Results 1  10
of
17
The LAMA planner: Guiding costbased anytime planning with landmarks
, 2010
"... LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudoheuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finitedomain rather ..."
Abstract

Cited by 55 (5 self)
 Add to MetaCart
LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudoheuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finitedomain rather than binary state variables and multiheuristic search. The latter is employed to combine the landmark heuristic with a variant of the wellknown FF heuristic. Both heuristics are costsensitive, focusing on highquality solutions in the case where actions have nonuniform cost. A weighted A ∗ search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A ∗ search greatly improves results, and shows synergy effects with the use of landmarks. 1.
RealTime Adaptive A*
 IN PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS
, 2008
"... Agents often have to solve series of similar search problems. Adaptive A * is a recent incremental heuristic search algorithm that solves series of similar search problems faster than A * because it updates the hvalues using information from previous searches. It basically transforms consistent hv ..."
Abstract

Cited by 44 (11 self)
 Add to MetaCart
Agents often have to solve series of similar search problems. Adaptive A * is a recent incremental heuristic search algorithm that solves series of similar search problems faster than A * because it updates the hvalues using information from previous searches. It basically transforms consistent hvalues into more informed consistent hvalues. This allows it to find shortest paths in state spaces where the action costs can increase over time since consistent hvalues remain consistent after action cost increases. However, it is not guaranteed to find shortest paths in state spaces where the action costs can decrease over time because consistent hvalues do not necessarily remain consistent after action cost decreases. Thus, the hvalues need to get corrected after action cost decreases. In this paper, we show how to do that, resulting in Generalized Adaptive A * (GAA*) that finds shortest paths in state spaces where the action costs can increase or decrease over time. Our experiments demonstrate that Generalized Adaptive A * outperforms breadthfirst search, A* and D * Lite for movingtarget search, where D * Lite is an alternative stateoftheart incremental heuristic search algorithm that finds shortest paths in state spaces where the action costs can increase or decrease over time.
The Joy of Forgetting: Faster Anytime Search via Restarting
"... {jtd7, ruml} at cs.unh.edu Anytime search algorithms solve optimisation problems by quickly finding a usually suboptimal solution and then finding improved solutions when given additional time. To deliver a solution quickly, they are typically greedy with respect to the heuristic costtogo estimate ..."
Abstract

Cited by 27 (13 self)
 Add to MetaCart
{jtd7, ruml} at cs.unh.edu Anytime search algorithms solve optimisation problems by quickly finding a usually suboptimal solution and then finding improved solutions when given additional time. To deliver a solution quickly, they are typically greedy with respect to the heuristic costtogo estimate h. In this paper, we first show that this lowh bias can cause poor performance if the heuristic is inaccurate. Building on this observation, we then present a new anytime approach that restarts the search from the initial state every time a new solution is found. We demonstrate the utility of our method via experiments in PDDL planning as well as other domains. We show that it is particularly useful for hard optimisation problems like planning where heuristics may be quite inaccurate and inadmissible, and where the greedy solution makes early mistakes.
Timebounded Lattice for Efficient Planning in Dynamic Environments ∗
"... For vehicles navigating initially unknown cluttered environments, current stateoftheart planning algorithms are able to plan and replan dynamicallyfeasible paths efficiently and robustly. It is still a challenge, however, to deal well with the surroundings that are both cluttered and highly dyna ..."
Abstract

Cited by 11 (2 self)
 Add to MetaCart
For vehicles navigating initially unknown cluttered environments, current stateoftheart planning algorithms are able to plan and replan dynamicallyfeasible paths efficiently and robustly. It is still a challenge, however, to deal well with the surroundings that are both cluttered and highly dynamic. Planning under these conditions is more difficult for two reasons. First, tracking and predicting the trajectories of moving objects (i.e., cars, humans) is very noisy. Second, the planning process is computationally more expensive because of the increased dimensionality of the statespace, with time as an additional variable. Moreover, replanning needs to be invoked more often since the trajectories of moving obstacles need to be constantly reestimated. In this paper, we develop a path planning algorithm that addresses these challenges. First, we choose a representation of dynamic obstacles that efficiently models their predicted trajectories and the uncertainty associated with the predictions. Second, to provide realtime guarantees on the performance of planning with dynamic obstacles, we propose to utilize a novel data structure for planning a timebounded lattice that merges together shortterm planning in time with longterm planning without time. We demonstrate the effectiveness of the approach in both simulations with up to 30 dynamic obstacles and on real robots.
Anytime motion planning using the RRT
 in IEEE International Conference on Robotics and Automation
, 2011
"... Abstract — The Rapidlyexploring Random Tree (RRT) algorithm, based on incremental sampling, efficiently computes motion plans. Although the RRT algorithm quickly produces candidate feasible solutions, it tends to converge to a solution that is far from optimal. Practical applications favor “anytime ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
Abstract — The Rapidlyexploring Random Tree (RRT) algorithm, based on incremental sampling, efficiently computes motion plans. Although the RRT algorithm quickly produces candidate feasible solutions, it tends to converge to a solution that is far from optimal. Practical applications favor “anytime” algorithms that quickly identify an initial feasible plan, then, given more computation time available during plan execution, improve the plan toward an optimal solution. This paper describes an anytime algorithm based on the RRT ∗ which (like the RRT) finds an initial feasible solution quickly, but (unlike the RRT) almost surely converges to an optimal solution. We present two key extensions to the RRT ∗ , committed trajectories and branchandbound tree adaptation, that together enable the algorithm to make more efficient use of computation time online, resulting in an anytime algorithm for realtime implementation. We evaluate the method using a series of Monte Carlo runs in a highfidelity simulation environment, and compare the operation of the RRT and RRT ∗ methods. We also demonstrate experimental results for an outdoor wheeled robotic vehicle. I.
ANA*: Anytime Nonparametric A*
"... Anytime variants of Dijkstra’s and A * shortest path algorithms quickly produce a suboptimal solution and then improve it over time. For example, ARA * introduces a weighting value (ε) to rapidly find an initial suboptimal path and then reduces ε to improve path quality over time. In ARA*, ε is base ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Anytime variants of Dijkstra’s and A * shortest path algorithms quickly produce a suboptimal solution and then improve it over time. For example, ARA * introduces a weighting value (ε) to rapidly find an initial suboptimal path and then reduces ε to improve path quality over time. In ARA*, ε is based on a linear trajectory with adhoc parameters chosen by each user. We propose a new Anytime A * algorithm, Anytime Nonparametric A * (ANA*), that does not require adhoc parameters, and adaptively reduces ε to expand the most promising node per iteration, adapting the greediness of the search as path quality improves. We prove that each node expanded by ANA * provides an upper bound on the suboptimality of the currentbest solution. We evaluate the performance of ANA * with experiments in the domains of robot motion planning, gridworld planning, and multiple sequence alignment. The results suggest that ANA * is as efficient as ARA * and in most cases: (1) ANA * finds an initial solution faster, (2) ANA * spends less time between solution improvements, (3) ANA * decreases the suboptimality bound of the currentbest solution more gradually, and (4) ANA * finds the optimal solution faster. ANA * is freely available from Maxim Likhachev’s Searchbased Planning Library (SBPL).
RapidlyExploring Roadmaps: Weighing Exploration vs. Refinement in Optimal Motion Planning
, 2010
"... Computing optimal motion plans requires both exploring the configuration space to identify free space regions as well as refining understanding of explored regions in order to optimize the plan. We present the rapidlyexploring roadmap (RRM), a new method for singlequery optimal motion planning t ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Computing optimal motion plans requires both exploring the configuration space to identify free space regions as well as refining understanding of explored regions in order to optimize the plan. We present the rapidlyexploring roadmap (RRM), a new method for singlequery optimal motion planning that allows the user to explicitly consider the tradeoff between exploration and refinement. Prior methods have focused solely on exploration or refine prematurely, resulting in wasted computation. RRM initially explores the configuration space like an RRT. Once a path is found, RRM uses a userspecified parameter to weigh whether to explore further or refine the explored space by adding edges to the current roadmap to enable finding higher quality paths in the explored space. We demonstrate the performance of RRM and the tradeoff between exploration and refinement using two examples, a point robot moving in a plane and a concentric tube robot capable of following curved trajectories that has the potential to assist physicians in minimally invasive medical procedures.
Asymptoticallyoptimal Path Planning for Manipulation using Incremental Samplingbased Algorithms
"... Abstract — A desirable property of path planning for robotic manipulation is the ability to identify solutions in a sufficiently short amount of time to be usable. This is particularly challenging for the manipulation problem due to the need to plan over highdimensional configuration spaces and to ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Abstract — A desirable property of path planning for robotic manipulation is the ability to identify solutions in a sufficiently short amount of time to be usable. This is particularly challenging for the manipulation problem due to the need to plan over highdimensional configuration spaces and to perform computationally expensive collision checking procedures. Consequently, existing planners take steps to achieve desired solution times at the cost of low quality solutions. This paper presents a planning algorithm that overcomes these difficulties by augmenting the asymptoticallyoptimal RRT ∗ with a sparse sampling procedure. With the addition of a collision checking procedure that leverages memoization, this approach has the benefit that it quickly identifies lowcost feasible trajectories and takes advantage of subsequent computation time to refine the solution towards an optimal one. We evaluate the algorithm through a series of Monte Carlo simulations of seven, twelve, and fourteen degree of freedom manipulation planning problems in a realistic simulation environment. The results indicate that the proposed approach provides significant improvements in the quality of both the initial solution and the final path, while incurring almost no computational overhead compared to the RRT algorithm. We conclude with a demonstration of our algorithm for singlearm and dualarm planning on Willow Garage’s PR2 robot. I.
Planning for Landing Site Selection in the Aerial Supply Delivery ∗
"... Autonomous aerial robots can sometimes be tasked to deliver supplies to various remote locations. Their high speed and operating altitude offer advantages over ground robots. However, UAVs need to take off and land, often requiring ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Autonomous aerial robots can sometimes be tasked to deliver supplies to various remote locations. Their high speed and operating altitude offer advantages over ground robots. However, UAVs need to take off and land, often requiring
Research Statement
"... Autonomous robots rely heavily on their ability to plan actions quickly, under massive uncertainty and with a large number of factors that affect execution. Graph searches such as Dijkstra’s search, BreadthFirst Search and A * [13] are highly popular means of finding leastcost plans due to their g ..."
Abstract
 Add to MetaCart
Autonomous robots rely heavily on their ability to plan actions quickly, under massive uncertainty and with a large number of factors that affect execution. Graph searches such as Dijkstra’s search, BreadthFirst Search and A * [13] are highly popular means of finding leastcost plans due to their generality, solid theoretical ground and simplicity in the implementation. Unfortunately, these methods can rarely be used to solve complex planning problems on real robotic systems because they don’t provide realtime performance guarantees, are limited to small (i.e., lowdimensional) problems and can not deal with problems that involve uncertainty. My research is driven by developing planning methods that are general, easytouse, theoretically wellgrounded but at the same time meet the requirements of real robotic systems. In particular, I develop novel graph search algorithms that solve complex and/or largescale problems in robotics in realtime while maintaining all the positive properties of graph searches. I use these algorithms to build planning modules for real robotic systems that perform challenging tasks. For example, together with my colleagues, I have developed a series of novel versions of A* search that are designed specifically for planning in partiallyknown and dynamic environments under severe time constraints. For instance, ARA * [7] an anytime version of A * finds an initial,