As robot technology improves, autonomous robots expect to work in place of humans. It is cost efficient when such autonomous robots are supposed to perform multiple tasks. This work deals with such multi-task robots and proposes a task selection technique for multi-task autonomous robots. When an autonomous robot is implemented for multiple purposes, it is important that the robot be able to appropriately select its own tasks. The proposed method, named collaborative task casting, takes degrees of task achievement into account and balances task occupation to ensure that adequate robot resources are directed towards each task. The experimental simulation results showed that the proposed method had advantage over existing methods in terms of minimizing unsatisfactory state and adaptively worked in many possible settings.
Kentaro Ishii, Michita Imai.
Collaborative Task Casting: A Task Selection Technique for Multi-Task Autonomous Robots.
SICE Journal of Control, Measurement, and System Integration, Vol.6, No.2, pp.157-165, March 2013.