Interleaving Planning and Robot Execution for Asynchronous User Requests

ROGUE is an architecture built on a real robot which provides algorithms for the integration of high-level planning, low-level robotic execution, and learning. ROGUE addresses successfully several of the challenges of a dynamic office gopher environment. This article presents the techniques for the integration of planning and execution.ROGUE uses and extends a classical planning algorithm to create plans for multiple interacting goals introduced by asynchronous user requests. ROGUE translates the planner';s actions to robot execution actions and monitors real world execution. ROGUE is currently implemented using the PRODIGY4.0 planner and the Xavier robot. This article describes how plans are created for multiple asynchronous goals, and how task priority and compatibility information are used to achieve appropriate efficient execution. We describe how ROGUE communicates with the planner and the robot to interleave planning with execution so that the planner can replan for failed actions, identify the actual outcome of an action with multiple possible outcomes, and take opportunities from changes in the environment.ROGUE represents a successful integration of a classical artificial intelligence planner with a real mobile robot.     

A planning language for embedded systems

Recent research in planning is more and more focusing on planning systems working in ‘real world’ domains. These systems need to act in sense and represent the real world. Furthermore, no action, even if apparently simple, is guaranteed to succeed and, therefore, no planning can be ‘sound’ (with respect to the real world) without taking into account possible failures. This is mainly due to the intrinsic complexity of reality. A planning language is therefore required to represent explicitly failures, sensing tasks, planning tasks, and task combinations. In this paper, we propose a planning language (called ?) which addresses the above features. ? allows representing the basic planning activities, the control structures and the basic operations to deal with failures. As a consequence, a uniform representation is used to describe both acting/sensing in the external world and basic planning activities. In this paper, we give the syntax and the semantics of ?. Furthermore we also give some examples from an application (the project MAIA) which uses ? as planning language.     

An ACO hybrid metaheuristic for close–open vehicle routing problems with time windows and fuzzy constraints

The close–open vehicle routing problem is a realistic variant of the “classical” vehicle routing problem where the routes can be opened and closed, i.e. all the vehicles are not required to return to the depot after completing their service. This variant is a planning model that is a standard practice in business nowadays. Companies are contracting their deliveries to other companies that hire vehicles, and payment is made based on the distance covered by the vehicles. Available information on parameters in real world situations is also imprecise, and must be included in the optimization model and method. The aims of this paper are to formulate a model of this novel variant with time windows and imprecise constraints and to propose a fuzzy optimization approach and a hybrid metaheuristic for its solutions. The full proposal is applied to a real route planning problem with outsourcing, obtaining promising practical results. Customer demands and travel times are imprecise, thus capacity and time windows constraints are considered flexible and modelled as fuzzy constraints.     

Real world representation of a road network for route planning in GIS

This paper addresses a methodology to properly represent a road network in the geographic information system (GIS) for network analysis. Over the years, the real world has become too complex to model properly within a given information system, such as GIS. Ideally, when the real world is represented as accurately as possible, a GIS can answer a question in its virtual world that coincides with the exact answer in the real world. However, existing methods related to impedance modeling for each segment of a road network in a route planning analysis that includes only a distance or time variable do not give proper results. Hence, this study investigates how a road network can represent the real world in a GIS and offer route planning tools. To address this, first, additional realistic variables are taken into account. These include weather, sight-seeing information, road type, and so on. Second, to combine these variables, an impedance model (IM) using the analytical hierarchical process (AHP) method is proposed. Finally, all of the models are implemented and verified with a sensitivity analysis. The models were successfully implemented in this work. All of the paths of the route planning analysis were successfully matched with the drivers’ paths that would normally be chosen in reality. It is anticipated that the use of other techniques such as analytical network process (ANP) in addition to AHP would be useful to overcome the aforementioned problem.     

Incorporating decision-theoretic planning in a robot architecture

The goal of robotics research is to design a robot to fulfill a variety of tasks in the real world. Inherent in the real world is a high degree of uncertainty about the robot’s behavior and about the world. We introduce a robot task architecture, DTRC, that generates plans with actions that incorporate costs and uncertain effects, and states that yield rewards.The use of a decision-theoretic planner in a robot task architecture is demonstrated on the mobile robot domain of miniature golf. The miniature golf domain shows the application of decision-theoretic planning in an inherently uncertain domain, and demonstrates that by using decision-theoretic planning as the reasoning method in a robot task architecture, accommodation for uncertain information plays a direct role in the reasoning process.     

基于启发式搜索的灵活规划的算法研究与系统实现

随着智能规划研究的深入,经典规划已不能满足实际应用的需要.本文分析了经典规划无法满足实际应用要求及产生灵活规划的原因.在对启发式搜索和灵活规划深入研究的基础上,提出了利用启发式搜索的方法来处理灵活规划问题的思想,并给出了基于启发式搜索的灵活规划算法和求解模型.采用智能规划中的基准问题对该算法进行测试,实验表明该方法在处理很多领域问题上都可以得到非常好的效果.     

行为控制月球车路径规划技术

提出了一种新的路径规划方法即自主行为路径规划方法.该方法能够自动构造大范围 自然环境下与路径规划任务相关的一类拓扑结构,从而大大地提高自然环境下月球车路径规划 速度.该方法在Tangent-Bug切线法基础上引入一组自适应行为来构造切线拓扑图,它是人工 路径规划行为的模仿,克服了计算几何路径规划方法非线性计算时间的不足.文中给出了相关 定义、定理、算法及真实环境下的仿真结果.     

强循环规划的观察信息约简

在现实的规划领域中,观察信息的获得需要花费一定的代价,然而对于一个规划解来说,一些观察信息在执行规划解的过程中是不会被用到的,所以如何把这些不必要的观察信息去除从而节省实际过程中的成本是非常有必要的。针对强循环规划的观察信息展开研究,提出了一个对强循环规划观察信息进行约简的算法,该算法能够找出在完全可观察条件下强循环规划的最小观察变量集合,通过使用最小观察变量集合,可以进一步提高执行强循环规划解的效率。     

Sensor-based probabilistic roadmaps: experiments with an eye-in-hand system

We present a real implemented eye-in-hand test-bed system for sensor-based collision-free motion planning for articulated robot arms. The system consists of a PUMA 560 with a triangulation-based area-scan laser range finder (the eye) mounted on its wrist. The framework for our planning approach is inspired by recent motion planning research for the classical model-based case (known environment) and incrementally builds a roadmap that represents the connectivity of the free configuration space, as the robot senses the physical environment. We present some experimental results with our sensor-based planner running on this real test-bed. The robot is started in completely unknown and cluttered environments. Typically, the planner is able to reach (planning as it senses) the goal configuration in about 7-25 scans (depending on the scene complexity), while avoiding collisions with the obstacles throughout.     

On the predictability of domain‐independent temporal planners

Temporal planning is a research discipline that addresses the problem of generating a totally or a partially ordered sequence of actions that transform the environment from some initial state to a desired goal state, while taking into account time constraints and actions' duration. For its ability to describe and address temporal constraints, temporal planning is of critical importance for a wide range of real‐world applications. Predicting the performance of temporal planners can lead to significant improvements in the area, as planners can then be combined in order to boost the performance on a given set of problem instances. This paper investigates the predictability of the state‐of‐the‐art temporal planners by introducing a new set of temporal‐specific features and exploiting them for generating classification and regression empirical performance models (EPMs) of considered planners. EPMs are also tested with regard to their ability to select the most promising planner for efficiently solving a given temporal planning problem. Our extensive empirical analysis indicates that the introduced set of features allows to generate EPMs that can effectively perform algorithm selection, and the use of EPMs is therefore a promising direction for improving the state of the art of temporal planning, hence fostering the use of planning in real‐world applications.     

A universal planning system for hybrid domains

Many real world problems involve hybrid systems, subject to (continuous) physical effects and controlled by (discrete) digital equipments. Indeed, many efforts are being made to extend the current planning systems and modelling languages to support such kind of domains. However, hybrid systems often present also a nonlinear behaviour and planning with continuous nonlinear change that is still a challenging issue.     

To Plan or to Simply React? An Experimental Study of Action Planning in a Game Environment

Many contemporary computer games, notably action and role‐playing games, represent an interesting class of navigation‐intensive dynamic real‐time simulations inhabited by autonomous intelligent virtual agents (IVAs). Although higher level reasoning of IVAs in these domains seems suited for action planning, planning is not widely adopted in existing games and similar applications. Moreover, statistically rigorous study measuring performance of planners in decision making in a game‐like domain is missing. Here, five classical planners were connected to the virtual environment of Unreal Development Kit along with a planner for delete‐free domains (only positive preconditions and positive effects). Performance of IVAs employing those planners and IVAs with reactive architecture was measured on a class of game‐inspired test environments of various sizes and under different levels of external interference. The analysis has shown that planning agents outperform reactive agents if (i) the size of the problem is small or if (b) the environment changes are either hostile to the agent or infrequent. In delete‐free domains, specialized approaches are inferior to classical planners because the lower expressivity of delete‐free domains results in lower plan quality. These results can help to determine when planning is advantageous in games and for IVAs control in other dynamic real‐time environments.     

基于与状态无关的激活集的包含派生谓词的规划问题求解

派生谓词是PDDL2．2语言的新特性之一。在2004年的规划大赛IPC-4上,许多规划系统都无法求解包含派生谓词的两个标准竞赛问题。在经典规划中,派生谓词是指不受领域动作直接影响的谓词,它们在当前状态下的真值是在封闭世界假设中由某些基本谓词通过领域公理推导出来的。本文提出一种新的方法来求解包含派生谓词的规划问题,即用与状态无关的激活集来取代派生谓词用于放宽式规划中。     

Knowledge-based approaches for scheduling problems: a survey

Recent developments in artificial intelligence (AI) have led to the use of knowledge-based techniques for solving scheduling problems. The authors survey several existing intelligent planning and scheduling systems with the aim of providing a guide to the main AI techniques used. In view of the prevailing difference is usage of the terms planning and scheduling between AI and operations research (OR), a taxonomy of planning and scheduling problems is presented. The modeling of real world problems from closed deterministic worlds to complex real worlds is illustrated with a project scheduling example. Some of the more successful planning and scheduling systems are surveyed, and their features are highlighted. The AI approaches are consolidated into knowledge representation and problem solving in the project management context     

An open‐source system for vision‐based micro‐aerial vehicle mapping,planning, and flight in cluttered environments

We present an open‐source system for Micro‐Aerial Vehicle (MAV) autonomous navigation from vision‐based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field‐of‐view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real‐world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search‐and‐rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real‐world experiments.     

Anytime search in dynamic graphs

Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out.A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases.In this paper, we present an A^∗-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving dynamic graphs.     

基于反射的规划方法

本文针对机器人在实际环境中执行任务时所面临的不确定性和实时性提出了一种基于反射的规划方法．该方法在较好地利用全局信息的基础上，充分发挥出机器人自身的能动的反射作用．我们首先讨论该方法的基本原理，然后着重讨论它在移动机器人规划和导航中的应用．     

Information modelling for case-based construction planning of highway bridge projects

In practice, construction planning and control draws on large-scale project and corporate data repositories, which are often unstructured. This article argues that the development of a large-scale data repository should be the precursor to any case-based reasoning system development. The article presents a large number of conceptual object models, which were developed to identify the attributes and relationships between product and planning information comprehensively, using bridges as a representative product. The models were used to develop a large information repository implemented in a database management system to facilitate real world project information collation, organisation, and management to reflect the large-scale nature of construction projects in practice. The database acts as a source of cases and sub-cases that are retrieved and mapped into a case-base. These cases are considered individually for indexing, matching, retrieval, and validation purposes, facilitating the re-use of parts of multiple cases to construct new project plans. A prototype software model, CBRidge Planner, which was developed and tested with real world project cases to demonstrate the approach is presented.