Planning robust displacement missions by means of robot-tasks and local maps

• Published in: Robotics and autonomous systems Vol. 20; no. 1; pp. 99 - 114
• Main Authors: Le Fort-Piat, N., Collin, I., Meizel, D.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.1997; Elsevier
• Subjects: Applied sciences; Computer science; control theory; systems; Control theory. Systems; Displacement missions; Exact sciences and technology; Local maps; Robot-tasks; Robotics; Robot-tasks; Displacement missions; Local maps; Autonomous system; Small scale map; Planning; Moving robot; Displacement; Robotics
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/S0921-8890(96)00024-3

This paper introduces a mobile robot mission planning method based on the robot-task concept. Missions are performed by a sequence of closed-loop mechanisms and are thus more robust with respect to the unavoidable differences between reality and the model us the planner. A displacement mission is defined as the combination of a path and of a description of the observations expected along the way, enabling thus to control/check its correct execution. These observations are obtained by the use of local maps defined as coherent sets of landmarks or beacons detectable by the on-board sensing devices. The considered vehicle is a car like one endowed with a rotative telemeter. The environment is 2D and is structured by polygonal obstacles and landmarks. Robot-task generation is carried out in three phases. The first phase consist of defining a path as a set of configurations. Each of these configurations is attached to the list of map primitives providing the robot with the best localization information. This part is a compromise between length, safety conditions and complexity of vehicle control. The second phase applies a data analysis method to the pain, in order to dynamically group path configurations ssociated to the same list of primitives called local maps. The third phase precisely deals with robot-task definition. It first consists in defining a smooth path (Bezier curve) from each group of configurations. The robot-task sequence correspond to the ordered list of these pathsassociated with their respective local maps.