Automatic Snake Gait Generation Using Model Predictive Control
This addresses the challenge of adaptive locomotion for snake robots in varying environments, though it is incremental as it builds on existing control and friction models.
The paper tackles the problem of generating undulatory gaits for snake robots by using Model Predictive Control to automatically produce locomotion gaits via trajectory optimization, resulting in gaits as efficient as Pareto-optimal serpenoid gaits tuned for each environment without method changes.
In this paper, we propose a method for generating undulatory gaits for snake robots. Instead of starting from a pre-defined movement pattern such as a serpenoid curve, we use a Model Predictive Control approach to automatically generate effective locomotion gaits via trajectory optimization. An important advantage of this approach is that the resulting gaits are automatically adapted to the environment that is being modeled as part of the snake dynamics. To illustrate this, we use a novel model for anisotropic dry friction, along with existing models for viscous friction and fluid dynamic effects such as drag and added mass. For each of these models, gaits generated without any change in the method or its parameters are as efficient as Pareto-optimal serpenoid gaits tuned individually for each environment. Furthermore, the proposed method can also produce more complex or irregular gaits, e.g. for obstacle avoidance or executing sharp turns.