18 citations · 40 across the 9 of their papers we have counts for
9 papers
A Pin-Array Structure for Gripping and Shape Recognition of Convex and Concave Terrain Profiles
Takuya Kato, Kentaro Uno, Kazuya Yoshida
This paper presents a gripper capable of grasping and recognizing terrain shapes for mobile robots in extreme environments. Multi-limbed climbing robots with grippers are effective…
Enabling Faster Locomotion of Planetary Rovers with a Mechanically-Hybrid Suspension
David Rodríguez-Martínez, Kentaro Uno, Kenta Sawa +6
The exploration of the lunar poles and the collection of samples from the martian surface are characterized by shorter time windows demanding increased autonomy and speeds. Autonom…
Mobility Strategy of Multi-Limbed Climbing Robots for Asteroid Exploration
Warley F. R. Ribeiro, Kentaro Uno, Masazumi Imai +5
Mobility on asteroids by multi-limbed climbing robots is expected to achieve our exploration goals in such challenging environments. We propose a mobility strategy to improve the l…
The effects of increasing velocity on the tractive performance of planetary rovers
David Rodríguez-Martínez, Fabian Buse, Michel Van Winnendael +1
An emerging paradigm is being embraced in the conceptualization of future planetary exploration missions. Ambitious objectives and increasingly demanding mission constraints stress…
RAMP: Reaction-Aware Motion Planning of Multi-Legged Robots for Locomotion in Microgravity
Warley F. R. Ribeiro, Kentaro Uno, Masazumi Imai +2
Robotic mobility in microgravity is necessary to expand human utilization and exploration of outer space. Bio-inspired multi-legged robots are a possible solution for safe and prec…
SegVisRL: Visuomotor Development for a Lunar Rover for Hazard Avoidance using Camera Images
Tamir Blum, Gabin Paillet, Watcharawut Masawat +2
The visuomotor system of any animal is critical for its survival, and the development of a complex one within humans is large factor in our success as a species on Earth. This syst…
RL STaR Platform: Reinforcement Learning for Simulation based Training of Robots
Tamir Blum, Gabin Paillet, Mickael Laine +1
Reinforcement learning (RL) is a promising field to enhance robotic autonomy and decision making capabilities for space robotics, something which is challenging with traditional te…
PPMC RL Training Algorithm: Rough Terrain Intelligent Robots through Reinforcement Learning
Tamir Blum, Kazuya Yoshida
Robots can now learn how to make decisions and control themselves, generalizing learned behaviors to unseen scenarios. In particular, AI powered robots show promise in rough enviro…
Deep Learned Path Planning via Randomized Reward-Linked-Goals and Potential Space Applications
Tamir Blum, William Jones, Kazuya Yoshida
Space exploration missions have seen use of increasingly sophisticated robotic systems with ever more autonomy. Deep learning promises to take this even a step further, and has app…