A humanoid robot has uncertain sensor data, and produces larger errors arising from the control process than other types of robot having wheels. The humanoid robot for providing service should not only generate proper behaviors to accomplish goals in unstable environments, but also consider flexible scalability to reflect demanding user's requests. We propose a control system based on planning-driven behavior selection network so as to generate autonomous behaviors of the robot rapidly and suitably. In this paper, the behavior selection network, one of behavior-based methods, is modularized considering sub-goals. The STRIPS planning makes a sequence of robot behaviors automatically by controlling the modules. The proposed system can control the robot to cope with the various environments, as well as to achieve goals according to the user's demand. Moreover, since the BSN and STRIPS planning structures are internally independent, the proposed system is scalable flexibly to increasing user requests. We confirm the usability of the proposed system by performing several test scenarios with NAO robot. Experiments show that the proposed system is able to make a behavioral sequence to fulfill goals appropriately, and can create behaviors of the robot in the various situations. We can also confirm an accuracy of 85.7% through applying the proposed system in the real world.
|Title of host publication||Proceedings of the International Joint Conference on Neural Networks|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||7|
|Publication status||Published - 2014 Sept 3|
|Event||2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China|
Duration: 2014 Jul 6 → 2014 Jul 11
|Name||Proceedings of the International Joint Conference on Neural Networks|
|Other||2014 International Joint Conference on Neural Networks, IJCNN 2014|
|Period||14/7/6 → 14/7/11|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence