Evolution of fin undulation on a physical knifefish-inspired soft robot

Abstract

Soft robotics is a growing field of research and one of its challenges is how to efficiently design a controller for a soft morphology. This paper presents a marine soft robot inspired by the ghost knifefish that swims on the water surface by using an undulating fin underneath its body. We investigate how propagating wave functions can be evolved and how these affect the swimming performance of the robot. The fin and body of the robot are constructed from silicone and six wooden fin rays actuated by servo motors. In order to bypass the reality gap, which would necessitate a complex simulation of the fish, we implemented a Covariance Matrix Adaptation Evolution Strategy (CMA-ES) directly on the physical robot to optimize its controller for travel speed. Our results show that evolving a simple sine wave or a Fourier series can generate controllers that outperform a hand programmed controller. The results additionally demonstrate that the best evolved controllers share similarities with the undulation patterns of actual knifefish. Based on these results we suggest that evolution on physical robots is promising for future application in optimizing behaviors of soft robots.

Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

“…Online controller learning is relatively easy to implement with physical experimentation, as the morphology remains fixed, and it has been applied successfully for locomotion [53,85] and a soft fish robot [123]. Other studies have co-optimized control and morphology in the real world.…”

“…In simulation they have been shown to effectively optimize a range of virtual agents [117], including ones for locomotion [9,24] or navigating a confined space [8]. EAs have also been demonstrated in the real world, evolving modular robots for locomotion [3,126] and for controlling a soft fish robot [123].…”

“…Recent developments in automated [42] and scalable [59] fabrication approaches facilitate an increasingly large number of real-world evaluations. Similarly, modular and adaptive robots allow multiple morphologies and controllers to be tested on a single reconfigurable platform [85,123]. By situating physically embodied robots within a task environment, significant amounts of useful experimental data can be gathered.…”

Automatic Generation of Locomotion Patterns for Soft Modular Reconfigurable Robots

“…Veenstra et al [22] managed to skip the "reality gap" by evolving the parameters of the controller in a real physical prototype. They designed a knifefish-like soft robot which uses a servo motor to drive the silica gel fish fin, and evolved the control parameters of the servo motor by CMA-ES.…”

“…Previous researchers [22,28] put forward that computational requirements scale proportionally to the amount of voxels simulated, usually exponentially, increasing the computational power needed when more are used, and that a very precise physical model may not be required for the purpose of gait optimization. Therefore, we wanted to use as few voxels as possible to construct our soft robot model to ensure computational efficiency, though of course under the premise that the appearance and deformation are almost consistent with the actual soft cube, as shown in Figure 4.…”