The problem with motorized wheelchairs is that they are large, clumsy and difficult to control. This is especially true if the driver has severely reduced capabilities. What we want is a wheelchair that can take instructions from the driver and then based on its understanding of the environment, construct a plan that will take the user to the intended destination. The user should be able to sit in a room, tell the wheelchair that he wants to be in another room and the wheelchair should take him there as quickly and smoothly as possible.The planner presented in this thesis uses a randomized bi-directional tree search. It builds two trees, one from the start state and one from the goal state by randomly sampling the control space of the robot. Each node is a state and each edge is a control input to the robot.In order to decrease the execution time and improve path quality, the planner uses several heuristics to guide the planner. The heuristics are based on Rapidly-exploring Random Trees, Probabilistic Road-maps and the gradient method.For a normal household situation, this planner can construct a decent plan in mere seconds on relatively slow hardware. Most times it finishes in a fraction of a second.This means that the planner has the ability to run in real-time. As a consequence, the planner can handle a dynamic environment, inaccurate sensor readings and an inaccurate physical robot model.
Author: Edlund, Andreas
Source: Linköping University
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