Control of a Nature-inspired Scorpion using Reinforcement Learning
This work addresses the need for stealthy surveillance robots in dangerous or unknown environments, but it appears incremental as it applies existing RL methods to a new robot model.
The paper tackled the problem of controlling a scorpion-inspired robot for navigation in rough terrain, proposing a reinforcement learning-based controller that demonstrated efficient navigation in simulation.
A terrestrial robot that can maneuver rough terrain and scout places is very useful in mapping out unknown areas. It can also be used explore dangerous areas in place of humans. A terrestrial robot modeled after a scorpion will be able to traverse undetected and can be used for surveillance purposes. Therefore, this paper proposes modelling of a scorpion inspired robot and a reinforcement learning (RL) based controller for navigation. The robot scorpion uses serial four bar mechanisms for the legs movements. It also has an active tail and a movable claw. The controller is trained to navigate the robot scorpion to the target waypoint. The simulation results demonstrate efficient navigation of the robot scorpion.