Active SLAM using Model Predictive Control and Attractor based Exploration

Leung, Cindy; Huang, Shoudong; Dissanayake, Gamini

doi:10.1109/iros.2006.282530

Cited by 100 publications

(91 citation statements)

References 11 publications

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“…In [17,19,149] free parameters are used in the objective function to motivate exploration. In [94,95,147,176,178] the world is populated with a set of dummy landmarks, and by finding the actions that reduce the uncertainty of all landmarks and the robot pose, the coverage constraint is implictly considered.…”

Section: Coverage Mechanismsmentioning

confidence: 99%

“…This idea has been pursued in some works [67,[94][95][96]106]. Huang et al [67] introduced a discussion about the problem of multi-step look-ahead exploration in the context of SLAM, arguing that multi-step active SLAM is possible when the current estimation error is small, the probability of observing new feature is low, and the computation capability is high.…”

Section: Action Selectionmentioning

confidence: 99%

“…Their method begins with a geometric coverage planner that selects sensing points, which are in turn inserted in the state to be used as attractors, in the same way as it is done in [94,178]. The cost function is defined as the sum of the squared errors and their action set consisted of discrete parameters of a cubic spline.…”

Section: Action Selectionmentioning

confidence: 99%

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Mapping, Planning and Exploration with Pose SLAM

Valencia,

Andrade-Cetto

2018

Springer Tracts in Advanced Robotics

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This thesis reports research on mapping, path planning, and autonomous exploration. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common SLAM approach, adopting Pose SLAM as the basic state estimation machinery. The main contribution of this thesis is an approach that allows a mobile robot to plan a path using the map it builds with Pose SLAM and to select the appropriate actions to autonomously construct this map.Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. In Pose SLAM, observations come in the form of relative-motion measurements between robot poses. With regards to extending the original Pose SLAM formulation, this thesis studies the computation of such measurements when they are obtained with stereo cameras and develops the appropriate noise propagation models for such case. Furthermore, the initial formulation of Pose SLAM assumes poses in SE(2) and in this thesis we extend this formulation to SE(3), parameterizing rotations either with Euler angles and quaternions. We also introduce a loop closure test that exploits the information from the filter using an independent measure of information content between poses. In the application domain, we present a technique to process the 3D volumetric maps obtained with this SLAM methodology, but with laser range scanning as the sensor modality, to derive traversability maps that were useful for the navigation of a heterogeneous fleet of mobile robots in the context of the EU project URUS.Aside from these extensions to Pose SLAM, the core contribution of the thesis is an approach for path planning that exploits the modeled uncertainties in Pose SLAM to search for the path in the pose graph with the lowest accumulated robot pose uncertainty, i.e., the path that allows the robot to navigate to a given goal with the least probability of becoming lost. An added advantage of the proposed path planning approach is that since Pose SLAM is agnostic with respect to the sensor modalities used, it can be used in different environments and with i different robots, and since the original pose graph may come from a previous mapping session, the paths stored in the map already satisfy constraints not easy modeled in the robot controller, such as the existence of restricted regions, or the right of way along paths. The proposed path planning methodology has been extensively tested both in simulation and with a real outdoor robot.Our path planning approach is adequate for scenarios where a robot is initially guided during map construction, but autonomous during execution. For other scenarios in which more autonomy is required, the robot should be able to explore the environment without any supervision. The second core contribution of this thesis is an autonomous exploration method that complements the aforementioned path planning strategy. The method selects the appropriate...

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Section: Coverage Mechanismsmentioning

confidence: 99%

Section: Action Selectionmentioning

confidence: 99%

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Mapping, Planning and Exploration with Pose SLAM

Valencia,

Andrade-Cetto

2018

Springer Tracts in Advanced Robotics

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“…Therefore our method can be applied to any kind of environment. Our proposal can be viewed as an integrated exploration strategy 11,5,26,18 , since the robot is guided to regions that contain information to minimize the uncertainty about its pose. Strategies like this tend to produce better results than purely random strategies, since they use additional information about the environment to determine the best next action to be executed.…”

Section: Particle Selection Based On Bvpmentioning

confidence: 99%

An improved particle filter for sparse environments

Prestes¹,

Ritt²,

Führ³

2009

J. Braz. Comp. Soc.

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Abstract:In this paper, we combine a path planner based on Boundary Value Problems (BVP) and Monte Carlo Localization (MCL) to solve the wake-up robot problem in a sparse environment. This problem is difficult since large regions of sparse environments do not provide relevant information for the robot to recover its pose. We propose a novel method that distributes particle poses only in relevant parts of the environment and leads the robot along these regions using the numeric solution of a BVP. Several experiments show that the improved method leads to a better initial particle distribution and a better convergence of the localization process.

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“…Huang et al [10] propose a model predictive control (MPC) strategy, associated with EKF-SLAM. Leung et al [22] propose an approach in which the MPC strategy is associated with a heuristic based on global attractors. Sim and Roy [28] propose A-optimal strategies for solving the active SLAM problem.…”

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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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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Active SLAM using Model Predictive Control and Attractor based Exploration

Cited by 100 publications

References 11 publications

Mapping, Planning and Exploration with Pose SLAM

Mapping, Planning and Exploration with Pose SLAM

An improved particle filter for sparse environments

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

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