ON THE IMPLEMENTATION AND EVALUATION OF AbTweak

In this paper, we describe the implementation and evaluation of the A b T weak planning system, a test bed for studying and teaching concepts in partial-order planning, abstraction, and search control. We start by extending the hierarchical, precondition-elimination abstraction of ABSTRIPS to partial-order-based, least-commitment planners such as T weak . The resulting system, A b T weak , illustrates the advantages of using abstraction to improve the efficiency of search. We show that by protecting a subset of abstract conditions achieved so far, and by imposing a bias on search toward deeper levels in a hierarchy, planning efficiency can be greatly improved. Finally, we relate A b T weak to other planning systems SNLP, ALPINE, and SIPE by exploring their similarities and differences.     

模型检测规划中的状态分层方法

基于模型检测的规划方法是最近发展起来的新方法,它可以处理带有不确定性的规划问题.分别设计了对求弱规划解、强规划解和强循环规划解的问题中的状态进行分层的方法.状态被分层后,求规划解只需要在从上层到其下一层状态之间寻找状态动作序偶就可以了,其他状态动作序偶都可以去掉.分别获得了求弱规划解、强规划解和强循环规划解时状态被分层后的一些重要性质,这些性质是关于一些状态动作序偶是否可以不参与构成弱规划解、强规划解和强循环规划解的结论.通过所获得的性质可以将大量的状态动作序偶直接去掉,从而减少问题规模.以往的对基于模型检测规划的研究都是采用从目标状态开始的反向搜索方法,在状态被分层以后可以采用正向搜索技术展开相应的研究.     

用非线性分层策略生成机器人动作规划

分层策略可以求解复杂的机器人规划生成问题.本文提出一种崭新的分层规划算法,该算法将原问题分解为若干子问题,然后按各层的动作细化规则,由顶而底.直至求出机器人的本原动作序列.在每个层次上,运用非线性技术,使之对子问题的求解可交叉进行,从而消除了目标相互影响问题;在规划动作的过程中,建立了前后指针和评价函数,并提出了解决分层规划中动作的冗余和矛盾的方法,大大地提高了解题效率.该算法已应用于 NARP机器人规划生成系统,本文简介了用 Common Lisp 语言编程的特点.系统在 Sun 机上实现.     

Constraint partitioning in penalty formulations for solving temporal planning problems

In this paper, we study the partitioning of constraints in temporal planning problems formulated as mixed-integer nonlinear programming (MINLP) problems. Constraint partitioning is attractive because it leads to much easier subproblems, where each is a significant relaxation of the original problem. Moreover, each subproblem is very similar to the original problem and can be solved by any existing solver with little or no modification. Constraint partitioning, however, introduces global constraints that may be violated when subproblems are evaluated independently. To reduce the overhead in resolving such global constraints, we develop in this paper new conditions and algorithms for limiting the search space to be backtracked in each subproblem. Using a penalty formulation of a MINLP where the constraint functions of the MINLP are transformed into non-negative functions, we present a necessary and sufficient extended saddle-point condition (ESPC) for constrained local minimization. When the penalties are larger than some thresholds, our theory shows a one-to-one correspondence between a constrained local minimum of the MINLP and an extended saddle point of the penalty function. Hence, one way to find a constrained local minimum is to increase gradually the penalties of those violated constraints and to look for a local minimum of the penalty function using any existing algorithm until a solution to the constrained model is found. Next, we extend the ESPC to constraint-partitioned MINLPs and propose a partition-and-resolve strategy for resolving violated global constraints across subproblems. Using the discrete-space ASPEN and the mixed-space MIPS planners to solve subproblems, we show significant improvements on some planning benchmarks, both in terms of the quality of the plans generated and the execution times to find them.     

An architecture for the vertical integration of tooling considerations from design to process planning

To meet the competitive demands of modern manufacturing, it is necessary to reduce design times and enrich decision making by integrating process planning into the design activity using Concurrent Engineering principles. Although this is traditionally done through the interaction between designers and process planners, it is perhaps more desirable for a CAD system to have the functionality necessary to automatically advise the designer of the shop floor implications of design decisions. Cutting tool selection is an essential thread linking feature-based design of machined parts to process planning. Thus, the implementation of tooling considerations into design is an important requirement for an integrated CAD/CAPP system. This paper defines an architecture to enable the vertical integration of tooling considerations from early design to process planning and scheduling. The architecture is based on a five-level tool selection procedure which is mapped to a time-phased aggregate, management and detailed process planning framework. This paper draws on literature and the results of an industrial survey to identify the tooling methods suitable for integration within a CAD system and categorises them into the five levels of tool selection. The functions are then placed on a time-dependent framework that covers the progression of a product from design to process planning. The new functionality is being implemented as an object-oriented application called VITool, which is being developed so that it can be fully integrated within an existing CAD system.     

<Emphasis Type="Italic">κ</Emphasis>-<Emphasis Type="Italic">PMP</Emphasis>: Enhancing Physics-based Motion Planners with Knowledge-Based Reasoning

Physics-based motion planning is a challenging task, since it requires the computation of the robot motions while allowing possible interactions with (some of) the obstacles in the environment. Kinodynamic motion planners equipped with a dynamic engine acting as state propagator are usually used for that purpose. The difficulties arise in the setting of the adequate forces for the interactions and because these interactions may change the pose of the manipulatable obstacles, thus either facilitating or preventing the finding of a solution path. The use of knowledge can alleviate the stated difficulties. This paper proposes the use of an enhanced state propagator composed of a dynamic engine and a low-level geometric reasoning process that is used to determine how to interact with the objects, i.e. from where and with which forces. The proposal, called κ-PMP can be used with any kinodynamic planner, thus giving rise to e.g. κ-RRT. The approach also includes a preprocessing step that infers from a semantic abstract knowledge described in terms of an ontology the manipulation knowledge required by the reasoning process. The proposed approach has been validated with several examples involving an holonomic mobile robot, a robot with differential constraints and a serial manipulator, and benchmarked using several state-of-the art kinodynamic planners. The results showed a significant difference in the power consumption with respect to simple physics-based planning, an improvement in the success rate and in the quality of the solution paths.     

Improving Electricity Planning – Use of a Multicriteria Decision Making Model

Electricity supply planners are now expected to consider a wide range of supply and demand options to meet future power needs. The evaluation is also expected to consider multiple objectives in such diverse areas as quality of supply, cost, environmental and social impacts. Further, there are a range of views on the appropriated weightings for those various considerations. Traditional methods for determining choices are no longer adequate. This paper outlines the development of a powerful tool to facilitate a multiobjective-driven electricity planning process. The multicriteria decision making model allows the evaluation of options against a wide range of criteria, grouped in a hierarchical structure. A formalism for comparing options is explored, showing that the model allows a planner to compare the range of alternatives against both a desired level of performance and a minimum acceptable level. The architecture of a computer-based decision support system is briefly described.     

On the Comparison of Proof Planning Systems: , Ωmega and IsaPlanner

We present a framework for describing proof planners. This framework is based around a decomposition of proof planners into planning states, proof language, proof plans, proof methods, proof revision, proof control and planning algorithms.We use this framework to motivate the comparison of three recent proof planning systems, λCLaM, Ωmega and IsaPlanner, and demonstrate how the framework allows us to discuss and illustrate both their similarities and differences in a consistent fashion. This analysis reveals that proof control and the use of contextual information in planning states are key areas in need of further investigation.     

Designing and managing local area networks: A survey of LAN personnel

A survey was conducted of planners of 45 local area networks (LANS) of personal computers used for office work found in a large metropolitan area. Usable responses were obtained from the planners of 27 of the LANS, a response rate of 60%. The planners were asked to rank 79 LAN planning criteria and to identify problems they encountered in managing the LANS.Twenty-one planning criteria were identified by planners as important or very important in the design of LANS. Five planning criteria were identified as unimportant by the planners. The planning criteria considered important varied between general purpose LANS and LANS primarily serving special applications. The planning criteria considered important also varied with LAN size.Few problems were encountered by the planners in this survey and the amount of time the planners felt should be committed to managing the LANS was modest. Installation problems were the most frequently cited.     

Gaussian mixture spline trajectory: learning from a dataset,generating trajectories without one

Most optimization-based motion planners use a naive linear initialization, which does not use previous planning experience. We present an algorithm called ‘Gaussian mixture spline trajectory’ (GMST) that leverages motion datasets for generating trajectories for new planning problems. Unlike other trajectory prediction algorithms, our method does not retrieve trajectories from a dataset. Instead, it first uses a Gaussian mixture model (GMM) to modelize the likelihood of the trajectories to be inside the dataset and then uses the GMM's parameters to generate new trajectories. As the use of the dataset is restricted only to the learning phase it can take advantage of very large datasets. Using both abstract and robot system planning problems, we show that the GMST algorithm decreases the computation time and number of iterations of optimization-based planners while increasing their success rates as compared to that obtained with linear initialization.     

Global and regional path planners for integrated planning and navigation

This paper presents a hierarchical strategy for field mobile robots that incorporates path planning at different ranges. At the top layer is a global path planner that utilizes gross terrain characteristics, such as hills and valleys, to determine globally safe paths through the rough terrain. This information is then passed via waypoints to a regional layer that plans appropriate navigation paths using regional terrain characteristics. The global and regional path planners share the same map information, but at different ranges. The motion recommendations from the regional layer are then combined with those of the reactive navigation layer to provide reactive control for the mobile robot. Details of the global and regional path planners are discussed, and simulation and experimental results are presented. © 2005 Wiley Periodicals, Inc.     

Integrated planning and execution control for an autonomous mobile robot

We present a distributed hierarchical planning and execution monitoring system and its implementation on an actual mobile robot. The planning system is a distributed hierarchical domain independent system called FPS for Flexible Planning System. It is a rule based plan generation system with planning specific and domain specific rules. A planning solution to the ‘Boxes and Wedge’ Problem is presented.The Robot Control System (RCS) operates and monitors the robot in the real world. In order to allow real-time responses to asynchronous events (both internal and external), RCS consists of a rule-based decision kernel and a distributed set of sensor/effector monitors. RCS contains an execution model and may authorize local corrective actions, e.g., unexpected obstacle avoidance during execution of a trajectory. RCS also generates status and failure reports through which the PMs inform the different decision subsystems as to the robot's state and current capacities. The failure reports help the RCS and planners in correcting/replanning a plan that has aborted. An illustrative example of system behaviour is to be presented.     

TOPS: AN INTELLIGENT ASSOCIATE FOR TURNING OPERATION PLANNING

This article describes the development of a new modeling environment that supports the building of intelligent associates for turning operation planning. By intelligent associates, we mean a computer-based system that allows a process planner to assert a greater degree of control during a consultation than do traditional expert systems. Intelligent associates are able to assist process planners in a colleague-like manner while solving domain tasks. They relieve routine operations while supporting creative problem-solving. Multiple levels of human experience are supported, advising and constraining inexperienced planners while providing a flexible environment for experienced planners working on new planning strategies. Several Al-based techniques have been employed in the development of this new modeling environment. The utility of this environment is demonstrated by developing a turning operation planning intelligent associate (TOPS). TOPS assists a process planner by computing interactions and checking for consistency among the various attributes of a lathe machine-operation planning problem and searching databases for machines, tool holders, and inserts.     

State agnostic planning graphs: deterministic,non-deterministic,and probabilistic planning

Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source.We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i.e., search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.     

Formalizing planning knowledge for hierarchical planning

A hierarchical planning system achieves efficiency by planning with the most important conditions first, and considering details later in the planning process. Few attempts have been made to formalize the structure of the planning knowledge for hierarchical planning. For a given domain, there is usually more than one way to define its planning knowledge. Some of the definitions can lead to efficient planning, while others may not. In this paper, we provide a set of restrictions which defines the relationships between a non-primitive action and its set of subactions. When satisfied, these restrictions guarantee improved efficiency for hierarchical planning. One important feature of these restrictions is that they are syntactic and therefore do not depend on the particular structure of any plan. Along with these restrictions, we also provide algorithms for preprocessing the planning knowledge of a hierarchical planner. When used during planning, the preprocessed operator hierarchies can enable a planner to significantly reduce its search space.     

Sampling-Based Roadmap of Trees for Parallel Motion Planning

This paper shows how to effectively combine a sampling-based method primarily designed for multiple-query motion planning [probabilistic roadmap method (PRM)] with sampling-based tree methods primarily designed for single-query motion planning (expansive space trees, rapidly exploring random trees, and others) in a novel planning framework that can be efficiently parallelized. Our planner not only achieves a smooth spectrum between multiple-query and single-query planning, but it combines advantages of both. We present experiments which show that our planner is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of our planner is that it is significantly more decoupled than PRM and sampling-based tree planners. Exploiting this property, we designed and implemented a parallel version of our planner. Our experiments show that our planner distributes well and can easily solve high-dimensional problems that exhaust resources available to single machines and cannot be addressed with existing planners.     

3d-Gis for Urban Purposes

New developments in urban planning, especially in environmentally oriented analysis including noise, air pollution, urban climate etc., call for new demands on authorities and planners. Due to the increasing availability of informations systems and of 3D-data, planners and municipalities emphasize modeling the urban space in three dimensions. While the visualization aspect is often and detailed considered, only a few investigations about interactive aspects on urban planning are available. In this paper we present a framework for a 3D-urban-GIS. This includes conceptual aspects and a first outline and implementation of an application prototype. For this representation, new scopes have to be considered from data acquisition to modeling and to storage. First, the urban object space is classified in an hierarchical 3D object structure. In accordance to different planning levels (i.e., levels-of-detail), several data acquisition methods are fused to obtain 3D datasets. The results show that a context specific methodology has to be defined. This includes planning aspects that are traditionally not available in GIS. Based on test sites in Rostock and Stuttgart, a 3D-urban-GIS prototype is in development, joining aspects of a 3D-visualization interface and a database for 3D objects.