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 platformindependent algorithms for unmanned aerial and ground vehicles. As obstacle detection and avoidance are standard requirements for autonomous robot operation, platformindependent 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-tocontact demonstrates SOIFRA's modularity. Experiments performed, utilizing TurtleBot, Clearpath Robotics Husky, AR Drone and Hector-quadrotor, establish SOIFRA's interoperability across several robotic platforms.

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