We propose a multi-agent vision-based architecture to solve complex sensor-based planning tasks. A test bed implementation, with skills such as vision and collision avoidance, was used to run experiments in the proposed architecture. We demonstrate experimentally how the system can execute successfully complex assembly plans while dealing with unpredictable events and imprecise information, with no significant cost in run-time efficiency. Such experiments provided important insights about vision and planning and on how to build real world robotic systems.
Purposive vision; multi-agent architecture; planning and execution; deliberative planning and reactive planning
