We study archetypal types of flyers and swimmers found in nature ranging from the microscale (pollen and bacteria) to the macroscale level (birds and eels). These forms serve for inspiration of engineering devices that can be in turn optimized using bioinspired algorithms.
To school, or not to school…
There has been a long-standing debate as to whether schooling fish reduce energy expenditure by adapting their swimming response to unsteady flow. This question has profound evolutionary significance, since any behavior that may lead to energy-savings can give a species an undeniable advantage over others that do not exploit this mechanism.
With the help of unsupervised machine learning algorithms, we have demonstrated that it is feasible to teach an artificial agent (a self-propelled fish-like swimmer) the capability to take adaptive decisions autonomously, so as to exploit energy deposited in the flow by an upstream swimmer. The ‘smart’ agent is able to minimize its own energy expenditure by interacting judiciously with the unsteady wake, while having no a-priori knowledge regarding details of the complex fluid phenomena involved.
Moreover, the agent explicitly chooses to pursue in the leader’s wake while attempting to maximize swimming-efficiency, although it is given no direct incentive to do so. This suggests that large groups of fish may indeed resort to schooling as a means of energy-saving. The results lay the groundwork for future robotic applications, where groups of robotic swimmers may attempt to maximize range and endurance by swimming in a coordinated manner, without having to depend upon complex (and potentially sub-optimal) hand-crafted rules.
Discovering the benefits of unsteady swimming
Steady, continuous swimming is rarely observed in most fish species. A large number adopt an intermittent form of locomotion referred to as `burst-and-coast’ swimming, where a few quick flicks of the tail are followed by a prolonged unpowered glide. This behavior is believed to confer energetic benefits, in addition to stabilizing the sensory field, and diminishing the wake-signature for predator-avoidance.
Unfortunately, these advantages may be offset by a reduction in average speed. We have coupled high-fidelity simulations with evolutionary-optimization algorithms to discover a range of intermittent-swimming patterns, the most efficient of which resemble the swimming-behavior of live fish. Importantly, the use of multi-objective optimization reveals locomotion patterns that strike the perfect balance between both speed and efficiency. Some of these patterns do not generally occur in nature, but can be invaluable for use in robotic applications. The resulting increase in range, endurance, and average speed can greatly enhance the mission capability of robotic swimmers.
Publications
2017
- G. Novati, S. Verma, D. Alexeev, D. Rossinelli, W. M. van Rees, and P. Koumoutsakos, “Synchronisation through learning for two self-propelled swimmers,” Bioinspir. Biomim., vol. 12, iss. 3, p. 36001, 2017.