Browsing by Author "Naidoo, Nicol."

Now showing 1 - 2 of 2

The cooperation of heterogeneous mobile robot configurations in advanced manufacturing environments.
(2014) Naidoo, Nicol.; Bright, Glen.; Stopforth, Riaan.
Cooperation of Multiple Mobile Robot Systems (MMRS) have drawn increasing attention in recent years since these systems have the ability to perform complex tasks more efficiently compared to Single Mobile Robot Systems (SMRS). An implementation of a cooperative MMRS in a manufacturing environment can, for example, solve the issue of bottlenecks in a production line, whereas the limitations of a SMRS can lead to a lot of problems in terms of time wastage, loss of revenue, poor quality products and dissatisfied customers. The study of cooperation in heterogeneous robot teams has evolved due to the engineering and economic benefits attribute as well as the existence of diversities in homogeneous robot teams. The challenge of cooperation in these systems is a result of the task taxonomies and fundamental abilities of each robot in the team; there is therefore a need for an Artificial Intelligence (AI) system that processes these heterogeneities to facilitate robot cooperation. This dissertation focuses on the research, design and development of an artificial intelligence for a team of heterogeneous mobile robots. The application of the system was directed towards advanced manufacturing systems, however, it can be adapted to search and rescue tasks. An essential component of the AI design is the machine learning algorithm which was used to predict suitable goal destinations for each mobile robot, given a set of input parameters. Mobile robot autonomy was achieved through the development of an obstacle avoidance and navigation system. The AI was also interfaced to a Supervisory Control and Data Acquisition System (SCADA) which facilitates end-user interaction - a vital ingredient to manufacturing automation systems.
A distributed framework for the control and cooperation of heterogeneous mobile robots in smart factories.
(2017) Naidoo, Nicol.; Bright, Glen.; Stopforth, Riaan.
The present consumer market is driven by the mass customisation of products. Manufacturers are now challenged with the problem of not being able to capture market share and gain higher profits by producing large volumes of the same product to a mass market. Some businesses have implemented mass customisation manufacturing (MCM) techniques as a solution to this problem, where customised products are produced rapidly while keeping the costs at a mass production level. In addition to this, the arrival of the fourth industrial revolution (Industry 4.0) enables the possibility of establishing the decentralised intelligence of embedded devices to detect and respond to real-time variations in the MCM factory. One of the key pillars in the Industry 4.0, smart factory concept is Advanced Robotics. This includes cooperation and control within multiple heterogeneous robot networks, which increases flexibility in the smart factory and enables the ability to rapidly reconfigure systems to adapt to variations in consumer product demand. Another benefit in these systems is the reduction of production bottleneck conditions where robot services must be coordinated efficiently so that high levels of productivity are maintained. This study focuses on the research, design and development of a distributed framework that would aid researchers in implementing algorithms for controlling the task goals of heterogeneous mobile robots, to achieve robot cooperation and reduce bottlenecks in a production environment. The framework can be used as a toolkit by the end-user for developing advanced algorithms that can be simulated before being deployed in an actual system, thereby fast prototyping the system integration process. Keywords: Cooperation, heterogeneity, multiple mobile robots, Industry 4.0, smart factory, manufacturing, middleware, ROS, OPC, framework.