Incremental Map Generation (IMG)

Abstract

Automatic motion planning has applications ranging from traditional robotics to computer-aided design to computational biology and chemistry. Randomized planners, such as probabilistic roadmap methods (prms), have been highly successful in solving these high degree of freedom problems. However, the traditional prm framework fails to address several practical issues. One of the most important issues is the difficulty of deciding what size roadmap is required to solve a given problem efficiently. prms do not provide an automated way to determine appropriate roadmap size. In this paper, we propose a new prm-based framework called Incremental Map Generation (img) to address this problem. Our strategy is to break the map generation into independent processes. Each process generates an independent roadmap component. img proceeds by adding independent roadmap components to an existing roadmap until some user defined criteria are satisfied. In addition to addressing the roadmap size problem, this framework supports roadmap reproducibility in that any of the roadmap increments can be reproduced by using the same set of seeds. Finally, these independent processes are natural for parallelization.

Hybrid Motion Planning Using Minkowski Sums

“…Similar to connecting individual configurations, it is also more desirable to connect each connected component (CC) to its k-closest CCs (instead of to all CCs). However, in PRMs, there is no well defined distance metrics for CCs [32]. On the contrary, for M-sum planner, the distance between two C-slices can simply be measured as the difference between their rotation and joint angles (of the seeds).…”

Structural Improvement Filtering Strategy for PRM

“…We implemented these strategies as described in [24]: every new sample in the roadmap attempts to connect to the 10-closest nodes already in the roadmap by using the straight-line and rotate-at-s local planners [2].…”

“…Measuring the largest component's diameter is not an exact measure of roadmap quality, but can be used as an approximation [24]. The diameter is the longest shortest-path in the roadmap, and the quality we consider refers to how efficiently the robot can move between the two extreme points in the roadmap.…”

“…Both a rise and a fall in the component's diameter signify an improvement: a rise corresponds to an addition of connected C-Space, and a fall corresponds to improvements in shorter pathways. We also show when the point at which the query for the environment is solved, although this is a poor metric for comparisons [15], [17], [24]. Additionally, tables of average statistics gathered when solving the queries are shown for: Maze in Table V, Hook in Table VI, and Walls in Table VII. All sample acceptance policies for a given planner provide some level of improvement over Pure; the amount of improvement depends on the type of planner.…”

“…Dynamically adapting the planning strategy to features discovered in each problem [7], [10], [16], [18], [24] has been successful in addressing the shortcomings of a particular sampling strategy. However these methods need metrics that gather relevant information about the planning process in order to make effective decisions to adapt the planning strategy.…”