![]() ![]() ![]() The robot controllers for each joint symmetry were evolved for a set number of generations and the robot controllers were evaluated using a fitness function that we designed. Different robot symmetries were tested, i.e., diagonal joint symmetry, diagonal joint reverse symmetry, adjacent joint symmetry, adjacent joint reverse symmetry and random joint symmetry or joint asymmetry. The artificial neural network was optimized using a genetic algorithm. The quadrupedal robot was created with eight joints, and it is controlled using an artificial neural network. The simulations were performed on the PyroSim software platform, a physics engine built on top of the Open Dynamics Engine. The simulation environment was set to a flat surface where the robots could be tested. ![]() To test the suggested algorithm, spider-like robot morphology was created in a simulator. In this research work, the effect of joint symmetry on the robot gait is studied. In the literature, symmetric and asymmetric gaits have been synthesized for legged robots however, no relation between the gait effectiveness and joint symmetry has been studied. The effectiveness of the robot gait depends on the joint symmetry of the robot variations in joint symmetries can result in different types of gaits suitable for different scenarios. Bio-inspired legged robots have the potential to traverse uneven terrains in a very efficient way. ![]()
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