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.
Highlights
Unmanned autonomous vehicles are robots, capable of intelligent actions and motions, operating without a guide or teleoperator
Service-oriented interoperable framework for robot autonomy (SOIFRA), an interoperable multi-agent framework for unmanned aerial and ground vehicles proposed in this work, provides a framework for generalized and platformindependent algorithms
As collision avoidance is standard for autonomous operation of unmanned robots, algorithms for obstacle detection and avoidance are incorporated into SOIFRA
Summary
Unmanned autonomous vehicles are robots, capable of intelligent actions and motions, operating without a guide or teleoperator. The proposed work SOIFRA is a behaviour-based multi-agent framework for autonomous unmanned aerial and ground vehicles. The translational part of the optical flow field is utilized to estimate the distance to the obstacle based on which obstacle avoidance is performed. SOIFRA, an interoperable multi-agent framework for unmanned aerial and ground vehicles proposed in this work, provides a framework for generalized and platformindependent algorithms. As collision avoidance is standard for autonomous operation of unmanned robots, algorithms for obstacle detection and avoidance are incorporated into SOIFRA. The experiments illustrate the feasibility in utilizing the same collision avoidance algorithm for autonomous unmanned aerial and ground vehicles, performing identical tasks. The obstacle to be avoided is identified utilizing the concept that optical flow vectors from edges of an object do not intersect if the robot is in collision course with the obstacle (Fig. 4).
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