Optimally efficient swimming in hyper-redundant mechanisms: control, design, and energy recovery

Abstract

Hyper-redundant mechanisms (HRMs), also known as snake-like robots, are highly adaptable during locomotion on land. Researchers are currently working to extend their capabilities to aquatic environments through biomimetic undulatory propulsion. In addition to increasing the versatility of the system, truly biomimetic swimming could also provide excellent locomotion efficiency. Unfortunately, the complexity of the system precludes the development of a functional solution to achieve this. To explore this problem, a rapid optimization process is used to generate efficient HRM swimming gaits. The low computational cost of the approach allows for multiple optimizations over a broad range of system conditions. By observing how these conditions affect optimal kinematics, a number of new insights are developed regarding undulatory swimming in robotic systems. Two key conditions are varied within the study, swimming speed and energy recovery. It is found that the swimmer mimics the speed control behaviour of natural fish and that energy recovery drastically increases the system's efficiency. Remarkably, this efficiency increase is accompanied by a distinct change in swimming kinematics. With energy recovery, the swimmer converges to a clearly anguilliform gait, without, it tends towards the carangiform mode.

Multi-objective optimization for efficient motion of underwater snake robots

“…As has been noted in the bio-robotics community, underwater snake robots bring a promising prospective to improve the efficiency and maneuverability of next generation underwater vehicles [1,11,30]. They have several promising applications for underwater exploration, monitoring, surveillance and inspection, and they carry a lot of potential for inspection of subsea oil and gas installations.…”

“…In [11,12], the authors propose a model of underwater snake robots, where the dynamic equations are written in closed form. Compared to the models in [1,4,21,24,27,30] it is an advantage from an analysis point of view that the model is in closed form, as opposed to including numerical evaluations of the drag effects. In addition, it is beneficial that it includes both resistive and reactive fluid forces, since swimming snake robots operate at Reynolds numbers that require both these effects to be taken into account.…”

“…Therefore, derivative free and stochastic methods, which have the ability to avoid local solutions, have received attention in problems with highly non-smooth objective functions containing multiple optima. Consequently, as also mentioned in [30], derivative free and stochastic methods are an appropriate choice for the motion optimization. Derivative free algorithms are adopted from researchers in the fields of swimming robots to investigate the efficiency of undulatory locomotion.…”

“…In [2] a probability-based reinforcement learning approach (PI 2 ) was applied to a land-based snake robot to learn the locomotion control parameters of the robot in order to generate motor primitives and locomotion. In [30], the total required energy was minimized by manipulating the gait parameters and keeping the velocity constant. A penalty term was introduced in the objective function to penalize any deviation of the velocity from its desired value.…”

“…A penalty term was introduced in the objective function to penalize any deviation of the velocity from its desired value. Therefore, a trade-off between the velocity and the required energy was not addressed in [30]. In this paper, however, we propose to minimize the energy and maximize the velocity, simultaneously.…”