Collective systems play very important role on Earth, and we encounter them in all sizes, scales and forms; in biological and technological areas; in ocean, air and on the ground. Examples include viruses, different colloidal systems, nano- and micro-scale particles, huge world of social insects and animals; collective systems in robotics vary from nano- up to large space exploration robots. To some extent, collective systems are ubiquitous. Such a prevalence and diversity and can be explained by several unique properties: scalability, reliability, flexibility, self-developmental capabilities. This guest lecture introduces the area of collective robotics and answers the questions “what and why”. Special attention is given to reconfigurable robotics, we discuses a big vision of “universal modularity” and several ways of its achieving.
“The whole is greater than the sum of its parts” — a catch phrase that aptly expresses the Distributed Flight Array: a modular robot consisting of hexagonal-shaped single-rotor units that can take on just about any shape or form. Although each unit is capable of generating enough thrust to lift itself off the ground, on its own it is incapable of flight much like a helicopter cannot fly without its tail rotor. However, when joined together, these units evolve into a sophisticated multi-rotor system capable of coordinated flight and much more.
In Modular Space Robotics, modules self-assemble while in orbit to create larger satellites for specific missions. Modular satellites have the potential to reduce mission costs (small satellites are cheaper to launch), increase reliability, and enable on-orbit repair and refueling. Each of the modules has its load of sensors, fuel and attitude control actuators (thrusters). Assembled modules therefore have redundant sensor and actuation capabilities. By fusing sensor data, the modular satellites can follow its trajectory more precisely and smart thruster activation can help save fuel.
The challenge is to figure out how to control such a self-assembled robot to minimize fuel consumption while balancing fuel distribution and improve trajectory following. To this end, Toglia et al. propose a cooperative controller where one of the modules, with information about the configuration of all other modules, is responsible for computing an optimal control schema. An extended Kalman-Bucy Filter is used to implement sensor fusion.
The cooperative controller was compared to an independent controller where each module attempts to follow its own trajectory while minimizing its own fuel usage and trajectory errors. Results from simulation and reality show that the cooperative controller can save significant amounts of fuel, up to 43% in one experiment, while making the trajectories more precise.
Experiments in reality were performed with two satellites using the MIT Field and Space Robotics Laboratory Free-Flying Space Robot Test Bed shown below.
In today’s episode we focus on modular robotics, or robots assembled out of many smaller modules. Whether all the modules are the same (‘homogeneous’) or of different types (‘heterogeneous’), modular robots can accomplish many different tasks simply by adjusting their configuration. We speak with two experts in the field, Kasper Støy from Denmark and Robert Fitch from Australia.