Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

Abstract

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Design and test of wirelessly powered IPMC artificial muscle for aquatic ecosystem health applications

“…Robots are often used to replace humans in hazardous, uncomfortable, and tedious works (Cengiz and Mamiş, 2015; Duraisamy et al, 2019; Gundogdu and Afacan, 2012; Moradi et al, 2017; Solyman et al, 2020). Hence, up to now, researchers have been focusing on constructing biomimetic underwater vehicles that can replicate a real fish motion unlike propeller-based vehicles (Chen et al, 2021; Fish and Lauder, 2006; Korkmaz et al, 2021; Lauder and Drucker, 2004; Peng et al, 2007; Xie et al, 2021; Yurugi et al, 2021). To do this, a muscle-like structure should be used to reach similar biological movements with higher efficiency and maneuverability (Chen et al, 2010; Mbemmo et al, 2008; Xu et al, 2009).…”

“…The fundamental idea in the concept of robotic fish is inspired by the unique natural swimming performance of fishes, which have high efficiency, low noise, and excellent maneuverability. On the other hand, classic swimming robots have several imperfections such as weighty body, and complex construction (Chen et al, 2021; Fish and Lauder, 2006; Korkmaz et al, 2021; Lauder and Drucker, 2004; Peng et al, 2007; Xie et al, 2021; Yurugi et al, 2021). With the aim of overcoming these obstacles, an effective method is taking advantage of smart materials such as shape memory alloy (SMA) actuators, piezoelectric actuators, and electroactive polymer (EAP) actuators (Kim and Shahinpoor, 2003; Pugal et al, 2010; Shahinpoor, 1992; Shahinpoor et al, 1998; Shahinpoor and Kim, 2005).…”

Grid Feature-Based Weighted Simulation Method for Multi-Objective Reliability-Based Design Optimization

“…Time complexity of an algorithm indicates the amount of resources required to run it, the time complexity of an algorithm can reflect the performance of the algorithm [43]. N refers to the number of individuals in the population and K represents the number of objectives.…”

Cost of Transport of Undulating Fin Propulsion

“…Initially, robots were built to show that undulatory fins could become a viable alternative to rotational thrusters [ 4 , 5 ]. Once their potential was acknowledged, researchers focused on improving control, propulsion and overall design integration [ 6 , 7 , 8 , 9 , 10 , 11 ], as well as examining how the fins’ shape and motion affect propulsion efficiency [ 12 ], maneuverability [ 12 , 13 ], static thrust and swimming velocity in a flow tunnel (attaching the undulating fin mechanism to a frame with roller bearings [ 14 ]; attaching the undulating fin mechanism to a frame with air bearings [ 15 ]). Along with better robotic undulatory fin designs, researchers used quantitative flow visualization techniques on maneuvering robots (flow visualization in Supplemental Material S1 video S1 , [ 16 ]) [ 17 ] or computational fluid dynamics models [ 18 ] to discover the physical principles of undulatory fin maneuvering.…”