Trails generates diverse indoor routes with controlled timing
TRAILS: Trajectory Generation for Automated Indoor Locomotion Studies
Robotics
Summary
Experiments with virtual reality often need many different paths inside buildings for people or robots to walk along, but making these paths by hand is hard and slow. The authors created TRAILS, a tool that automatically builds indoor layouts and paths that follow specific shape and speed rules. TRAILS uses math-based shape fitting for smooth routes and sets speeds to keep travel time the same across paths while changing route shapes and how close they come to walls. This helps researchers run fair and varied navigation studies without spending a lot of time creating environments.
What this means in practice
- •For virtual reality developers: Generate numerous indoor navigation paths with controlled timing and geometry to create varied VR experimentation settings.
- •For robotics simulation teams: Automatically produce realistic indoor routes and speed profiles that satisfy kinematic constraints for robot motion testing.
Authors
Juho Kalliokoski, Evan G. Center, Steven M. LaValle, Timo Ojala, Basak Sakcak
Abstract
Virtual reality experiments involving navigation and simulated robot motion often require large, realistic environments and numerous distinct trajectories to avoid repetitive navigation while providing sufficient variation for controlled experiments. Designing such scenarios manually is labor-intensive, particularly when trajectories must satisfy predefined geometric and kinematic constraints while remaining diverse and comparable across experimental conditions. This paper presents TRAILS, an automated framework for generating complete virtual navigation scenarios consisting of an environment layout, a path, and a time-parameterized motion profile. The framework combines a constraint-based procedural environment generator inspired by Wave Function Collapse, trajectory generation using optimized cubic Bézier curves, and speed-profile generation subject to kinematic constraints. The generated scenarios provide diverse indoor environments with corridors, open spaces, and obstacles, together with trajectories of identical duration that vary in path geometry, wall proximity, and velocity profiles while minimizing systematic dependencies between these characteristics. The framework is designed as an easy-to-use tool requiring only a small number of intuitive user-defined parameters to automatically generate large sets of distinct navigation scenarios satisfying prescribed geometric and kinematic constraints. By relying on explicit, seed-controlled algorithmic procedures rather than data-driven models, the generated scenarios remain transparent, reproducible, and configurable, making the framework well suited for telepresence and human-subject experiments. The code is available at https://github.com/JucciK/Trails .