From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

An Embodied Intelligence-Based Biologically Inspired Strategy for Searching a Moving Target

Bacterial chemotaxis in unicellular Escherichia coli, the simplest biological creature, enables it to perform effective searching behaviour even with a single sensor, achieved via a sequence of "tumbling" and "swimming" behaviours guided by gradient information. Recent studies show that suitable random walk strategies may guide the behaviour in the absence of gradient information. This article presents a novel and minimalistic biologically inspired search strategy inspired by bacterial chemotaxis and embodied intelligence concept: a concept stating that intelligent behaviour is a result of the interaction among the "brain," body morphology including the sensory sensitivity tuned by the morphology, and the environment. Specifically, we present bacterial chemotaxis inspired searching behaviour with and without gradient information based on biological fluctuation framework: a mathematical framework that explains how biological creatures utilize noises in their behaviour. Via extensive simulation of a single sensor mobile robot that searches for a moving target, we will demonstrate how the effectiveness of the search depends on the sensory sensitivity and the inherent random walk strategies produced by the brain of the robot, comprising Ballistic, Levy, Brownian, and Stationary search. The result demonstrates the importance of embodied intelligence even in a behaviour inspired by the simplest creature.

Handover between Vehicular Network Providers Using Bioinspired Attractor Selection Technique

This study looks at how to describe vehicular network connectivity using MATLAB simulation of an E. coli biological system. The model takes advantage of E. coli’s ability to adapt to environmental changes and variations by having stable states, which allows E. coli to continue surviving and developing, and uses this property to improve vehicular network connectivity to network service providers. As a result, an adaptive response to network pattern changes in terms of signal quality and stability can be obtained, as well as acceptable levels of connectivity in a changing environment via a handover mechanism. After applying it to four distinct networks, this probability-based technique was shown to work through simulation. The four networks used successfully completed the handover and maintained connectivity at various threshold levels. The impact of signal threshold variations and network sensitivity in reaction to surroundings on the handover process is also discussed in the study. By introducing two new physiologically based metrics (threshold and sensitivity), the goal of delivering Quality of Service (QoS) is realized. The used adaptive biological model allows for the selection probability λ to change according to the number of participating networks under certain environmental conditions. Noise effect is also discussed in the presented work as it affects network signals but does not affect the adaptive handover process, due to the built intelligence of the bio-inspired model.

Muscle Coordination Control for an Asymmetrically Antagonistic-Driven Musculoskeletal Robot Using Attractor Selection

Recently, numerous musculoskeletal robots have been developed to realize the flexibility and dexterity analogous to human beings and animals. However, because the arrangement of many actuators is complex, the design of the control system for the robot is difficult and challenging. We believe that control methods inspired by living things are important in the development of the control systems for musculoskeletal robots. In this study, we propose a muscle coordination control method using attractor selection, a biologically inspired search method, for an antagonistic-driven musculoskeletal robot in which various muscles (monoarticular muscles and a polyarticular muscle) are arranged asymmetrically. First, muscle coordination control models for the musculoskeletal robot are built using virtual antagonistic muscle structures with a virtually symmetric muscle arrangement. Next, the attractor selection is applied to the control model and subsequently applied to the previous control model without muscle coordination to compare the control model's performance. Finally, position control experiments are conducted, and the effectiveness of the proposed muscle coordination control and the virtual antagonistic muscle structure is evaluated.