Bridging the Gap between Task Planning and Path Planning
ABSTRACT Autonomous service robots have to recognize and interpret their environment to be able to interact with it. This paper focuses on service tasks such as serving a glass of water where a humanoid two-arm-system has to acquire an object from the scene. A task planner should be able to autonomously discern the necessary actions to solve the task. In the process, a path planner can be used to compute motion sequences to execute these actions. To plan trajectories, the path planner requires a pair of configurations, the start and the goal configuration of the robot, to be provided e.g. by a task planner. This paper proposes a method to autonomously find the goal configurations necessary to acquire objects from the scene and thus makes an attempt to bridge the gap between task planning and path planning. The method determines where to grasp an object by analyzing the scene and the influence of obstacles on the intended grasp location. For the case where the goal object can not be grasped due to obstructing obstacles, a solution is proposed
Full-textDOI: · Available from: Christoph Borst, Aug 28, 2015
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- "Moreover, the issue of dependency between subproblems of movement is not addressed. The other approaches dealing with the link between task planning and geometric reasoning are mainly approaches for one kind of specific problems . In , an integrated planner is presented, in which the definitions of states of the world contains symbolic and geometric informations. "
ABSTRACT: Mobile robots such as explorer rovers need task and path planning abilities in order to fulfill their assigned missions: path planning to plan their movements and task planning to plan their actions. The coupling between these two kinds of planning presents open issues such as the description of the environment and the consideration of geometric constraints that must be verified in order to act and move during an action. This paper addresses these issues by proposing an architecture in which a hierarchical task planner sends requests to a path planner in order to check the feasibility of actions. Requirements allowing the path planner to produce an answer are presented as well as the description of planning operators. Finally, we specify the mechanism and the communication language by which the task planner produces requests and takes into account answers.06/2008; 1019(1):162-167. DOI:10.1063/1.2952971
- " we already emphasized the importance of being able to easily find robot target configurations that solve the subtask and facilitate the subsequent process of finding paths in a task planning process. The simple approach presented, worked on a planar projection of the scene. "
Conference Paper: Capturing robot workspace structure: representing robot capabilities[Show abstract] [Hide abstract]
ABSTRACT: Humans have at some point learned an abstraction of the capabilities of their arms. By just looking at the scene they can decide which places or objects they can easily reach and which are difficult to approach. Possessing a similar abstraction of a robot arm's capabilities in its workspace is important for grasp planners, path planners and task planners. In this paper, we show that robot arm capabilities manifest themselves as directional structures specific to workspace regions. We introduce a representation scheme that enables to visualize and inspect the directional structures. The directional structures are then captured in the form of a map, which we name the capability map. Using this capability map, a manipulator is able to deduce places that are easy to reach. Furthermore, a manipulator can either transport an object to a place where versatile manipulation is possible or a mobile manipulator or humanoid torso can position itself to enable optimal manipulation of an object.Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on; 12/2007
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- "However, the specialized planner builds its work on an accessibility graph and does not integrate reasoning about the geometric constraints of the problem. The other approaches dealing with the link between task planning and geometric reasoning are mainly approaches for one kind of specific problems (Guéré and Alami 2001; Zacharias, Borst, and Hirzinger 2006). In (Cambon, Alami, and Gravot 2009), a planning architecture is presented, in which the definition of states of the world contains symbolic and geometric information. "
ABSTRACT: Planning a mission for a mobile robot involves the use of a symbolic and a geometric planner. The gap be- tween their internal representation of the environment is an open issue and current researches are conducted without unified formalisms and algorithms. In this pa- per, we propose to extend the classical planning formal- ism in order to model actions with geometric precon- ditions and we propose, develop and test a constraint satisfaction method based on non-linear programming that aims at defining a destination attitude for motion planning.