Mobile Robot Control and Navigation: A Global Overview

Tzafestas, Spyros G.

doi:10.1007/s10846-018-0805-9

Cited by 174 publications

(79 citation statements)

References 118 publications

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“…The robot discussed in this paper is a two-wheeled mobile robot. The kinematic model of the robot is described in detailed in [40,41]. The environment where the mobile robot works is always filled with unknown obstacles.…”

Section: Problem Statementmentioning

confidence: 99%

A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

Sun

Chen

2019

Electronics

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The obstacle avoidance control of mobile robots has been widely investigated for numerous practical applications. In this study, a control scheme is presented to deal with the problem of trajectory tracking while considering obstacle avoidance. The control scheme is simplified into two controllers. First, an existing trajectory tracking controller is used to track. Next, to avoid the possible obstacles in the environment, an obstacle avoidance controller, which is used to determine the fastest collision avoidance direction to follow the boundary of the obstacle at a constant distance, is proposed based on vector relationships between the robot and an obstacle. Two controllers combined via a switch strategy are switched to perform the task of trajectory tracking or obstacle avoidance. The stability of each controller in the control scheme is guaranteed by a Lyapunov function. Finally, several simulations are conducted to evaluate the proposed control scheme. The simulation results indicate that the proposed scheme can be applied to the mobile robot to ensure its safe movement in unknown obstacle environments.

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Section: Problem Statementmentioning

confidence: 99%

A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

Sun

Chen

2019

Electronics

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“…Two more recent surveys of path planning research can also be found in the papers proposed by Mac et al [20] and Tzafestas [21]. The first survey mainly focuses on heuristic approaches and the second offers broader coverage to almost all related research topics.…”

Section: A Background Of 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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“…In recent years, the relative concept of intelligence has been paid great attention due to the simulation of human intelligence [ 19 , 20 ]. As a result, it is reasonable to model experts’ uncertainty in the process of decision-making in FMEA, which is important to improve the intelligent degree of the evaluation system.…”

Section: Introductionmentioning

confidence: 99%

An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy

Liu

Xiao

2019

Entropy

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Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications. However, how to determine the weights of team members appropriately, with the impact factor of domain experts’ uncertainty in decision-making of FMEA, is still an open issue. In this paper, a new method to determine the weights of team members, which combines evidence theory, intuitionistic fuzzy sets (IFSs) and belief entropy, is proposed to analyze the failure modes. One of the advantages of the presented model is that the uncertainty of experts in the decision-making process is taken into consideration. The proposed method is data driven with objective and reasonable properties, which considers the risk of weights more completely. A numerical example is shown to illustrate the feasibility and availability of the proposed method.

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Mobile Robot Control and Navigation: A Global Overview

Cited by 174 publications

References 118 publications

A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

A Multi-Switching Tracking Control Scheme for Autonomous Mobile Robot in Unknown Obstacle Environments

A New Path Evaluation Method for Path Planning With Localizability

An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy

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