Information-based centralization of locomotion in animals and robots

Abstract

The centralization of locomotor control from weak and local coupling to strong and global is hard to assess outside of particular modeling frameworks. We developed an empirical, model-free measure of centralization that compares information between control signals and both global and local states. A second measure, co-information, quantifies the net redundancy in global and local control. We first validate that our measures predict centralization in simulations of phase-coupled oscillators. We then test how centralization changes with speed in freely running cockroaches. Surprisingly, across all speeds centralization is constant and muscle activity is more informative of the global kinematic state (the averages of all legs) than the local state of that muscle’s leg. Finally we use a legged robot to show that mechanical coupling alone can change the centralization of legged locomotion. The results of these systems span a design space of centralization and co-information for biological and robotic systems.

A Wearable Master–Slave Rehabilitation Robot Based on an Epidermal Array Electrode Sleeve and Multichannel Electromyography Network

“…[7] Physiologically, simple one-to-one communication between muscle groups cannot allow for enough degrees of freedom in the musculoskeletal system, and movement requires complicated interactions among the related muscles. [8][9][10][11] The coordination of multiple muscles forms a muscle network, and network analysis can provide a clear spatial pattern to reflect how information is exchanged among muscles. [12,13] Due to the reflection of muscle cooperation, an EMG network will have the potential to provide new discrimination features for neural prosthesis control and also provides feedback biomarkers to guide the development of a new rehabilitation system.…”

Autonomous mining through cooperative driving and operations enabled by parallel intelligence

“…All the involved machines need to timely negotiate and communicate with each other in a real-time manner so as to collaboratively finish one task, which precisely illustrates the characteristics of large-scale, heterogeneous, and complex task-oriented multi-robot systems. Multi-robot systems have been studied extensively 11,12 , with research topics spanning from self-organizing bionic clusters [13][14][15][16] to hierarchical multi-robot systems [17][18][19] . However, constructing such a complex robot system remains an unresolved problem 12,20 .…”