Graph planning method guides space station multi-limbed robots safely
Graph-Based Simultaneous Path and Foothold Planning for Multi-Limbed Intra-Vehicular Robots in Space Stations
Robotics
Summary
Moving robots inside space stations is tricky because they must grab onto limited handrails and surfaces to hold on safely. The authors developed a new planning method that helps multi-limbed robots figure out where to place their limbs and how to move through the station together. They tested this method in simulations of the International Space Station to show it can create safe and efficient routes for the robots to travel and work in microgravity.
What this means in practice
- •For space robotics teams: Plan and coordinate multi-limbed robot movements inside space station cabins where grasp points are limited and discrete.
- •For industrial robot programmers: Generate feasible limb placement and motion sequences for robots navigating environments with sparse, fixed grip interfaces.
Authors
Masazumi Imai, Kentaro Uno, Toshinori Kuwahara, Kazuya Yoshida
Abstract
Robot-aided operations in space stations are essential for reducing the workload of astronauts and improving the efficiency of on-orbit activities. Multi-limbed intra-vehicular robots (MLIVRs) equipped with grappling end-effectors have emerged as a promising solution, as they can securely grasp pre-existing interfaces, such as handrails and seat tracks, thereby enabling stable locomotion and forceful manipulation in microgravity environments. Since graspable locations on these interfaces are spatially limited and discretely distributed, motion planning for MLIVRs must be addressed jointly with foothold planning. This paper presents a simultaneous path and foothold planning framework based on graph theory for MLIVRs. The proposed method efficiently searches for feasible stance sequences for a multi-limbed robot while satisfying manipulability constraints. The effectiveness of the proposed framework is validated through simulations in a 3D model of the International Space Station (ISS) cabin, demonstrating its capability to generate feasible and efficient locomotion plans in realistic intra-vehicular environments.