Integrated motion planning and control for graceful balancing mobile robots

  • Authors:

  • Umashankar Nagarajan;George Kantor;Ralph Hollis

  • Affiliations:

  • Disney Research, Pittsburgh, PA, USA, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA;The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA;The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • International Journal of Robotics Research
  • Year:
  • 2013

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Abstract

This paper presents an integrated motion planning and control framework that enables balancing mobile robots to gracefully navigate human environments. A palette of controllers called motion policies is designed such that balancing mobile robots can achieve fast, graceful motions in small, collision-free domains of the position space. The domains determine the validity of a motion policy at any point in the robot's position state space. An automatic instantiation procedure that generates a motion policy library by deploying motion policies from a palette on a map of the environment is presented. A gracefully prepares relationship that guarantees valid compositions of motion policies to produce overall graceful motion is introduced. A directed graph called the gracefully prepares graph is used to represent all valid compositions of motion policies in the motion policy library. The navigation tasks are achieved by planning in the space of these gracefully composable motion policies. In this work, Dijsktra's algorithm is used to generate a single-goal optimal motion policy tree, and its variant is used to rapidly replan the optimal motion policy tree in the presence of dynamic obstacles. A hybrid controller is used as a supervisory controller to ensure successful execution of motion policies and also successful switching between them. The integrated motion planning and control framework presented in this paper was experimentally tested on the ballbot, a human-sized dynamically stable mobile robot that balances on a single ball. The results of successful experimental testing of two navigation tasks, namely, point-point and surveillance motions are presented. Additional experimental results that validate the framework's capability to handle disturbances and rapidly replan in the presence of dynamic obstacles are also presented.