Perceptual ambiguity maps for robot localizability with range perception

Ruiz-Mayor, Antonio; Crespo, Juan-Carlos; Triviño, Gracián

doi:10.1016/j.eswa.2017.05.015

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Let

be the observation data captured by the robot-mounted sensors at the pose

. Considering an input pose

as the independent variable, the likelihood function for sensor-based localization can be defined as [ 21 ]

…”

Section: Energy Model and Localizability Measurementioning

confidence: 99%

“…The early effort of this is presented in [ 20 ], where the authors first provide theoretical analysis for localization accuracy based on Cramér–Rao Bound. An ambiguity model of the indistinguishability is presented to formalize and quantify the perceptual ambiguity in the static environment [ 21 ]. In [ 22 ], this paper proposes a novel method for real-time localizability estimation by analyzing the constraints in each direction to predict localization performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

Zhang

Huang

Rong

et al. 2021

Sensors

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With the rapid development of robotics, wheeled mobile robots are widely used in smart factories to perform navigation tasks. In this paper, an optimal trajectory planning method based on an improved dolphin swarm algorithm is proposed to balance localization uncertainty and energy efficiency, such that a minimum total cost trajectory is obtained for wheeled mobile robots. Since environmental information has different effects on the robot localization process at different positions, a novel localizability measure method based on the likelihood function is presented to explicitly quantify the localization ability of the robot over a prior map. To generate the robot trajectory, we incorporate localizability and energy efficiency criteria into the parameterized trajectory as the cost function. In terms of trajectory optimization issues, an improved dolphin swarm algorithm is then proposed to generate better localization performance and more energy efficiency trajectories. It utilizes the proposed adaptive step strategy and learning strategy to minimize the cost function during the robot motions. Simulations are carried out in various autonomous navigation scenarios to validate the efficiency of the proposed trajectory planning method. Experiments are performed on the prototype “Forbot” four-wheel independently driven-steered mobile robot; the results demonstrate that the proposed method effectively improves energy efficiency while reducing localization errors along the generated trajectory.

show abstract

“…Let

be the observation data captured by the robot-mounted sensors at the pose

. Considering an input pose

as the independent variable, the likelihood function for sensor-based localization can be defined as [ 21 ]

…”

Section: Energy Model and Localizability Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

Zhang

Huang

Rong

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Qian et al proposed a localizability estimation method by introducing a factor of influence of dynamic obstacles [26], while the localizability matrices in use were derived from the work of Wang et al [28]. A more recent effort on localizability was proposed by Ruiz-Mayor et al where a probabilistic model of the indistinguishability for perception with different kinds of range sensors was proposed to estimate the perceptual ambiguity [29]. Weikun Zhen et al also proposed a new method to evaluate the localizability of a given 3D map [30].…”

Section: B Background Of Localizability In Path Planningmentioning

confidence: 99%

A New Path Evaluation Method for Path Planning With Localizability

Gao

Liu

et al. 2019

IEEE Access

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As a fundamental capability of mobile robots, path planning highly relies on the accurate localization of the robot. However, limited consideration for the localizability (which describes the capability of acquiring accurate localization) has been made in path planning. This brings a high risk of choosing a path that is optimal but results in the robot easily getting lost. There exist two key challenges to address this problem: 1) How to evaluate the localizability of a path and its impact on path planning. 2) How to balance the localizability of a path and the standard path planning criteria (e.g., shortest travel distance, obstacle-free path, etc.. To overcome the two challenges a new path evaluation method is required. So we first analyzed the uncertainty that comes from dead-reckoning and map matching. Then the localizability was estimated by the fusion of the uncertainty coming from both of them. Based on that, the impact of the localizability on the path planning task has been evaluated by an evaluation function. By combining the localizability evaluation function with traditional criteria (e.g., shortest length, obstacle-free path, etc.), a new path evaluation function for path planning is established. Both simulation and experimental studies show that the new path evaluation function can offer a balance between the localizability and the traditional criteria for path planning.

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A Prediction Method of Localizability Based on Deep Learning

Gao

Wang

et al. 2020

IEEE Access

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Perceptual ambiguity maps for robot localizability with range perception

Cited by 6 publications

References 28 publications

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

Optimal Trajectory Planning for Wheeled Mobile Robots under Localization Uncertainty and Energy Efficiency Constraints

A New Path Evaluation Method for Path Planning With Localizability

A Prediction Method of Localizability Based on Deep Learning

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