Robot motion planning for sensor-based control with uncertainties

Page, Lance A.; Sanderson, Arthur C.

doi:10.1109/robot.1995.525464

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…Moreover, the landmarks perceived is the same in every subset of h i . This makes the partition defined by (18)- (19) unique.…”

Section: Limited Visibilitymentioning

confidence: 99%

“…In fact, given a robot orientation (k) ∈ h j , the feasible set (4) associated with the robot pose p(k) becomes Figure 3. where h j ∈ H(z) is an element of the partition (19). Clearly, the size of M ( j) ( p(k)) in (20) depends on h j , and hence on the partition subset h j in which the robot orientation lies, but it does not depend on the exact value (k) of the orientation itself.…”

Section: A Simplified Optimization Problemmentioning

confidence: 99%

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Path planning with uncertainty: A set membership approach

Ceccarelli

Marco

Garulli

et al. 2010

Adaptive Control & Signal

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The paper addresses the path planning problem in a set theoretic framework. The considered scenario is that of a mobile robot exploiting range and bearing measurements with respect to known landmarks to localize itself. By assuming unknown-but-bounded measurement noise, set membership localization techniques are used to estimate the uncertainty of each robot pose within the considered environment. The path planning problem is formulated and solved, with the aim of minimizing the total uncertainty associated with the travelled path. Practical issues such as limited sensory range and obstacle avoidance are taken into account. The proposed technique is validated via numerical simulations.

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“…Moreover, the landmarks perceived is the same in every subset of h i . This makes the partition defined by (18)- (19) unique.…”

Section: Limited Visibilitymentioning

confidence: 99%

Section: A Simplified Optimization Problemmentioning

confidence: 99%

Path planning with uncertainty: A set membership approach

Ceccarelli

Marco

Garulli

et al. 2010

Adaptive Control & Signal

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“…In chronological order, the main family of approaches in the literature have been pre-image back-chaining [17], [13], [18]; sensory uncertainty fields [8], [19], [20], [21]; sensorbased planning [22], [4], [23]; the Information Space approach [10]; the set-membership approach [24], [5]; stochastic dynamic programming [15], [25]; and classical search strategies in an extended space [12], [1], [9], [26], [27]. The latter is the family to which this paper belongs.…”

Section: Minimizing the Expectation Of A Functional Of Q(t)mentioning

confidence: 99%

A Bayesian framework for optimal motion planning with uncertainty

Censi

Calisi

Luca

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to pathplanning in the extended space of poses × covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state.

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“…The precise determination of an attractive domain around each point of the reference path, where the robot could possibly lie during the path following process, is necessary for planning safe trajectories. Indeed, whereas previous works considered the propagation of estimation errors to design safe open-loop trajectories [3][8] [13][15] such an approach did not analyze the robustness of feedback controller with respect to localization errors. As feedback control inputs are the estimated value instead of the exact ones, the use of robust control laws to achieve stabilization process appears to be essential.…”

Section: Resultsmentioning

confidence: 99%

“…The same principle of fusion has been used in [8], where a simple potential evolution function (travel distance dependent), manages fusion between odometry and contact sensors (or telemetric sensors). In the case of Page [13], fusion is extended to vision. Indeed, both fix cameras looking at robot and landmarks (walls) generate a zone with, in each point of this zone, an uncertainty area.…”

Section: Introductionmentioning

confidence: 99%

A safe and robust path following planner for wheeled robots

Lambert

Hamel²,

Fort-Piat³

Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Appl

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This paper addresses the problem of planning a path following, robust with respect t o small localization errors, allowing a mobile robot t o reach safely its goal.A path planning method dealing with uncertainties is proposed where both uncertainty in localization and in control of a non-holonomic mobile robot are managed. The safeness of this method i s due t o the combination of the planning phase and the navigation phase by using the same localization and control processes. The localization process realized the multisensor fusion of data provided by a ring of telemetric sensors in combination with a dead-reckoning system. The robustness is due t o a stabilizing controller o n which the system remains stable when estimated values are used for feedback instead of exact ones. B y determining a compact attractive domain bounding the regulation error, we dynamically compute a security margin during the path planning. This security margin ensures the safeness of the planned path.

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Robot motion planning for sensor-based control with uncertainties

Cited by 13 publications

References 18 publications

Path planning with uncertainty: A set membership approach

Path planning with uncertainty: A set membership approach

A Bayesian framework for optimal motion planning with uncertainty

A safe and robust path following planner for wheeled robots

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