The classical Traveling Salesperson Problern (TSP) models the movements of a salesperson travelling through a nurnber of cities. The optimization problem is to choose the sequence in which to visit the cities in order to minimize the total distance travelled. This thesis addresses a multi-robot assembly planning problem which in essence is a TSP-type optimization problem. However, in this augmented TSP (TSP+), both the "salesperson" (a robot with a tool) as well as the "cities" (another robot with a workpiece) move. Namely, in addition to the sequencing of tasks, further planning is required to choose where the "salesperson" should rendezvous with each "city".

A generalized point-to-point (PTP) motion-planning technique is presented in this thesis for manufacturing systems with multiple, coordinated assembly robots that can be modelled as a TSP problem. As an example area, the optimization of the electroniccomponent placement process is addnssed. This TSP+ problem is investigated for single and two placement robots. Namely, the electronic-component placement machine comprises one or two placement robots, a moving XY-table (on which the printed circuit board (PCB) is fixnired), and two moving component delivery systems (CDSs). The use of a genetic algorithm (GA) is chosen as the search engine for the solution of the TSP+ optimization problem defined above.

The simulation tools developed within the frarnework of the thesis were tested on five diffennt component-placement system configurations. In the most generalized coafiguration, the placement robot meets the CDS at an optimal rendezvous location for the pick-up of the component and subsequently mats the PCB (on the mobile XY-table) at an optimal rendezvous location. In addition to the solution of the componentplacement sequencing problem and the rendezvous-point planning problem the collisionavoidance issue is addressed for the system configuration with two placement robots.

The proposed novel optimization rnethodology can facilitate the use of higher degrees of freedom in robotic assembly systems, so that substantial improvements in production times may be obtained. Its effectiveness is successfully shown herein via simulated assembly cases.