Bionic robot architecture
Robot architecture refers to the structural design of one or more robots in terms of information processing and control logic to accomplish a specified goal.
1. Decompose based on function
Functional decomposition-based architectures in artificial intelligence belong to the traditional deliberative intelligence model. Structurally, they are serially distributed, and in terms of execution, they are asynchronous, processing and controlling information according to a "perception-planning-action" pattern. The NASR [MtI] proposed by NASA and the National Institute of Standards and Technology (NIST) is a typical example. The advantages of this architecture are clear system functions, well-defined hierarchies, and simple implementation. However, the asynchronous processing significantly prolongs the system's response time to external events, and changes in the environment necessitate replanning, thus reducing execution efficiency. Therefore, it is only suitable for performing relatively complex tasks in known structured environments.
2. Decomposition based on behavior
In artificial intelligence, behavior-based architectures belong to the category of modern reactive intelligence. Structurally, they are characterized by parallel (inclusive) distribution, while in execution, they are synchronous, meaning information processing and control are performed in parallel according to a "perception-action" model. Typical examples include the behavior-layered inclusive architecture proposed by R.A. Brooks at MIT and the Motor Schemistry-based architecture proposed by Arkin. Their main advantages are short execution time, high efficiency, and strong flexibility. However, due to a lack of overall management, they are difficult to adapt to various situations. Therefore, they are only suitable for performing relatively simple tasks in a virtual environment.
3. Decomposition based on intelligent distribution
The distributed intelligence architecture represents the latest form of distributed intelligence in artificial intelligence. Structurally, it is distributed across multiple agents, while in execution, it operates collaboratively. It can solve individual local problems independently, or collaboratively solve one or more global problems. Multi-agent architectures are a typical example. The advantage of this architecture is that it possesses both the characteristics of "distributed intelligence" and a unified coordination mechanism. However, how to rationally divide and coordinate among the agents still requires extensive research and practice. This architecture has been widely applied in many large-scale intelligent information processing systems.
Besides the three main architectures mentioned above, there are also some improved hybrid architectures, such as behavior decomposition patterns with feedback loops, hierarchical architectures based on distributed intelligence, and multi-agent structures based on functional decomposition. However, overall, they either lack flexibility and scalability in functional modules, fail to coordinate deliberative intelligence and reactive intelligence effectively, or have imperfect communication mechanisms between different layers.
