Rapidly-Exploring Quotient-Space Trees: Motion Planning using Sequential Simplifications
This addresses motion planning efficiency for robotics, but it is incremental as it builds on existing RRT methods with a novel twist.
The paper tackles motion planning by using sequential simplifications through quotient-spaces, presenting the QRRT algorithm that guarantees probabilistic completeness and reduces runtime by at least one order of magnitude in experiments.
Motion planning problems can be simplified by admissible projections of the configuration space to sequences of lower-dimensional quotient-spaces, called sequential simplifications. To exploit sequential simplifications, we present the Quotient-space Rapidly-exploring Random Trees (QRRT) algorithm. QRRT takes as input a start and a goal configuration, and a sequence of quotient-spaces. The algorithm grows trees on the quotient-spaces both sequentially and simultaneously to guarantee a dense coverage. QRRT is shown to be (1) probabilistically complete, and (2) can reduce the runtime by at least one order of magnitude. However, we show in experiments that the runtime varies substantially between different quotient-space sequences. To find out why, we perform an additional experiment, showing that the more narrow an environment, the more a quotient-space sequence can reduce runtime.