Branch and bound for informative path planning

Binney, Jonathan; Sukhatme, Gaurav S.

doi:10.1109/icra.2012.6224902

Cited by 114 publications

(96 citation statements)

References 19 publications

(18 reference 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%

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Planning under uncertainty in the continuous domain: A generalized belief space approach

Indelman

Carlone

Dellaert

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This work investigates the problem of planning under uncertainty, with application to mobile robotics. We propose a probabilistic framework in which the robot bases its decisions on the generalized belief, which is a probabilistic description of its own state and of external variables of interest. The approach naturally leads to a dual-layer architecture: an inner estimation layer, which performs inference to predict the outcome of possible decisions, and an outer decisional layer which is in charge of deciding the best action to undertake. The approach does not discretize the state or control space, and allows planning in continuous domain. Moreover, it allows to relax the assumption of maximum likelihood observations: predicted measurements are treated as random variables and are not considered as given. Experimental results show that our planning approach produces smooth trajectories while maintaining uncertainty within reasonable bounds.

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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%

Planning under uncertainty in the continuous domain: A generalized belief space approach

Indelman

Carlone

Dellaert

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…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%

Sampling-based Motion Planning for Robotic Information Gathering

Hollinger

Sukhatme

2013

Robotics: Science and Systems IX

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Abstract-We propose an incremental sampling-based motion planning algorithm that generates maximally informative trajectories for guiding mobile robots to observe their environment. The goal is to find a trajectory that maximizes an information metric (e.g., variance reduction, information gain, or mutual information) and also falls within a pre-specified budget constraint (e.g., fuel, energy, or time). Prior algorithms have employed combinatorial optimization techniques to solve these problems, but existing techniques are typically restricted to discrete domains and often scale poorly in the size of the problem. Our proposed Rapidly-exploring Information Gathering (RIG) algorithm extends ideas from Rapidly-exploring Random Graphs (RRGs) and combines them with branch and bound techniques to achieve efficient optimization of information gathering while also allowing for operation in continuous space with motion constraints. We provide a rigorous analysis of the asymptotic optimality of our approach, and we present several conservative pruning strategies for modular, submodular, and time-varying information objectives. We demonstrate that our proposed approach finds optimal solutions more quickly than existing combinatorial solvers, and we provide a proof-ofconcept field implementation on an autonomous surface vehicle performing a wireless signal strength monitoring task in a lake.

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“…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%

Multi-robot path planning for budgeted active perception with self-organising maps

Best

Faigl

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has polynomial-bounded runtime independent of the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and selfocclusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Simulations were performed using a 3D point cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for budgeted active perception tasks with continuous sets of candidate viewpoints and long planning horizons.

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Branch and bound for informative path planning

Cited by 114 publications

References 19 publications

Planning under uncertainty in the continuous domain: A generalized belief space approach

Planning under uncertainty in the continuous domain: A generalized belief space approach

Sampling-based Motion Planning for Robotic Information Gathering

Multi-robot path planning for budgeted active perception with self-organising maps

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