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In recent years, there has been a significant increase in the interest for snake like modular robots due to their superior locomotion capabilities in terms of versatility, adaptability and scalability. Passive wheeled planar snake like robots are a major category and they are being actively researched. Due to the nonholonomic constraints imposed on them, certain configurations lead to the singularity which must be avoided at all costs. Furthermore, it is vital to generate a locomotion pattern such that they can track a wide range of trajectories. All of these objectives must be accomplished smoothly and in an energy efficient manner. Studies indicate that meeting all of these requirements is a challenging problem. In this study, a novel form of the serpenoid curve is proposed in order to make the robot track arbitrary paths. A controller has been designed using the feedback linearization method. Afterwards, a new performance measure, considering both the efficiency and sustainability of the locomotion, has been proposed to evaluate the locomotion. Optimal parameters for the proposed serpenoid curve and the linear controller have been determined for efficient locomotion by running series of simulations. Relations between the locomotion performance, locomotion speed and eigenvalues of the linear controller have been demonstrated. Simulation results show striking differences between the locomotion by using the proposed serpenoid curve with optimal parameters and the locomotion by purely tracking a given path. Obtained results also indicate that the aforementioned requirements are met successfully and confirm the validity and consistency of the proposed performance measure.

This paper deals with the design of a robotic snake, its assembly, simulation using CoppeliaSim, and the testing of various methods for the control of robotic snakes (Serpentinoid, CPG). For individual control methods, the influence of selected parameters on the signals controlling the motorized joints of the robotic snake is observed, and their influence on the speed and energy consumption of the given mechanism is described.

[pt] Na última década, os robôs móveis com pernas ganharam notoriedade por sua capacidade de se movimentar com segurança em terrenos acidentados e superar obstáculos, como declives e escadas, podendo ser utilizados em mais aplicações em comparação com os robôs móveis com rodas. Novos desenvolvimentos que melhorem a robustez do planejamento de trajetória e o controle dinâmico de robôs com pernas são cruciais para o avanço desse campo. O objetivo deste trabalho é desenvolver um framework baseado em C++ e ROS Noetic que integre otimização de trajetória off-line para robôs com pernas com Model Predictive Control (MPC) on-line, considerando o mapa de elevação do terreno. A otimização de trajetória é baseada na biblioteca de código aberto TOWR (Trajectory Optimization for Walking Robots), que emprega uma função contínua para representar o mapa do terreno. Para tornála mais genérica, foi implementada uma interface que permite que mapas de elevação 2,5D sejam usados como representação do terreno. Além disso, as trajetórias geradas pelo TOWR são fornecidas como referências para um controlador MPC baseado na biblioteca de código aberto OCS2. As trajetórias otimizadas pelo MPC são então rastreadas por um Whole-Body Controller (WBC), que calcula os torques de atuação das juntas do robô. A estrutura é validada em simulações usando a dinâmica completa do robô, com diferentes tipos de terreno e sob perturbação externa. / [en] In the last decade, legged mobile robots have gained notoriety for their ability to move safely over rough terrain and overcome obstacles such as slopes and stairs, opening up new applications compared to wheeled mobile robots. New developments that improve the robustness of trajectory planning and dynamic control of legged robots are crucial for the advancement of this field. The aim of this work is to develop a framework based in C++ and ROS Noetic that integrates offline trajectory optimization for legged robots with online Model Predictive Control (MPC) while taking into account the elevation map of the terrain. The trajectory optimization is based on the open-source library TOWR (Trajectory Optimization for Walking Robots), which employs a continuous function to represent the map of the terrain. To make it more generic, an interface was implemented to allow 2.5D elevation maps to be used as terrain representation. Furthermore, the trajectories generated by TOWR are provided as references for a MPC implemented based on the open-source library OCS2. The trajectories optimized by the MPC are then tracked by a weighted Whole-Body Controller (WBC), which computes the actuation torques for the robot s joints. The framework is validated in simulations using the full dynamics of the robot, with different terrain types and under external disturbance.