Certain open issues challenge the software engineering of autonomous robot software (ARS). One issue is to provide enabling software technologies to support autonomous and rational behaviours of robots operating in an open environment, and another issue is the development of an effective engineering approach to manage the complexity of ARS to simplify the development, deployment and evolution of ARS. We introduce the software framework AutoRobot to address these issues. This software provides abstraction and a model of accompanying behaviours to formulate the behaviour patterns of autonomous robots and enrich the coherence between task behaviours and observation behaviours, thereby improving the capabilities of obtaining and using the feedback regarding the changes. A dual-loop control model is presented to support flexible interactions among the control activities to support continuous adjustments of the robot's behaviours. A multi-agent software architecture is proposed to encapsulate the fundamental software components. Unlike most existing research, in AutoRobot, the ARS is designed as a multi-agent system in which the software agents interact and cooperate with each other to accomplish the robot's task. AutoRobot provides reusable software packages to support the development of ARS and infrastructure integrated with ROS to support the decentralized deployment and running of ARS. We develop an ARS sample to illustrate how to use the framework and validate its effectiveness.

Robotic systems operating in the real-world have to cope with unforeseen events by determining appropriate decisions based on noisy or partial knowledge. In this respect high functional robots are equipped with many sensors and actuators and run multiple processing modules in parallel. The resulting complexity is even further increased in case of cooperative multi-robot systems, since mechanisms for joint operation are needed. In this paper a complete and modular framework that handles this complexity in multi-robot systems is presented. It provides efficient exchange of generated data as well as a generic scheme for task execution and robot coordination.

Incremental evolution with learning (IEWL) is proposed for the development of autonomous robots, and the validity of the method is evaluated with a real mobile robot to acquire a complex task. Development of the control system for a complex task, i.e., approaching toward a target object by avoiding obstacles in an environment, is incrementally carried out in two-stage. In the first-stage, controllers are developed to avoid obstacles in the environment. By using acquired knowledge of the first-stage, controllers are developed in the second-stage to approach toward the target object by avoiding obstacles in the environment. It is found that the use of learning in conjunction with incremental evolution is beneficial for maintaining diversity in the evolving population. The performances of two controllers, one developed by IEWL and the other developed by incremental evolution without learning (IENL), are compared on the given task. The experimental results show that robust performance is achieved when controllers are developed by IEWL.

This paper presents the concepts and methodology of knowledge-based information modeling based on Cognitive Science for realizing the autonomous humanoid service robotic arm and hand system HARIS. The HARIS robotic system consists of model-based 3D vision, intelligent scheduler, computerized arm/hand controller, humanoid HARIS arm/hand unit and human interface, and aims to serve the aged and disabled on desk-top object manipulations. The world model, i.e., a shared knowledge base, is introduced to work as a communication channel among the software modules. The task scheduling as well as the 3D-vision is based on Cognitive Science, i.e., a human's way of vision and scheduling is considered in designing the knowledge-based software system. The key idea is to use "words" in describing a scene, scheduling tasks, controlling an arm and hand, and interacting with a human. The world model plays a key role in fusing a variety of distributed functions. The generalized frame-based knowledge engineering environment ZERO++ has been effectively used as a software platform in implementing the system. The experimental system is working within a limited situation successfully. Through the introduction of Cognitive Science-based information modeling we have learned useful hints for realizing human-robot symbiosis, that is our long term goal of the project.