# Locomotion Efficiency Optimization of Biologically Inspired Snake Robots
###### Abstract  
Snake robots constitute bio-inspired solutions that have been studied due to their ability to move in challenging environments where other types of robots, such as wheeled or legged robots, usually fail. In this paper, we consider both land-based and swimming snake robots. One of the principal concerns of the bio-inspired snake robots is to increase the motion efficiency in terms of the forward speed by improving the locomotion methods. Furthermore, energy efficiency becomes a crucial challenge for this type of robots due to the importance of long-term autonomy of these systems. In this paper, we take into account both the minimization of the power consumption and the maximization of the achieved forward velocity in order to investigate the optimal gait parameters for bio-inspired snake robots using lateral undulation and eel-like motion patterns. We furthermore consider possible negative work effects in the calculation of average power consumption of underwater snake robots. To solve the multi-objective optimization problem, we propose transforming the two objective functions into a single one using a weighted-sum method. For different set of weight factors, Particle Swarm Optimization is applied and a set of optimal points is consequently obtained. Pareto fronts or trade-off curves are illustrated for both land-based and swimming snake robots with different numbers of links. Pareto fronts represent trade-offs between the objective functions. For example, how increasing the forward velocity results in increasing power consumption. Therefore, these curves are a very useful tool for the control and design of snake robots. The trade-off curve thus constitutes a very useful tool for both the control and design of bio-inspired snake robots. In particular, the operators or designers of bio-inspired snake robots can choose a Pareto optimal point based on the trade-off curve, given the preferred number of links on the robot. The optimal gait parameters for the robot control system design are then directly given both for land-based and underwater snake robots. Moreover, we are able to obtain some observations about the optimal values of the gait parameters, which provide very important insights for future control design of bio-inspired snake robots. bio-inspired snake robots; multi-objective optimization; particle swarm optimization (PSO); energy efficiency +
Footnote †: journal: Applied Soft  
them to the design and the locomotion of the robotic systems. In many instances, to address scientific problems, engineers seek for solutions by getting inspired from the nature. In particular, they study the robust motion capabilities of biological creatures that have emerged through millions of years of evolution, and this process is termed biomimetics. Nowadays, there has been increasing interest on using robotic systems for exploration, monitoring, and surveillance tasks. Bio-inspired robotic systems that mimic the motion of biological snakes or fish can be considered good candidates for these kinds of applications. Inspired by the robustness and the stability of biological snake locomotion, snake robots carry the potential to meet the growing need for new technology that can be used in challenging environments [1]. Several models have been proposed for bio-inspired snake robots moving on land [1]. In [2], the locomotion capabilities of biological snakes has been studied, while the world's first snake-like robot was developed in Japan in 1972 [3]. Bio-inspired snake robots have the potential to be used in several applications such as inspection and intervention operations in hazardous environments in industrial plants, manipulator tasks in tight spaces for instance pipe inspection. In addition, the robotic community signified the need for new technology that can be employed to locate survivors inside a collapsed building. Hence, a very relevant application of bio-inspired snake robots can involve search and rescue missions in earthquake areas in the future [1].