Interoperable multi-agent framework for unmanned aerial/ground vehicles: towards robot autonomy

Abstract

Multi-agent architectures for autonomous robots are generally mission and platform oriented. Autonomous robots are commonly employed in patrolling, surveillance, search and rescue and human-hazardous missions. Irrespective of the differences in unmanned aerial and ground robots, the algorithms for obstacle detection and avoidance, path planning and path-tracking can be generalized. Service-oriented interoperable framework for robot autonomy (SOIFRA) proposed in this paper is an interoperable multi-agent framework focusing on generalizing platform-independent algorithms for unmanned aerial and ground vehicles. As obstacle detection and avoidance are standard requirements for autonomous robot operation, platform-independent collision avoidance algorithms are incorporated into SOIFRA. SOIFRA is behaviour based and is interoperable across unmanned aerial and ground vehicles. Obstacle detection and avoidance are performed utilizing computer vision-based algorithms, as these are generally platform independent. Obstacle detection is achieved utilizing Hough transform, Canny contour and Lucas–Kanade sparse optical flow algorithm. Collision avoidance performed utilizing optical flow-based and expansion of object-based time-to-contact demonstrates SOIFRA’s modularity. Experiments performed, utilizing TurtleBot, Clearpath Robotics Husky, AR Drone and Hector-quadrotor, establish SOIFRA’s interoperability across several robotic platforms.