A Fuzzy Adaptive Approach to Decoupled Visual Servoing for a Wheeled Mobile Robot

Shi, Haobin; Meng, Xu; Hwang, Kao-Shing

doi:10.1109/tfuzz.2019.2931219

Cited by 47 publications

(20 citation statements)

References 41 publications

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“…In a specific battlefield environment, the existence of some obstacles is inevitable. An accurate obstacle avoidance algorithm is a prerequisite for effective flight, and the reinforcement learning algorithm may provide a good solution [34][35][36]. We will investigate cooperative mission planning in-flight scenarios with obstacles.…”

Section: Discussionmentioning

confidence: 99%

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

2020

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Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchical decision-making perspective. From the view of the actual mission planning issue, the two key problems to be solved in UAV collaborative mission planning are mission allocation and route planning. In this paper, both of these problems are taken into account via a hierarchical decision-making model. Firstly, we use a target clustering algorithm to divide the original targets into target subgroups, where each target subgroup contains multiple targets. Secondly, a fuzzy ant colony algorithm is used to calculate the global path between target subgroups for a single-target group. Thirdly, a fuzzy ant colony algorithm is also used to calculate the local path between multiple targets for a single-target subgroup. After three levels of decision-making, the complete path for multiple UAVs can be obtained. In order to improve the efficiency of a collaborative task between different types of UAVs, a cooperative communication strategy is developed, which can reduce the number of UAVs performing tasks. Finally, experimental results demonstrate the effectiveness of the proposed cooperative mission planning and cooperative communication strategy for multiple UAVs.

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Section: Discussionmentioning

confidence: 99%

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

2020

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“…For Markov dynamic systems, the stochastic control problems are modeled as Semi-Markov decision processes (SMDPs) [26,27]. The time cost for the RL system to transit from one state to the next state is defined as the sojourn time.…”

Section: An Improved Q-learning Methods In Semi-markov Decision Processesmentioning

confidence: 99%

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Meng

2019

Information

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Multi-Robot Confrontation on physics-based simulators is a complex and time-consuming task, but simulators are required to evaluate the performance of the advanced algorithms. Recently, a few advanced algorithms have been able to produce considerably complex levels in the context of the robot confrontation system when the agents are facing multiple opponents. Meanwhile, the current confrontation decision-making system suffers from difficulties in optimization and generalization. In this paper, a fuzzy reinforcement learning (RL) and the curriculum transfer learning are applied to the micromanagement for robot confrontation system. Firstly, an improved Qlearning in the semi-Markov decision-making process is designed to train the agent and an efficient RL model is defined to avoid the curse of dimensionality. Secondly, a multi-agent RL algorithm with parameter sharing is proposed to train the agents. We use a neural network with adaptive momentum acceleration as a function approximator to estimate the state-action function. Then, a method of fuzzy logic is used to regulate the learning rate of RL. Thirdly, a curriculum transfer learning method is used to extend the RL model to more difficult scenarios, which ensures the generalization of the decision-making system. The experimental results show that the proposed method is effective.

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“…e x x  is the tracking error, C is the positive constant vector. Substituting (14) into (13) then the following result is obtained: e Ce (15) Because C is positive then the closed loop is asymptotically stable.…”

Section: Adaptive Controller Design For Nonlinear Mismatched Disturbamentioning

confidence: 99%

“…By using a fuzzy logic system (FLS) to approximate the model and an adaptive fuzzy observer to estimate the unmeasured velocities, the position errors are minimized and the response of the system is fast. The FLSs are also used for the WMR systems in others researches [15][16][17][18]. The problems of wheel slips are also considered by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive controller for wheel mobile robot with disturbances and wheel slips

Ong

Trinh

et al. 2021

IJECE

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In this paper an observer based adaptive control algorithm is built for wheel mobile robot (WMR) with considering the system uncertainties, input disturbances, and wheel slips. Firstly, the model of the kinematic and dynamic loops is shown with presence of the disturbances and system uncertainties. Next, the adaptive controller for nonlinear mismatched disturbance systems based on the disturbances observer is presented in detail. The controller includes two parts, the first one is for the stability purpose and the later is for the disturbances compensation. After that this control scheme is applied for both two loops of the system. In this paper, the stability of the closed system which consists of two control loops and the convergence of the observers is mathematically analysed based on the Lyapunov theory. Moreover, the proposed model does not require the complex calculation so it is easy for the implementation. Finally, the simulation model is built for presented method and the existed one to verify the correctness and the effectiveness of the proposed scheme. The simulation results show that the introduced controller gives the good performances even that the desired trajectory is complicated and the working condition is hard.

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A Fuzzy Adaptive Approach to Decoupled Visual Servoing for a Wheeled Mobile Robot

Cited by 47 publications

References 41 publications

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

Fuzzy Reinforcement Learning and Curriculum Transfer Learning for Micromanagement in Multi-Robot Confrontation

Robust adaptive controller for wheel mobile robot with disturbances and wheel slips

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