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 * 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 one to two orders of magnitudes faster than planning from scratch since it modifies previous search results locally. Consequently, it has been extensively used in mobile robotics. In this article, we introduce an alternative to Focussed Dynamic A * that implements the same navigation strategy but is algorithmically different. Focussed Dynamic A * Lite is simple, easy to understand, easy to analyze and easy to extend, yet is more efficient than Focussed Dynamic A*. We believe that our results will make D*-like replanning methods even more popular and enable robotics researchers to adapt them to additional applications. 1

In real world planning problems, time for deliberation is often limited. Anytime planenrs

We describe a heuristic search algorithm for generating optimal plans in a new class of decision problem, characterised by the incorporation of hidden state. The approach exploits the nature of the hidden state to reduce the state space by orders of magnitude. It then interleaves heuristic expansion of the reduced space with forwards and backwards propagation phases to produce a solution in a fraction of the time required by other techniques. Results are provided on an outdoor path planning application.

Many real-world planning problems require one to solve a series of similar planning tasks. In this case, replanning can be much faster than planning from scratch. In this paper, we introduce a novel replanning method for symbolic planning with heuristic search-based planners, currently the most popular planners. Our SHERPA replanner is not only the first heuristic search-based replanner but, different from previous replanners for other planning paradigms, it also guarantees that the quality of its plans is as good as that achieved by planning from scratch. We provide an experimental feasibility study that demonstrates the promise of SHERPA for heuristic search-based replanning.

Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be constant-bounded regardless of the problem size. Such agents plan only a few actions in a local search space and avoid getting trapped in heuristic local minima by improving their heuristic function over time. We extend a wide class of real-time search algorithms with automatically built graph abstraction. Extensive empirical evaluation in the domain of goaldirected navigation demonstrates that the use of abstraction accelerates learning of the heuristic function while maintaining real-time performance. The resulting algorithm outperforms virtually all tested algorithms simultaneously along negatively correlated performance measures.

We address the problem of nding the shortest path between two points in an unknown real physical environment, where a traveling agent must move around in the environment to explore unknown territory. Weintroduce the Physical-A * algorithm (PHA*) for solving this problem. PHA * expands all the mandatory nodes that A * would expand and returns the shortest path between the two points. However, due to the physical nature of the problem, the complexity of the algorithm is measured by the traveling e ort of the moving agent and not by the number of generated nodes, as in standard A*. PHA * is presented as atwo-level algorithm, such that its high level, A*, chooses the next node to be expanded and its low level directs the agent to that node in order to explore it. We presenta number of variations for both the high-level and low-level procedures and evaluate their performance theoretically and experimentally. We show that the travel cost of our best variation is fairly close to the optimal travel cost, assuming that the mandatory nodes of A * are known in advance. We then generalize our algorithm to the multi-agent case, where anumber of cooperative agents are designed to solve the problem. Speci cally, weprovide an experimental implementation for such a system. It should be noted that the problem addressed here is not a navigation problem, but rather a problem of nding the shortest path between two points for future usage. 1.

Abstract. We present an interpolation-based planning and replanning algorithm for generating smooth paths through non-uniform cost grids. Most grid-based path planners use discrete state transitions that artificially constrain an agent’s motion to a small set of possible headings (e.g. 0, π π, , etc). As a result, even the ‘optimal ’ grid planners produce unnatural, suboptimal 4 2 paths. Our approach uses linear interpolation during planning to calculate accurate path cost estimates for arbitrary positions within each grid cell and to produce paths with a continuous range of headings. Consequently, it is particularly well suited to planning smooth, least-cost trajectories for mobile robots. In this paper, we present a number of applications and results, a comparison to related algorithms, and several implementations on real robotic systems. 1

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 order for an autonomous agent to successfully complete its mission, the agent must be able to quickly replan on the fly, as unforeseen events arise in the environment. This is enabled through the use of temporally flexible plans, which allow the agent to adapt to execution uncertainties, by not over committing to timing constraints, and through continuous planners, which are able to replan at any point when the current plan fails. To achieve both of these requirements, planners must have the ability to reason quickly about timing constraints. We enable continuous, temporally flexible planning through a temporal consistency algorithm (ITC), which supports incremental consistency testing on a new type of disjunctive temporal constraint network, the Temporal Plan Network (TPN), and supports focused search through incremental conflict extraction. The ITC algorithm combines the speed of shortest-path algorithms known to network optimization with the spirit of incremental algorithms such as Incremental A * and those used within truth maintenance systems (TMS). Empirical studies of ITC applied to the Kirk temporal planner demonstrate an order of magnitude speed increase on cooperative air vehicle scenarios and on randomly generated plans.

In real world problems, time for deliberation is often limited. Anytime algorithms are beneficial in these conditions as they usually find a first, possibly highly suboptimal, solution very fast and then continually work on improving the solution until allocated time expires. While anytime algorithms are popular, existing anytime search methods are unable to provide a measure of goodness of their results. In this paper we propose the ARA* algorithm. ARA* is an anytime heuristic search which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution. In addition to the theoretical analysis we demonstrate the practical utility of ARA* with experiments on a simulated robot kinematic arm and dynamic path planning problem for an outdoor rover.