A framework for autonomous self-righting of a generic robot on sloped planar surfaces

Increasingly, robots are being applied to challenges in dynamic, unstructured environments including urban search and rescue (USAR), planetary exploration, and military missions. During the execution of these missions, the robot may unintentionally tip over, rendering it unable to move normally. The ability to self-right and recover in such situations is crucial to mission completion and safe robot recovery. However, to date, nearly all self-righting solutions have been point solutions, each designed for a specific platform. As a first step toward a generic solution, this paper presents a framework for analyzing the self-righting capabilities of any generic robot on sloped planar surfaces. Based on the planar assumption, interactions with the ground can be defined entirely in terms of the robot´s convex hull. Motion of arms, legs, or other appendages may change the convex hull shape and/or center of mass position, affecting the robot´s orientation. Our framework for solving this problem can be summarized as follows: first, for each stable conformation, we analyze the position of the center of mass relative to the vertical projection of the convex hull face in contact with the ground. From this, we develop a conformation space map, defining stable state sets as nodes and the conformations where discontinuous state changes occur as transitions. Finally, we convert this map into a directed graph, and assign costs to the transitions according to changes in potential energy between states. Based upon the ability to traverse this directed graph to the goal state, one can analyze a robot´s ability to self-right. To illustrate each step in our framework, we use a simple two-dimensional robot with a one degree of freedom arm, and then show a case study of iRobot´s 510 Packbot®. Ultimately, we project that this framework will be useful both for designing robots with the ability to self-right and for planning joint movements to achieve efficient, autonomous self-r- ghting behaviors.