Mobile robots often operate in domains that are only incompletely known, for example, when they have to move from given start coordinates to given goal coordinates in unknown terrain. In this case, they need to be able to replan quickly as their knowledge of the terrain changes. Stentz’ Focussed Dynamic A * (D*) is a heuristic search method that repeatedly determines a shortest path from the current robot coordinates to the goal coordinates while the robot moves along the path. It is able to replan faster than planning from scratch since it modifies its previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to D * that determines the same paths and thus moves the robot in the same way but is algorithmically different. D * Lite is simple, can be rigorously analyzed, extendible in multiple ways, and is at least as efficient as D*. We believe that our results will make D*-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications.

In dynamic environments, agents have to deal with changing situations. In these cases, repairing a plan is often more efficient than planning from scratch, but existing planning techniques are more advanced than existing plan repair techniques. Therefore, we propose a straightforward method to extend planning techniques such that they are able to repair plans. This is possible, because plan repair consists of two different operations: (i) removing obstructing constraints (such as actions) from the plan, and (ii) adding actions to achieve the goals. Adding actions is similar to planning, but as we demonstrate, planning heuristics can also be used for removing constraints, which we call unrefinement. We present a plan repair template that reflects these two operations, and we present a heuristic for unrefinement that can make use of an arbitrary existing planning technique. We apply this method to an existing planning system (VHPOP) resulting in POPR, a plan repair system that performs much better than replanning from scratch, and also significantly better than another recent plan repair method (GPG). Furthermore, we show that the plan repair template is a generalisation of existing plan repair methods.

Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important since many artificial intelligence systems have to adapt their plans continuously to changes in (their knowledge of) the world. In this article, we therefore give an overview of incremental search, focusing on Lifelong Planning A*, and outline some of its possible applications in artificial intelligence. Overview It is often important that searches be fast. Artificial intelligence has developed several ways of speeding up searches by trading off the search time and the cost of the resulting path. This includes using inadmissible heuristics (Pohl

Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task is extremely difficult if no prior information is available and is trivial if perfect prior information is available and the position of the robot is precisely known. Perfect prior maps are rare, but good-quality, high-resolution prior maps are increasingly available. Although the position of the robot is usually known through the use of the Global Position System (GPS), there are many scenarios in which GPS is not available, or its reliability is compromised by different types of interference such as mountains, buildings, foliage or jamming. If GPS is not available, the position estimate of the robot depends on dead-reckoning alone, which drifts with time and can accrue very large errors. Most existing approaches to path planning and navigation for outdoor environments are unable to use prior maps if the position of the robot is not precisely known. Often these approaches end up performing the much harder task of navigating without prior information. This thesis addresses the problem of planning paths with uncertainty in position for large outdoor environments. The objective is to be able to reliably navigate autonomously

In this paper, we develop Fringe-Saving A* (FSA*), an incremental version of A * that repeatedly finds shortest paths in a known gridworld from a given start cell to a given goal cell while the traversability costs of cells increase or decrease. The first search of FSA * is the same as that of A*. However, FSA * is able to find shortest paths during the subsequent searches faster than A * because it reuses the beginning of the immediately preceeding A * search tree that is identical to the current A* search tree. FSA * does this by restoring the content of the OPEN list of A * at the point in time when an A * search for the current search problem could deviate from the A * search for the immediately preceeding search problem. We present first experimental results that demonstrate that FSA * can have a runtime advantage over A * and Lifelong Planning A * (LPA*), an alternative incremental version of A*. 1

Abstract — In this paper we present a new method for kinodynamic motion planning in environments that contain both static and moving obstacles. We present an efficient twostage approach: in the preprocessing phase, it constructs a roadmap that is collision-free with respect to the static obstacles and encodes the kinematic constraints on the robot. In the query phase, it plans a time-optimal path on the roadmap that obeys the dynamic constraints (bounded acceleration, curvature derivative) on the robot and avoids collisions with any of the moving obstacles. We do not put any constraints on the motions of the moving obstacles, but we assume that they are completely known when a query is performed. We implemented our method, and experiments confirm its good performance. I.

Abstract—In this paper, we address the problem of answering continuous route planning queries over a road network, in the presence of updates to the delay (cost) estimates of links. A simple approach to this problem would be to recompute the best path for all queries on arrival of every delay update. However, such a naive approach scales poorly when there are many users who have requested routes in the system. Instead, we propose two new classes of approximate techniques – K-paths and proximity measures to substantially speed up processing of the set of designated routes specified by continuous route planning queries in the face of incoming traffic delay updates. Our techniques work through a combination of precomputation of likely good paths and by avoiding complete recalculations on every delay update, instead only sending the user new routes when delays change significantly. Based on an experimental evaluation with 7,000 drives from real taxi cabs, we found that the routes delivered by our techniques are within 5 % of the best shortest path and have run times an order of magnitude or less compared to a naive approach. I.

This paper describes a study performed with the objective of investigating Concurrent Constraint Programming (CCP) as the main tool for the design and the implementation of a software path planner. The main features of the path planning are introduced along with some modeling and implementation issues. The CCP approach is motivated by the facility of translating complex models with domain specific features into efficient implementations. In order to use CCP, the path planning is formalized as a constraint satisfaction problem by defining variables, domains, and constraints. The proposed solution is as general as possible, and it is widely applicable in several contexts. A demo application has been implemented, and it is described in this paper. The application comes with a graphical interface that allow the user to define the mission constraints. The output of the application is a path plan, i.e. a list of waypoints that can also be displayed on a geographical map. The estimated path length, fuel consumption, and path risk are given as well.

Autonomy for autonomous vehicles is achieved at the level of decision making. This paper discusses the lack of autonomous decision making in current unmanned vehicle platforms. Although current unmanned vehicles are often termed as autonomous, they rely on the communications and decisions established with and by the operator. In order to achieve this autonomy during mission without requiring operator feedback, the study proposes the integration of novel approaches in the context of on-line plan repair algorithms capable of providing machine behavioural awareness for on-line mission adaptation and recoverability.

Kambhampati’s refinement planning framework provides a unifying view on planning algorithms. It shows how a planning problem can be solved by successively applying a refinement operator, which adds more and more detail to the plan. In this paper, we show how this framework can be extended to include plan repair capabilities. This extension relies on the inclusion of unrefinement operators, that remove obsolete or obstructing actions from the plan. Based on this general plan repair framework, we develop a generic plan repair heuristic. This heuristic consists of a new unrefinement strategy, and it can make use of existing planning heuristics as the refinement operator. The unrefinement strategy deteriorates a plan in a number of ways, by removing certain combinations of actions. The planning heuristic is then used to find the most promising candidate, which is then given to the planner as the initial partial plan to refine. The big advantage of the heuristic is that it allows to employ existing planning heuristics. This way, it can not only make use of advances in planning technology, but it can also be used to extend existing planners, giving them plan repair capabilities. The performance of the heuristic is demonstrated by adapting an existing planner, and comparing the results with planning from scratch and a plan adaptation system. Experiments are presented that show that the new heuristic is very competitive with the other systems. 1