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This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.

Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.

Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.

Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.

It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.

Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.

The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

The purpose of this paper is to investigate how to make powered‐wheelchair driving easier using simple expert systems to interpret joystick and ultrasonic sensor data. The expert systems interpret shaky joystick movement and identify potentially hazardous situations and then recommend safe courses of action.

The way that a human user interacts with a powered‐wheelchair is investigated. Some simple expert systems are presented that interpret hand tremor and provide joystick position signals for an ultrasonic sensor system. Results are presented from a series of timed tasks completed by users using a joystick to control a powered‐wheelchair. Effect on the efficiency of driving a powered‐wheelchair is measured using the times to drive through progressively more complicated courses. Drivers completed tests both with and without sensors and the most recently published systems are used to compare results.

The new expert systems consistently out‐performed the most recently published systems. A minor secondary result was that in simple environments, wheelchair drivers tended to perform better without any sensor system to assist them but in more complicated environments then they performed better with the sensor systems.

The time taken for a powered‐wheelchair to move from one place to another partly depends on how a human user interacts with the powered‐wheelchair. Wheelchair driving relies heavily on visual feedback and the experience of the drivers. Although attempts were made to remove variation in skill levels by using sets of data associated with each driver and then using paired statistical tests on those sets, some variation must still be present.

The paper presents new systems that could allow more people to use powered‐wheelchairs and also suggests that the amount of sensor support should be varied depending on circumstances.

The new systems described in the paper consistently performed driving tasks more quickly than the most recently published systems.

The purpose of this paper is to describe the use of simple expert systems to improve the performance of tele‐operated mobile robots and ultrasonic sensor systems. The expert systems interpret data from the joystick and sensors and identify potentially hazardous situations and then recommend safe courses of action so that tele‐operated mobile‐robot tasks can be completed more quickly.

The speed of a tele‐operator in completing progressively more complicated driving tasks is investigated while using a simple expert system. Tele‐operators were timed completing a series of tasks using a joystick to control a mobile robot through a simple expert system that assisted them with driving the robot while using ultrasonic sensors to avoid obstacles. They either watched the robot while operating it or sat at a computer and viewed scenes remotely on a screen from a camera mounted on the robot. Tele‐operators completed tests with the simple expert system and the sensors connected. The system used an umbilical cable to connect to the robot.

The simple expert systems consistently performed faster than the other systems. Results are compared with the most recently published results and show a significant improvement. In addition, in simple environments, tele‐operators performed better without a sensor system to assist them but in more complicated environments than tele‐operators performed better with the sensor systems to assist.

Simple expert systems are shown to improve the operation of a tele‐operated mobile robot with an obstacle avoidance systems fitted.

Tele‐operated systems rely heavily on visual feedback and experienced operators. This paper investigates how to make tasks easier. Simple expert systems are shown to improve the operation of a tele‐operated mobile robot. The paper also suggests that the amount of sensor support should be varied depending on circumstances.

The simple expert systems are shown in this paper to improve the operation of a tele‐operated mobile robot. Tele‐operators completed tests with the simple expert system and the sensors connected. The results are compared with a tele‐operator driving a mobile robot without any assistance from the expert systems or sensors and they show a significant improvement.