Energy Efficient Navigation for Running Legged Robots

Abstract

Energy-efficient navigation is an important technology for mobile robots because of its potential to increase the operation time of the robot. In particular, when coupled with a dynamic legged quadruped, the need for energy savings is made more apparent as payloads are limited. Due to the complexity in modeling motion and power models of these robots, a new approach is necessary to effectively motion plan for these complex robots. We accomplish this by using Sampling-Based Model Predictive optimization (SBMPO) which was extended for use on the LLAMA quadrupedal platform in simulation. SBMPO allows for direct generation of trajectories while using a heuristic-based search to speed up computations. This approach is shown to effectively motion plan while optimizing for energy consumption and maintaining the natural dynamics of the robot in a simulated environment.