Efficient Robotic Walking by Learning Gaits and Terrain Properties

In this thesis, we investigate the question of how a legged robot can walk efficiently, and take advantage of its ability to alter its gait. This work targets the issue of increasing the efficiency of legged vehicles on different challenging terrains. We decompose the problem into three sub-problems: walking gait problem, physical adaptation problem, and terrain identification and gait adaptation problem. In the walking gait sub-problem, we investigate the effects of gait parameters on the performance of the robot. In particular, we assess the ground speed, power efficiency and terrain sensibility of the robot at varying leg cycle frequencies. In the physical adaptation sub-problem, we investigate the effects of different kinds of legs on the robot’s performance. We also look at the influence of leg-compliance on walking behavior. In the terrain identification and gait adaptation sub-problem, we design a gait adaptation algorithm to identify the terrain by initially classifying the proprioceptive information collected over different terrains and then adapt its gait accordingly. Identifying the terrain in real-time helps the robot plan its gait on that terrain and effectively increase the walking efficiency in real-time. We use a cost-based unsupervised learning algorithm [28] to classify the terrain data. In our experiments, we use proprioceptive sensor data collected by running the robot on four different terrains. We also use synthetic data for verifying our algorithm. We conclude with an analysis of the data and validate the performance of our algorithm.