RoboSimian is of non-humanoid designs. It has 28 degrees-of freedom for mobility and manipulation and folds into a relatively small volume.
Humans control the robot remotely using a normal computer monitor, keyboard and mouse.
The robot is designed to assist in dangerous situations such as nuclear meltdowns and the aftermath of bomb blasts.
The use of four versatile limbs allows it to adapt to the test scenario in ways that would be difficult for a bipedal robot. We chat with Katie Byl of the UC Santa Barbara Robotics Lab, whose team programmed RoboSimian, to learn about the advantages of a quadruped design and how RoboSimian may be utilized in complex environments like being underground or even in space! (from below Tested video)
sites: https://www-robotics.jpl.nasa.gov/tasks/showTask.cfm?TaskID=236&tdaID=700043
https://www.jpl.nasa.gov/news/news.php?feature=4617
designs and algoritms
RoboSimian is a quadruped robot inspired by an ape-like morphology, with four symmetric limbs that provide a large dexterous workspace and high torque output capabilities. Advantages of using RoboSimian for rough terrain locomotion include (1) its large, stable base of support, and (2) existence of redundant kinematic solutions, toward avoiding collisions with complex terrain obstacles. However, these same advantages provide significant challenges in experimental implementation of walking gaits. Specifically: (1) a wide support base results in high variability of required body pose and foothold heights, in particular when compared with planning for humanoid robots, (2) the long limbs on RoboSimian have a strong proclivity for self-collision and terrain collision, requiring particular care in trajectory planning, and (3) having rear limbs outside the field of view requires adequate perception with respect to a world map. In our results, we present a tractable means of planning statically stable and collision-free gaits, which combines practical heuristics for kinematics with traditional randomized (RRT) search algorithms.
In planning experiments, our method outperforms other tested methodologies. Finally, real-world testing indicates that perception limitations provide the greatest challenge in real-world implementation - more in the article
locomotion planning
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