Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study
David Coleman, Ioan Sucan, Sachin Chitta, Nikolaus Correll
TL;DR
The paper tackles the challenge of high entry barriers in robot-agnostic software by presenting best-practice design principles and detailing MoveIt!'s Setup Assistant as a case study. It introduces a plugin-based motion planning framework, automatic configuration, and visualization tools that enable rapid, out-of-the-box motion planning for new robots, along with automated tuning and benchmarking. Empirical results from adoption metrics, a community survey, and comparative analyses with other MPFs demonstrate improved accessibility and engagement, while highlighting ongoing areas for hardware integration and documentation. The work argues that systematic barrier-reduction strategies can accelerate innovation and collaboration in robotic software, with broad applicability beyond MoveIt!.
Abstract
Developing robot agnostic software frameworks involves synthesizing the disparate fields of robotic theory and software engineering while simultaneously accounting for a large variability in hardware designs and control paradigms. As the capabilities of robotic software frameworks increase, the setup difficulty and learning curve for new users also increase. If the entry barriers for configuring and using the software on robots is too high, even the most powerful of frameworks are useless. A growing need exists in robotic software engineering to aid users in getting started with, and customizing, the software framework as necessary for particular robotic applications. In this paper a case study is presented for the best practices found 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.