Abstract
Contemporary robots perform energy intensive tasks -- e.g. manipulation and locomotion -- making the development of energy autonomous robots challenging. Since plants are primary energy producers in natural ecosystems, we took plants as a source of inspiration for designing our robotics platform. This led us to investigate energy autonomy in robots through employing solar panels. As plants move slowly compared to other large terrestrial organisms, it is expected that plant-inspired robots can enable robotic applications such as long-term monitoring and exploration where energy consumption could be minimized. Since it is difficult to manually design robotic systems that adheres to full energy autonomy, we utilize evolutionary algorithms to automate the design and evaluation of energy harvesting robots. We demonstrate how artificial evolution can lead to the design and control of a modular plant-like robot. Robotic phenotypes were acquired through implementing an evolutionary algorithm, a generative encoding and modular building blocks in a simulation environment. The generative encoding is based on a context sensitive Lindemayer-System (L-System) and the evolutionary algorithm is used to optimize compositions of heterogeneous modular building blocks in the simulation environment. Phenotypes that evolved from the simulation environment are in turn transferred to a physical robot platform. The robotics platform consists of five different types of modules: (1) a base module; (2) a cube module; (3) servo modules; and (4,5) two types of solar panel modules that are used to harvest energy. The control system for the platform is initially evolved in the simulation environment and afterwards transferred to an actual physical robot. A few experiments were done showing the relationship between energy cost and the amount of light tracking that evolved in the simulation. The reconfigurable modular robots are eventually used to harvest light with the possibility to be reconfigured based on the needs of the designer, the type of usable modules and/or the optimal configuration derived from the simulation environment. Long term energy autonomy has not been tested in this robotics platform. However, we think our robotics platform can serve as a stepping stone towards full energy autonomy in modular robots.
Highlights
Energy autonomy in artificial systems is beneficial for long-term autonomous behavior in single or multi-robot applications required for, e.g., monitoring and exploration
We have shown how an evolutionary algorithm can evolve light harvesting robots that can be transferred to the real world using a modular robotics approach
There is an advantage for evolutionary algorithms to exploit movement when only one solar panel module is simulated but not when more are simulated given the constrains of the simulator
Summary
Energy autonomy in artificial systems is beneficial for long-term autonomous behavior in single or multi-robot applications required for, e.g., monitoring and exploration. It can be complicated to design energy autonomous systems since this depends on the energy demand and energy acquisition of the robot. Though the same principles in evolutionary robotics can be implemented for energy autonomy in robotic systems, energy autonomy is usually implemented on different robotic systems where the robot is able to utilize energy from light (Noth et al, 2006; Afarulrazi et al, 2011) or microbial fuel cells (Ieropoulos et al, 2003; Philamore et al, 2015). Being able to automatically design robotic systems that are geared toward energy autonomy could give us unintuitive solutions that might be more effective than traditional solutions. Having a modular robotic system that conforms to energy optimization could give rise to self-reconfigurable robots that maximize energy acquisition
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