Landing A Mobile Robot Safely from Tall Walls Using Manipulator Motion Generated from Reinforcement Learning

Abstract

A three-tracked-link robot was designed previously for autonomous welding inside double-hulled ship blocks with tight spaces and protruding stiffeners. Bilge blocks, a type of double-hulled blocks, have a tall wall at the entrance. Climbing down from this tall wall involves a risk of toppling as neither of the three links of the robot (front arm, body, and rear arm) is long enough to reach the ground from the wall top, and the robot carries a heavy manipulator for welding. Instead of being a burden, we explore the use of the manipulator motion to shift the center of gravity, helping the robot climbing down safely. In this paper, we proposed the use of reinforcement learning and physics-based computer simulation to determine suitable motion sequences for safe climbing down from a tall wall. We discovered two effective safe-landing modes that use both arms for major balancing acts and a manipulator for balance trimming during the controlled landing. The method also allowed us to explore the effect of other design factors such as the choice of manipulator size, manipulator motion type, and change in environment on the motion sequence.