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Dynamic Obstacle Detection and Avoidance

Unknown collision avoidance through multi-modal perception and reactive planning

Akash Kumbar, Astik Srivastava, and Nabanita Dash

ROS Husky A200 RGB-D LiDAR Fast-LIO RRT* MPC
Course Report (PDF) ā†— Real-world Robot Demo ā†—

We developed a navigation stack for the Clearpath Husky A200 to detect and track moving obstacles, localize against a LiDAR map, and reactively plan collision-free motion. The system combines RGB-D and LiDAR perception with Fast-LIO localization, RRT*/MinCo global planning, and MPC-based local planning within ROS.

System overview

System architecture for perception, localization, planning, and execution
Perception, localization, global planning, reactive planning, and robot execution.
LiDAR and visual perception pipeline
Multi-modal perception and dynamic-obstacle tracking pipeline. Image taken from LV-DOT.

Dynamic actor detection and tracking

Detecting and tracking a moving actor using the onboard perception stack.

LiDAR localization

The robot localizing with LiDAR against a provided map.

Reactive Planning and Real-world Navigation

RViz visualization

RViz view of localization, obstacle perception, and robot motion. Open in Google Drive ā†—

Real-world Husky demo

Real-world robot navigation and reactive obstacle avoidance. Open in Google Drive ā†—

Husky platform

Clearpath Husky A200 with RGB-D camera and LiDAR
Hardware setup on the Clearpath Husky A200.
Husky robot model in simulation
Husky platform in simulation.

Results