D. Coleman, I. Sucan, S. Chitta, N. Correll: Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case-Study. In: Journal of Software Engineering in Robotics, Special issue on Best Practice in Robot Software Development, 5 (1), pp. 3-16, 2014.

We describe best practices for lowering the barrier of entry in the MoveIt! framework, an open-source tool for mobile manipulation in ROS, that allows users to 1) quickly get basic motion planning functionality with minimal initial setup, 2) automate its configuration and optimization, and 3) easily customize its components. A graphical interface that assists the user in configuring MoveIt! is the cornerstone of our approach, coupled with the use of an existing standardized robot model for input, automatically generated robot-specific configuration files, and a plugin-based architecture for extensibility. These best practices are summarized into a set of barrier to entry design principles applicable to other robotic software. The approaches for lowering the entry barrier are evaluated by usage statistics, a user survey, and compared against our design objectives for their effectiveness to users.


M. Otte, N. Correll: C-FOREST: Parallel Shortest-Path Planning with Super Linear Speedup. In: IEEE Transaction on Robotics, 29 (3), pp. 798-806, 2013.
C-FOREST is a parallelization framework for single-query sampling-based shortest path-planning algorithms. Multiple search trees are grown in parallel (e.g., 1 per CPU). Each time a better path is found, it is exchanged between trees so that all trees can benefit from its data. Specifically, the path’s nodes increase the other trees’ configuration space visibility, while the length of the path is used to prune irrelevant nodes and to avoid sampling from irrelevant portions of the configuration space. Experiments with a robotic team, a manipulator arm, and the alpha benchmark demonstrate that C-FOREST achieves significant superlinear speedup in practice for shortest path-planning problems (team and arm), but not for feasible path panning (alpha).
M. Otte, N. Correll: Any-Com Multi-Robot Path-Planning with Dynamic Teams: Multi-Robot Coordination under Communication Constraints. In: Experimental Robotics, Springer Tracts in Advanced Robotics, pp. 743-757, New Dehli, India, 2010.
We are interested in finding solutions to the multi-robot path-planning problem that have guarantees on completeness, are robust to communication failure, and incorporate varying team size. In this paper we present an algorithm that addresses the complete multi-robot path-planning problem from two different angles. First, dynamic teams are used to minimize computational complexity per robot and maximize communication bandwidth between team-members. Second, each team is formed into a distributed computer that utilizes surplus communication bandwidth to help achieve better solution quality and to speed-up consensus time. The proposed algorithm is evaluated in three real-world experiments that promote dynamic team formation. In the first experiment, a five mobile robot team plans a set of compatible paths through an office environment while communication quality is disrupted using a tin-can Faraday cage. Results show that the distributed framework of the proposed algorithm drastically speeds-up computation, even when packet loss is as high as 97%. In the second and third experiments, four robots are deployed in a network of three building wings connected by a common room. Results of the latter experiments emphasize a need for dynamic team algorithms that can judiciously choose which subset of the original problem a particular dynamic team should solve.
M. Otte, N. Correll: Any-Com Multi-Robot Path Planning: Maximizing Collaboration for Variable Bandwidth. In: Proceedings of the 10th Int. Symposium on Distributed Autonomous Robotic Systems (DARS), Distributed Autonomous Robotic Systems, Springer Tracts in Advanced Robotics 83, pp. 161-173, 2010.
We identify a new class of algorithms for multi-robot problems called “Any-Com” and present the first algorithm belonging to that class: “Any-Com intermediate solution sharing” (or Any-Com ISS) for multi-robot path planning. Any-Com algorithms find a suboptimal solution quickly and then refine that solution subject to communication constraints. This is analogous to the “Any-Time” framework, in which a suboptimal solution is found quickly, and refined as time permits. The current paper focuses on the task of finding a coordinated set of collisionfree paths for all robots in a common area. The computational load of calculating a solution is distributed among all robots, such that the robotic team becomes a distributed computer. Any-Com ISS is probabilistically/resolution complete and a particular robot contributes to the global solution as much as communication reliability permits. Any-Com ISS is “Centralized” in the planning-algorithmic sense that all robots are viewed as pieces of a composite robot; however, there is no dedicated leader and all robots have the same priority. Previous centralized multi-robot navigation algorithms make assumptions about communication topology and bandwidth that are often invalid in the real world. Any-Com allows for collaborative problem solving with graceful performance declines as communication deteriorates. Results are validated experimentally with a team of 5 robots.

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