Leveraging Evolutionary Algorithms for Feasible Hexapod Locomotion Across Uneven Terrain


  • Jack Vice University of Central Florida
  • Gita Sukthankar University of Central Florida
  • Pamela K. Douglas University of Central Florida




legged locomotion, evolutionary algorithms


Optimizing gait stability for legged robots is a difficult problem. Even on level surfaces, effectively traversing across different textures (e.g., carpet) rests on dynamically tuning parameters in multidimensional space. Inspired by biology, evolutionary algorithms (EA) remain an attractive solution for feasibly implementing robotic locomotion with both energetic economy and rapid parameter convergence. Here, we leveraged this class of algorithms to evolve a stable hexapod gait controller capable of traversing uneven terrain and obstacles. Gait parameters were evolved in a rigid body dynamics simulation on an 8 x 3 meter obstacle course comprised of random step field, linear obstacles and inclined surfaces. Using a fitness function that jointly optimized locomotion velocity and stability, we found that multiple successful gait parameter evolutions yielded specialized functionality for each leg. Specific gait parameters were identified as critical to developing a rough terrain gait.




How to Cite

Vice, J., Sukthankar, G., & Douglas, P. K. (2022). Leveraging Evolutionary Algorithms for Feasible Hexapod Locomotion Across Uneven Terrain. The International FLAIRS Conference Proceedings, 35. https://doi.org/10.32473/flairs.v35i.130715



Special Track: Autonomous Robots and Agents