Robots are increasingly being used for various applications including search and rescue operations and combat situations. Unfortunately, during the performance of maneuvers, a robot may fall or tip over, preventing it from moving normally. Controlling the robot to successfully right itself can be a difficult and time-consuming task for operators — a major problem in situations that may be both time-sensitive and dangerous; for example, those controlling the robot must determine how to re-orient the robot to a desired position, if it is possible.

There have been several approaches employed for robot self-righting that can be categorized into four main groups: passive approaches, specific mechanisms, overturned drivability, and dynamic approaches. Passive approaches do not make an effort to actively self-right, relying on the shape of the robot and its center of mass location to allow for easy righting or to inhibit flipping. Some robots rely on specific mechanisms for self-righting, such as a flipper.

Another category of robots allows for upside-down operation, attempting to limit the need for self-righting. And still other robots take a dynamic approach, focusing on the release of stored mechanical energy in an attempt to right itself, such as by leveraging spring legs or generating rolling momentum.

An improved robot self-righting methodology has been developed that could be applied to any generic robot. A computational methodology is executed to analyze various orientations of the robot and joint configurations to determine those that are stable and those that induce instability (i.e., tip-over) events. The results from this analysis may be organized into a graphical network model. This methodology allows robots to autonomously determine how to right themselves, to provide designers with a tool to assess whether their robots are able to self-right, and to determine the qualities that make robots more capable to self-right.

The calculations may be performed prior to fielding the robot, creating a static map of the conformation space that can be stored using a computer-readable storage medium. The sensory data from the robot is then used to localize the current state on the map, and a path plan need only be generated to move from the current state to the nearest pre-computed optimal path to implement the above described method.

For more information, contact This email address is being protected from spambots. You need JavaScript enabled to view it., or visit here .