Hierarchical task network and operator-based planning: two complementary approaches to real-world planning

Work on generative planning systems has focused on two diverse approaches to plan construction. Hierarchical task network (HTN) planners build plans by successively refining high-level goals into lower-level activities. Operator-based planners employ means-end analysis to directly formulate plans consisting of low-level activities. While many have argued the universal dominance of a single approach, this paper presents an alternative view: that in different situations either may be most appropriate. To support this view, a number of advantages and disadvantages of these approaches are described in light of experiences in developing two real-world, fielded planning systems.     

Image-based robot task planning and control using a compact visualrepresentation

We present an approach for the design and control of both reflexive and purposive visual tasks. The approach is based on the bidimensional appearance of the objects in the environment and explicitly takes into account independent object motions. A linear model of camera-object interaction is embedded in the control scheme, which dramatically simplifies visual analysis and control by reducing the size of visual representation. We describe the implementation of three visual tasks of increasing complexity, obtained with the proposed scheme and based on the active contour analysis and polynomial planning of image contour transformations. Both simulations and real-time experiments with a robotic eye-in-hand configuration are discussed, validating the approach in terms of robustness and applicability to visual navigation, active exploration and perception, and human-robot interaction     

Effects of physical exertion on task performance in modern manufacturing: a taxonomy,a review,and a model

Modern manufacturing systems have the characteristic of demanding human cognitive task performance in an environment where sudden physical demands are also possible. There is little research addressing the effects of physical work on cognitive task performance and it is not well organized for application to manufacturing systems design. This paper uses an initial three-dimensional taxonomy to review the literature. Some clear findings emerge, but many studies produced contradictory results. While part of this contradiction was due to experimental technique, some was also due to the lack of a detailed structure to guide future research. Such a model is thus proposed, using the concept of limited resources for both the cognitive and physical aspects of tasks. In addition, specific predictions of research needs for modern manufacturing systems arise.     

Friendship-aware task planning in mobile crowdsourcing

Recently, crowdsourcing platforms have attracted a number of citizens to perform a variety of location-specific tasks. However, most existing approaches consider the arrangement of a set of tasks for a set of crowd workers, while few consider crowd workers arriving in a dynamic manner. Therefore, how to arrange suitable location-specific tasks to a set of crowd workers such that the crowd workers obtain maximum satisfaction when arriving sequentially represents a challenge. To address the limitation of existing approaches, we first identify a more general and useful model that considers not only the arrangement of a set of tasks to a set of crowd workers, but also all the dynamic arrivals of all crowd workers. Then, we present an effective crowd-task model which is applied to offline and online settings, respectively. To solve the problem in an offline setting, we first observe the characteristics of task planning (CTP) and devise a CTP algorithm to solve the problem. We also propose an effective greedy method and integrated simulated annealing (ISA) techniques to improve the algorithm performance. To solve the problem in an online setting, we develop a greedy algorithm for task planning. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments using real and synthetic datasets.     

The many faces of inheritance: a taxonomy of taxonomy

One of the most important considerations in constructing object-oriented software is the methodology of inheritance: how to use this mechanism well. The paper presents a taxonomy comprising twelve kinds of inheritance usage grouped into three broad categories: model inheritance; software inheritance; and variation inheritance     

Multihierarchical interactive task planning: application to mobile robotics.

To date, no solution has been proposed to human-machine interactive task planning that deals simultaneously with two important issues: 1) the capability of processing large amounts of information in planning (as it is needed in any real application) and 2) being efficient in human-machine communication (a proper set of symbols for human-machine interaction may not be suitable for efficient automatic planning and vice versa). In this paper, we formalize a symbolic model of the environment to solve these issues in a natural form through a human-inspired mechanism that structures knowledge in multiple hierarchies. Planning with a hierarchical model may be efficient even in cases where the lack of hierarchical information would make it intractable. However, in addition, our multihierarchical model is able to use the symbols that are most familiar to each human user for interaction, thus achieving efficiency in human-machine communication without compromising the task-planning performance. We formalize here a general interactive task-planning process which is then particularized to be applied to a mobile robotic application. The suitability of our approach has been demonstrated with examples and experiments.     

Robotic furniture assembly: task abstraction,motion planning,and control

Intelligent Service Robotics - This paper presents a novel framework for the complete assembly of a chair based on assembly instructions. First, the framework utilizes task templates and task...     

Robot assembly planning using a task grammar augmented with heuristic knowledge

Automated assembly planning in a manufacturing environment requires not only mathematically sound formal methods and algorithmic computations but also heuristic knowledge. Much of this knowledge can be extracted from manual task manipulation strategies, such as the motion classification scheme used in methods-time measurement (MTM) studies. In this paper, we delineate various task-level operations in the context of robotic assembly, and show how these operations can be organized in the form of a task grammar. The proposed task grammar captures the intrinsic principle on how the sequence of robot operations should be ordered and how one high-level operation can be effectively decomposed into low-level operations. In order to control the process of robot task decomposition, we explicitly represent and apply qualitative heuristic knowledge about task constraints and operation applicability. In the paper, we first describe how syntactical knowledge about robot operations can be formulated for assembly-related manipulation tasks. Next, through illustrative examples, we attempt to show how qualitative knowledge can be effectively used in the task decomposition in three distinct ways: heuristic-based operation pattern matching, spatial-feature-based qualitative state envisionment, and canonical transformation of task environments.     

The top architecture for multiagent task planning and scheduling

This paper describes a planner known as TOP. TOP is an acronym for Task Oriented Planner. The planning concept in TOP extends the methods of the GPS means-ends-analysis and NOAH's least commitment approach to include deliberation. The TOP has been implemented in two environments — in aircraft turnaround function scheduling and constraint-directed microblock world involving teleoperation.     

Reverse engineering and design recovery: a taxonomy

The key to applying computer-aided software engineering to the maintenance and enhancement of existing systems lies in applying reverse-engineering approaches. However, there is considerable confusion over the terminology used in both technical and marketplace discussions. The authors define and relate six terms: forward engineering, reverse engineering, redocumentation, design recovery, restructuring, and reengineering. The objective is not to create new terms but to rationalize the terms already in use. The resulting definitions apply to the underlying engineering processes, regardless of the degree of automation applied     

Automated instructional design for CSCL: A hierarchical task network planning approach

In Computer Supported Collaborative Learning (CSCL), one of the most important tasks for instructional designers is to define scenarios that foster group learning. Such scenarios, defined as Units of Learning (UoLs), comprise different components and are organized according to pedagogical approaches to orchestrate group learning processes. Examples of UoL components are learning objects, student roles, student characteristics (e.g., background, preferences, learning styles, etc.), instructional/learning goals, and activities, among others. Thus, the instructional design (ID) of a proper UoL for CSCL is a complex task that requires practice and experience. This is particularly true when designing, developing, adapting, and customizing UoLs, taking into consideration different instructional/learning goals and individual preferences of students. This paper therefore proposes using a Hierarchical Task Network (HTN) planning approach to automate and optimize the tasks of designers. To accomplish that, we define an initial CSCL scenario as “an ID task” and “a set of information related to students and the domain to be taught.” Then we propose a model that formally describes ID for CSCL as HTN planning, where the initial CSCL scenario is adapted and refined according to student needs. In this model, the ID strategies are defined as hierarchical tasks and methods into a planning domain definition, and the initial CSCL scenario is defined as a planning problem definition. To validate our approach, we develop a CSCL courseware generator that (i) helps designers to set up an initial CSCL scenario; (ii) automatically generates a personalized UoL based on a given initial scenario; and (iii) supports the adaptation of UoLs.     

Physics-based virtual reality for task learning and intelligent disassembly planning

Physics-based simulation is increasingly important in virtual manufacturing for product assembly and disassembly operations. This work explores potential benefits of physics-based modeling for automatic learning of assembly tasks and for intelligent disassembly planning in desktop virtual reality. The paper shows how realistic physical animation of manipulation tasks can be exploited for learning sequential constraints from user demonstrations. In particular, a method is proposed where information about physical interaction is used to discover task precedences and to reason about task similarities. A second contribution of the paper is the application of physics-based modeling to the problem of disassembly sequence planning. A novel approach is described to find all physically admissible subassemblies in which a set of rigid objects can be disassembled. Moreover, efficient strategies are presented aimed at reducing the computational time required for automatic disassembly planning. The proposed strategies take into account precedence relations arising from user assembly demonstrations as well as geometrical clustering. A motion planning technique has also been developed to generate non-destructive disassembly paths in a query-based approach. Experiments have been performed in an interactive virtual environment including a dataglove and motion tracker that allows realistic object manipulation and grasping.     

RALPH static planner: CAD-based manipulator assembly task planning for CSG-modeled objects

This article describes a manipulator assembly task planner that processes the knowledge of the working environment and generates a sequence of general, manipulator independent commands. the planner takes a very high level command, such as “insert PEG into HOLE” without further specifications, reasons about the involved object features using the information from the CAD system, and generates a process plan for the manipulator to automatically perform the task. In this planner, the grasp planning and the path planning are developed and implemented for a static world.     

Assembly operation process planning by mapping a virtual assembly simulation to real operation

Virtual assembly has been widely used in product development. However, virtual operation and actual operation are different in time and space, the simulation of interactive virtual assembly cannot support the assembly operation's process planning directly. In this paper, the solution for assembly operation's process planning is developed based on interactive virtual assembly. According to the solution, interactive assembly operation is used to obtain the actions of operation sequence. The actions are mapped into the data of a real operation action to obtain real operation actions. Then assembly operation cards can be obtained. To support the assembly operation actions obtained through virtual assembly simulation, a product assembly model is proposed. An operation semantic model is used to replace the geometric constraint model of assembly, which contains several ordered geometric constraints and some engineering restriction conditions. To test the solution and the models, one process planning example of an automobile engine is introduced. The results verified the feasibility and the effectiveness of the methods.     

Perception-based learning for motion in contact in task planning

This paper presents a new approach to error detection during motion in contact under uncertainty for robotic manufacturing tasks. In this approach, artificial neural networks are used for perception-based learning. The six force-and-torque signals from the wrist sensor of a robot arm are fed into the network. A self-organizing map is what learns the different contact states in an unsupervised way. The method is intended to work properly in complex real-world manufacturing environments, for which existent approaches based on geometric analytical models may not be feasible, or may be too difficult. It is used for different tasks involving motion in contact, particularly the peg-in-hole insertion task, and complex insertion or extraction operations in a flexible manufacturing system. Several real examples for these cases are presented.Category: (8) AI in Robotics and Manufacturing/FMS.     

Hierarchical planning with state abstractions for temporal task specifications

Autonomous Robots - We often specify tasks for a robot using temporal language that can include different levels of abstraction. For example, the command “go to the kitchen before going to...     

Two-handed assembly with immersive task planning in virtual reality

Assembly modelling is the process of capturing entities and activity information related to assembling and assembly. Currently, most CAD systems have been developed to ease the design of individual components, but are limited in their support for assembly designs and planning capability, which are crucial for reducing the cost and processing time in complex design, constraint analysis and assembly task planning. This paper presents a framework of a two-handed virtual assembly (VA) planner for assembly tasks, which coordinates two hands jointly for feature-based manipulation, assembly analysis and constraint-based task planning. Feature-based manipulation highlights the important assembling features (e.g. dynamic reference frames, moving arrow, mating features) to guide users for the ease of assembly and in an efficient and fluid manner. The users can freely navigate and move the mating pair along the collision-free path. The free motion of two-handed input in assembly is further restricted to the allowable motion guided by the constraints recognised on-line. The allowable motion in assembly is planned by the logic steps derived from the analysis of constraints and their translation in the progress of assembly. No preprocessing or predefined assembly sequence is necessary since the planning is produced in real-time upon the two-handed interactions. Mating features and constraints in databases are automatically updated after each assembly to simplify the planning process. The two-handed task planner has been developed and experimented for several assembly examples including a drill (12-parts) and a robot (17-parts). The system can be generally applied for the interactive task planning of assembly-type applications.