1 research outputs found
Motion Planning for a Climbing Robot with Stochastic Grasps
Motion planning for a multi-limbed climbing robot must consider the robot's
posture, joint torques, and how it uses contact forces to interact with its
environment. This paper focuses on motion planning for a robot that uses
nontraditional locomotion to explore unpredictable environments such as martian
caves. Our robotic concept, ReachBot, uses extendable and retractable booms as
limbs to achieve a large reachable workspace while climbing. Each extendable
boom is capped by a microspine gripper designed for grasping rocky surfaces.
ReachBot leverages its large workspace to navigate around obstacles, over
crevasses, and through challenging terrain. Our planning approach must be
versatile to accommodate variable terrain features and robust to mitigate risks
from the stochastic nature of grasping with spines. In this paper, we introduce
a graph traversal algorithm to select a discrete sequence of grasps based on
available terrain features suitable for grasping. This discrete plan is
complemented by a decoupled motion planner that considers the alternating
phases of body movement and end-effector movement, using a combination of
sampling-based planning and sequential convex programming to optimize
individual phases. We use our motion planner to plan a trajectory across a
simulated 2D cave environment with at least 95% probability of success and
demonstrate improved robustness over a baseline trajectory. Finally, we verify
our motion planning algorithm through experimentation on a 2D planar prototype.Comment: 7 pages, 7 figure