Markovian robots: Minimal navigation strategies for active particles.

Area of Science:

• Robotics and Autonomous Systems
• Statistical Physics
• Complex Systems

Background:

• Active particles and self-propelled agents are crucial in various scientific fields.
• Navigation in dynamic and complex external fields presents significant challenges for autonomous systems.
• Existing navigation strategies often require complex internal states or memory.

Purpose of the Study:

• To introduce and explore minimal navigation strategies for active particles in complex, dynamical fields.
• To develop a class of autonomous, self-propelled particles called Markovian robots (MR).
• To identify the minimum design requirements for navigation control systems (NCS) to perform complex tasks.

Main Methods:

• Designing a navigation control system (NCS) with a Boolean internal state governed by a Markov chain.
• Analyzing the temporal dynamics of the Boolean variable with transition rates dependent on local external field values.
• Developing an effective description of long-time motility behavior by reducing NCS dynamics complexity.
• Assembling a proof-of-concept robot utilizing the minimalistic NCS.

Main Results:

• Demonstrated that Markovian robots can exhibit nontrivial motility behaviors in 1, 2, and 3 dimensions.
• Identified minimum NCS design requirements for tasks like adaptive gradient following and value selection.
• Showcased the robustness of Markovian robots in navigating complex information landscapes.
• Validated the concept of navigation using a simple NCS with a one-bit internal state.

Conclusions:

• Minimalistic navigation control systems are sufficient for complex navigation tasks in dynamical fields.
• Markovian robots offer a viable paradigm for engineering miniaturized robots with advanced capabilities.
• The proposed framework provides insights into the fundamental principles of autonomous navigation and control.