introductionmentioning
confidence: 99%
“…Such problems have been shown to be NP-hard [20] or even PSPACE-hard [18] depending on the form of the objective function and the space of possible trajectories. Prior work has leveraged approximation algorithms [20] and branch and bound solvers [2] to provide informative trajectories for mobile robots. However, these prior algorithms are limited to discrete domains and often scale poorly in the amount of budget and the size of the environment.…”
Section: Introductionmentioning
confidence: 99%
“…Our approach combines ideas from Rapidly-exploring Random Graphs (RRGs) [12] with insights from branch and bound optimization [2] to provide improved efficiency and generality. Adapting theoretical analysis from the RRT* algorithm also allows us to show asymptotic optimality for a large class of objective functions.…”
Section: Introductionmentioning
confidence: 99%
“…The GTSP has been applied to robotic path planning problems for mapping [10] and mobile refuelling [20] tasks, which are solved using a transformation to the standard TSP. Related graph-based robot path planning algorithms include branch and bound [21] and sweep planes [22]. These formulations restrict the search to discrete points and the computation time increases with the number of points.…”
Section: Related Workmentioning
confidence: 99%