Introducing MantisBot: Hexapod robot controlled by a high-fidelity, real-time neural simulation

Abstract

We present MantisBot, a 28 degree of freedom robot controlled by a high-fidelity neural simulation. It is modeled after the mantis, with many degrees of freedom, because we intend to study directed behaviors and leg multi-functionality, such as prey tracking and striking. As a first step, we present a distributed reflexive posture controller. MantisBot maintains posture through a series of reflexes observed in insects, specifically: strain measurements from a leg produce proportional torque commands (reflex A); large or rapidly decreasing leg strains produce a rapid, single “restep” (reflex B); a leg can only restep if its neighboring legs are all under strain (reflex C); and a leg will search for the ground if it does not reach it as expected (reflex D). All of these reflexes contribute to a hardware platform's posture, and are implemented in a highly distributed fashion. The two most distal joints in each leg each has its own central pattern generator (CPG, 12 total), upon which all of these behaviors depend. To achieve the desired dynamics, we implement a control network of conductance-based neurons with persistent sodium channels arranged in a network like the animal may possess in its thoracic ganglia. The result is a robot capable of actively maintaining posture without a centralized planner or body model. In addition, the network implementation is fast, calculating network dynamics 150 times faster than real time.