Planning under uncertainty using model predictive control for information gathering

Leung, Cindy; Huang, Shoudong; Kwok, Ngaiming; Dissanayake, Gamini

doi:10.1016/j.robot.2006.05.008

Cited by 59 publications

(70 citation statements)

References 12 publications

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“…At this stage, Assumptions 1-3 and condition (11) are fulfilled by properly choosing control parameters in the MPC design procedure, which is summarized as follows:…”

Section: Terminal Region and Controllermentioning

confidence: 99%

“…For nonlinear MPC, although it is more powerful, the computational time restricts the control bandwidth in a low range. Therefore, the nonlinear MPC is more likely to be seen in the guidance layer to enhance the autonomy of the UAVs rather than in the time-critical flight control layer [10,11]. Nonlinear MPC also has been applied to the control of helicopters' group formation in [12], but only simulation results are provided.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Piecewise constant model predictive control for autonomous helicopters

Liu¹,

Chen²,

Andrews³

2011

Robotics and Autonomous Systems

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“…At this stage, Assumptions 1-3 and condition (11) are fulfilled by properly choosing control parameters in the MPC design procedure, which is summarized as follows:…”

Section: Terminal Region and Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Piecewise constant model predictive control for autonomous helicopters

Liu¹,

Chen²,

Andrews³

2011

Robotics and Autonomous Systems

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“…In our case, the expected number of landmarks to see and a very rough uniform disposition of them in the environment are our initial conditions. Several authors make such assumption either with a priori grid-based discretization of the environment [14], [20] or by adding uniformly distributed unvisited landmarks as vague priors [21], [22].…”

Section: Action Selectionmentioning

confidence: 99%

Action Selection for Single-Camera SLAM

Vidal-Calleja

Sanfeliu

Andrade‐Cetto

2010

IEEE Trans. Syst., Man, Cybern. B

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Abstract-A method for evaluating, at video rate, the quality of actions for a single camera while mapping unknown indoor environments is presented. The strategy maximizes mutual information between measurements and states to help the camera avoid making ill-conditioned measurements that are appropriate to lack of depth in monocular vision systems. Our system prompts a user with the appropriate motion commands during 6-DOF visual simultaneous localization and mapping with a handheld camera. Additionally, the system has been ported to a mobile robotic platform, thus closing the control-estimation loop. To show the viability of the approach, simulations and experiments are presented for the unconstrained motion of a handheld camera and for the motion of a mobile robot with nonholonomic constraints. When combined with a path planner, the technique safely drives to a marked goal while, at the same time, producing an optimal estimated map.Index Terms-Action selection, active vision, bearing-only simultaneous localization and mapping (SLAM), mutual information, path planning.

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“…Action selection in SLAM can also be approached as an optimization problem using receding horizon strategies [6,11,10]. Multi-step look ahead exploration in the context of SLAM makes sense only for situations in which the concatenation of prior estimates without measurement evidence remain accurate for large motion sequences.…”

Section: Introductionmentioning

confidence: 99%

Potential information fields for mobile robot exploration

Vallvé

Andrade‐Cetto

2015

Robotics and Autonomous Systems

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We present a decision theoretic approach to mobile robot exploration. The method evaluates the reduction of joint path and map entropy and computes a potential information field in robot configuration space using these joint entropy reduction estimates. The exploration trajectory is computed descending on the gradient of these field. The technique uses Pose SLAM as its estimation backbone. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction for each sensor ray, and for each possible robot orientation, taking frontiers and obstacles into account. In the end, the computation of this field on the entire configuration space is shown to be very efficient computationally. The approach is tested in simulations in a pair of publicly available datasets comparing favorably both in quality of estimates and in execution time against an RRT*-based search for the nearest frontier and also against a locally optimal exploration strategy.

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Planning under uncertainty using model predictive control for information gathering

Cited by 59 publications

References 12 publications

Piecewise constant model predictive control for autonomous helicopters

Piecewise constant model predictive control for autonomous helicopters

Action Selection for Single-Camera SLAM

Potential information fields for mobile robot exploration

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