![]() ![]() (Interline itself provides managed hosting of OTP for a variety of public-transit agencies around North America.)Īnd yet the OTP project rests on an unstable foundation. Since then, OTP has grown to be used by public-transit agencies, planning firms, and transportation researchers around the world. The OpenTripPlanner routing engine (also known as OTP) was created in 2009 by TriMet (the public-transit agency in Portland, Oregon), OpenPlans (a civic tech non-profit), and individual experts and enthusiasts. However, some background will help explain how relevant many of these open-source practices are to OpenTripPlanner and similar open-source projects that have become critical - but also somewhat neglected - pieces of infrastructure for many organizations. None of the practices are particular to that project. This blog post comes out of a presentation Interline gave at last month's OpenTripPlanner Summit in Boston. OpenTripPlanner: An open-source project going on 10 years □ Automated acceptance test and performance suites.□ RFC (request for comments) processes.In this blog post, we'll share an overview of practices used by a wide variety of open-source projects: However, the health of an open-source project is not a given - it requires ongoing attention and thought. It's less an individual matter of principle than a set of shared advantages: our clients build on each other's advances, and we collaborate productively with a wide range of partners. Geraerts & Overmars (2002) for a discussion.At Interline, we specialize in deploying, creating, and managing the growth of open-source software. There are many variants on the basic PRM method, some quite sophisticated, that vary the sampling strategy and connection strategy to achieve faster performance. The invention of the PRM method is credited to Lydia E. Roughly, if each point can "see" a large fraction of the space, and also if a large fraction of each subset of the space can "see" a large fraction of its complement, then the planner will find a path quickly. The rate of convergence depends on certain visibility properties of the free space, where visibility is determined by the local planner. Given certain relatively weak conditions on the shape of the free space, PRM is provably probabilistically complete, meaning that as the number of sampled points increases without bound, the probability that the algorithm will not find a path if one exists approaches zero. In the query phase, the start and goal configurations are connected to the graph, and the path is obtained by a Dijkstra's shortest path query. Configurations and connections are added to the graph until the roadmap is dense enough. Then, it is connected to some neighbors, typically either the k nearest neighbors or all neighbors less than some predetermined distance. First, a random configuration is created. In the construction phase, a roadmap (graph) is built, approximating the motions that can be made in the environment. The probabilistic roadmap planner consists of two phases: a construction and a query phase. The starting and goal configurations are added in, and a graph search algorithm is applied to the resulting graph to determine a path between the starting and goal configurations. ![]() The basic idea behind PRM is to take random samples from the configuration space of the robot, testing them for whether they are in the free space, and use a local planner to attempt to connect these configurations to other nearby configurations. An example of a probabilistic random map algorithm exploring feasible paths around a number of polygonal obstacles. ![]()
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