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Improved action and path synthesis using gradient sampling

University of British Columbia

An autonomous or semi-autonomous powered wheelchair would bring the benefits of increased mobility and independence to a large population of cognitively impaired older adults who are not currently able to operate traditional powered wheelchairs. Algorithms for navigation of such wheelchairs are particularly challenging due to the unstructured, dynamic environments older adults navigate in their daily lives. Another set of challenges is found in the strict requirements for safety and comfort of such platforms. We aim to address the requirements of safe, smooth, and fast control with a version of the gradient sampling optimization algorithm of [Burke, Lewis & Overton, 2005]. We suggest that the uncertainty arising from such complex environments be tracked using a particle filter, and we propose the Gradient Sampling with Particle Filter (GSPF) algorithm, which uses the particles as the locations in which to sample the gradient. At each step, the GSPF efficiently finds a consensus direction suitable for all particles or identifies the type of stationary point on which it is stuck. If the stationary point is a minimum, the system has reached its goal (to within the limits of the state uncertainty) and the algorithm naturally terminates; otherwise, we propose two approaches to find a suitable descent direction. We illustrate the effectiveness of the GSPF on several examples with a holonomic robot, using the Robot Operating System (ROS) and Gazebo robot simulation environment, and also briefly demonstrate its extension to use a version of the RRT* planner instead of a value function.

Text

2017-10-17

Attribution-NonCommercial-ShareAlike 4.0 International

http://hdl.handle.net/2429/63328

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-sa/4.0/