Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Barto's model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system.

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A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance

Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots

This paper considers the problem of a robot navigating in a crowded or congested environment. A robot operating in such an environment can get easily blocked by moving humans and other objects. To deal with this problem it is proposed to attempt to predict the motion trajectory of humans and obstacles. Two kinds of prediction are considered: short-term and long-term. The short-term prediction refers to the one-step ahead prediction and the long-term to the prediction of the final destination point of the obstacle's movement. The robot movement is controlled by a partially observable Markov decision process (POMDP). POMDPs are utilized because of their ability to model information about the robot's location and sensory information in a probabilistic manner. The solution of a POMDP is computationally expensive and thus a hierarchical representation of POMDPs is used.

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Predictive autonomous robot navigation

This paper describes a vision-based system for inspections of underwater structures, e.g., pipelines, cables, etc., by an autonomous underwater vehicle (AUV). Usually underwater inspections are performed by remote operated vehicles (ROVs) driven by human operators placed in a support vessel. However, this task is often challenging, especially in conditions of poor visibility or in presence of strong currents. The system proposed allows the AUV to accomplish the task in autonomy. Moreover, the use of a three-dimensional (3-D) model of the environment and of an extended Kalman filter (EKF) allows the guidance and the control of the vehicle in real time. Experiments done on real underwater images have demonstrated the validity of the proposed method and its efficiency in the case of critical and complex situations.

Visual inspection of sea bottom structures by an autonomous underwater vehicle

Learning and adaptation of an intelligent mobile robot navigator operating in unstructured environment based on a novel online Fuzzy–Genetic system

In this paper, an alternative training approach to the EEM-based training method is presented and a fuzzy reactive navigation architecture is described. The new training method is 270 times faster in learning speed; and is only 4% of the learning cost of the EEM method. It also has very reliable convergence of learning; very high number of learned rules (98.8%); and high adaptability. Using the rule base learned from the new method, the proposed fuzzy reactive navigator fuses the obstacle avoidance behaviour and goal seeking behaviour to determine its control actions, where adaptability is achieved with the aid of an environment evaluator. A comparison of this navigator using the rule bases obtained from the new training method and the EEM method, shows that the new navigator guarantees a solution and its solution is more acceptable.

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An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning

Describes a fully integrated mobile vehicle simulator-EXPECTATIONS. The structure of the simulator is modular and object-oriented, where the virtual environment, static and dynamic objects and their interactions are hierarchically constructed. It supports 2D/3D real-time graphic rendering of the composite environment, which can be visualized on multiple X-windows in a time synchronized manner, in which vehicle or object movement can be animated in accordance with the calculation of the algorithms written in C/C++. Algorithms such as path planning, behavior learning, collision avoidance and navigation strategies can be 'plug-and-play' easily through the so called action decision interchange concept. Apart from providing a realistic visualization tool for development, it also supports fast algorithmic study and development, and the knowledge learnt through the simulation may potentially be used by the physical vehicle in real operations.

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N.H.C. Yung

Papers

An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning

EXPECTATIONS-an autonomous mobile vehicle simulator