Showing papers by "Maxim Likhachev published in 2012"

Topological constraints in search-based robot path planning

Abstract: There are many applications in motion planning where it is important to consider and distinguish between different topological classes of trajectories. The two important, but related, topological concepts for classifying manifolds that are of importance to us are those of homotopy and homology. In this paper we consider the problem of robot exploration and planning in Euclidean configuration spaces with obstaclees to (a) identify and represent different homology classes of trajectories; (b) plan trajectories constrained to certain homology classes or avoiding specified homology classes; and (c) explore different homotopy classes of trajectories in an environment and determine the least cost trajectories in each class. We exploit theorems from complex analysis and the theory of electromagnetism to solve the problem 2-dimensional and 3-dimensional configuration spaces respectively. Finally, we describe the extension of these ideas to arbitrary D-dimensional configuration spaces. We incorporate these basic concepts to develop a practical graph-search based planning tool with theoretical guarantees by combining integration theory with search techniques, and illustrate it with several examples.

Anytime search-based footstep planning with suboptimality bounds

Abstract: Efficient footstep planning for humanoid navigation through cluttered environments is still a challenging problem. Many obstacles create local minima in the search space, forcing heuristic planners such as A* to expand large areas. The goal of this work is to efficiently compute long, feasible footstep paths. For navigation, finding the optimal path initially is often not needed as it can be improved while walking. Thus, we propose anytime search-based planning using the anytime repairing A* (ARA*) and randomized A* (R*) planners. This allows to obtain efficient paths with provable suboptimality within short planning times. Opposed to completely randomized methods such as rapidly-exploring random trees (RRTs), these planners create paths that are goal-directed and guaranteed to be no more than a certain factor longer than the optimal solution. We thoroughly evaluated the planners in various scenarios using different heuristics. ARA* with the 2D Dijkstra heuristic yields fast and efficient solutions but its potential inadmissibility results in non-optimal paths for some scenarios. R*, on the other hand borrows ideas from RRTs, yields fast solutions, and is less dependent on a well-designed heuristic function. This allows it to avoid local minima and reduces the number of expanded states.

Navigation in three-dimensional cluttered environments for mobile manipulation

Abstract: Collision-free navigation in cluttered environments is essential for any mobile manipulation system. Traditional navigation systems have relied on a 2D grid map projected from a 3D representation for efficiency. This approach, however, prevents navigation close to objects in situations where projected 3D configurations are in collision within the 2D grid map even if actually no collision occurs in the 3D environment. Accordingly, when using such a 2D representation for planning paths of a mobile manipulation robot, the number of planning problems which can be solved is limited and suboptimal robot paths may result. We present a fast, integrated approach to solve path planning in 3D using a combination of an efficient octree-based representation of the 3D world and an anytime search-based motion planner. Our approach utilizes a combination of multi-layered 2D and 3D representations to improve planning speed, allowing the generation of almost real-time plans with bounded sub-optimality. We present extensive experimental results with the two-armed mobile manipulation robot PR2 carrying large objects in a highly cluttered environment. Using our approach, the robot is able to efficiently plan and execute trajectories while transporting objects, thereby often moving through demanding, narrow passageways.

E-Graphs: Bootstrapping Planning with Experience Graphs

Abstract: Human environments possess a significant amount of underlying structure that is under-utilized in motion planning and mobile manipulation In domestic environments for example, walls and shelves are static, large objects such as furniture and kitchen appliances most of the time do not move and do not change, and objects are typically placed on a limited number of support surfaces such as tables, countertops or shelves Motion planning for robots operating in such environments should be able to exploit this structure to improve its performance with each execution of a task In this paper, we develop an online motion planning approach which learns from its planning episodes (experiences) a graph, an Experience Graph This graph represents the underlying connectivity of the space required for the execution of the mundane tasks performed by the robot The planner uses the Experience graph to accelerate its planning efforts whenever possible On the theoretical side, we show that planning with Experience graphs is complete and provides bounds on sub-optimality with respect to the graph that represents the original planning problem On the experimental side, we show in simulations and on a physical robot that our approach is particularly suitable for higher-dimensional motion planning tasks such as planning for single-arm manipulation and two armed mobile manipulation The approach provides significant speedups over planning from scratch and generates predictable motion plans: motions planned from start positions that are close to each other to goal positions that are also close to each other tend to be similar In addition, we show how the Experience graphs can incorporate solutions from other approaches such as human demonstrations, providing an easy way of bootstrapping motion planning for complex tasks

Anytime Safe Interval Path Planning for dynamic environments

Abstract: Path planning in dynamic environments is significantly more difficult than navigation in static spaces due to the increased dimensionality of the problem, as well as the importance of returning good paths under time constraints. Anytime planners are ideal for these types of problems as they find an initial solution quickly and then improve it as time allows. In this paper, we develop an anytime planner that builds off of Safe Interval Path Planning (SIPP), which is a fast A*-variant for planning in dynamic environments that uses intervals instead of timesteps to represent the time dimension of the problem. In addition, we introduce an optional time-horizon after which the planner drops time as a dimension. On the theoretical side, we show that in the absence of time-horizon our planner can provide guarantees on completeness as well as bounds on the sub-optimality of the solution with respect to the original space-time graph. We also provide simulation experiments for planning for a UAV among 50 dynamic obstacles, where we can provide safe paths for the next 15 seconds of execution within 0.05 seconds. Our results provide a strong evidence for our planner working under real-time constraints.

Planning with adaptive dimensionality for mobile manipulation

Abstract: Mobile manipulation planning is a hard problem composed of multiple challenging sub-problems, some of which require searching through large high-dimensional state-spaces. The focus of this work is on computing a trajectory to safely maneuver an object through an environment, given the start and goal configurations. In this work we present a heuristic search-based deterministic mobile manipulation planner, based on our recently-developed algorithm for planning with adaptive dimensionality. Our planner demonstrates reasonable performance, while also providing strong guarantees on completeness and suboptimality bounds with respect to the graph representing the problem.

Combining global and local planning with guarantees on completeness

Abstract: Planning with kinodynamic constraints is often required for mobile robots operating in cluttered, complex environments. A common approach is to use a two-dimensional (2-D) global planner for long range planning, and a short range higher dimensional planner or controller capable of satisfying all of the constraints on motion. However, this approach is incomplete and can result in oscillations and the inability to find a path to the goal. In this paper we present an approach to solving this problem by combining the global and local path planning problem into a single search using a combined 2-D and higher dimensional state-space.

Search-based planning for dual-arm manipulation with upright orientation constraints

Abstract: Dual-arm manipulation is an increasingly important skill for robots operating in home, retail and industrial environments. Dual-arm manipulation is especially essential for tasks involving large objects which are harder to grasp and manipulate using a single arm. In this work, we address dual-arm manipulation of objects in indoor environments. We are particularly focused on tasks that involve an upright orientation constraint on the grasped object. Such constraints are often present in human environments, e.g. when manipulating a tray of food or a container with fluids. In this paper, we present a search-based approach that is capable of planning dual-arm motions, often within one second, in cluttered environments while adhering to the orientation constraints. Our approach systematically constructs a graph in task space and generates motions that are consistent across runs with similar start/goal configurations and are low-cost. These motions come with guarantees on completeness and bounds on the suboptimality with respect to the graph that encodes the planning problem. For many problems, the consistency of the generated motions is important as it helps make the actions of the robot more predictable for a human interacting with the robot.

The University of Pennsylvania MAGIC 2010 multi-robot unmanned vehicle system

Abstract: In this report, we describe the technical approach and algorithms that have been used by the University of Pennsylvania in the MAGIC 2010 competition. We have constructed and deployed a multi-vehicle robot team, consisting of intelligent sensor and disrupter unmanned ground vehicles that can survey, map, recognize, and respond to threats in a dynamic urban environment with minimal human guidance. The custom hardware systems consist of robust and complementary sensors, integrated electronics, computation, and highly capable propulsion and actuation. The mapping, navigation, and planning software is organized hierarchically, allowing autonomous decisions to be made by the robots while enabling human operators to interact with the robot team in an efficient and strategic manner. The ground control station integrates information coming from the robots as well as metadata feeds to focus the attention of the operators and respond rapidly to emerging threats. These systems were developed and tested by the UPenn team to complete two phases of the MAGIC 2010 challenge in a safe and timely manner. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Using state dominance for path planning in dynamic environments with moving obstacles

Abstract: Path planning in dynamic environments with moving obstacles is computationally complex since it requires modeling time as an additional dimension. While in other domains there are state dominance relationships that can significantly reduce the complexity of the search, in dynamic environments such relationships do not exist. This paper presents a novel state dominance relationship tailored specifically for dynamic environments, and presents a planner that uses that property to plan paths over ten times faster than without using state dominance.

Heuristic search comes of age

Abstract: In looking back on the last five to ten years of work in heuristic search a few trends emerge. First, there has been a broadening of research topics studied. Second, there has been a deepened understanding of the theoretical foundations of search. Third, and finally, there have been increased connections with work in other fields. This paper, corresponding to a AAAI 2012 invited talk on recent work in heuristic search, highlights these trends in a number of areas of heuristic search. It is our opinion that the sum of these trends reflects the growth in the field and the fact that heuristic search has come of age.

Efficiently finding optimal winding-constrained loops in the plane

Abstract: We present a method to efficiently find winding-constrained loops in the plane that are optimal with respect to a minimumcost objective and in the presence of obstacles. Our approach is similar to a typical graph-based search for an optimal path in the plane, but with an additional state variable that encodes information about path homotopy. Upon finding a loop, the value of this state corresponds to a line integral over the loop that indicates how many times it winds around each obstacle, enabling us to reduce the problem of finding paths satisfying winding constraints to that of searching for paths to suitable states in this augmented state space. We give an intuitive interpretation of the method based on fluid mechanics and show how this yields a way to perform the necessary calculations efficiently. Results are given in which we use our method to find optimal routes for autonomous surveillance and intruder containment.

Game-based data capture for player metrics

Abstract: Player metrics are an invaluable resource for game designers and QA analysts who wish to understand players, monitor and improve game play, and test design hypotheses. Usually such metrics are collected in a straightforward manner by passively recording players; however, such an approach has several potential drawbacks. First, passive recording might fail to record metrics which correspond to an infrequent player behavior. Secondly, passive recording can be a costly, laborious, and memory intensive process, even with the aid of tools. In this paper, we explore the potential for an active approach to player metric collection which strives to collect data more efficiently, and thus with less cost. We use an online, iterative approach which models the relationship between player metrics and in-game situations probabilistically using a Markov Decision Process (MDP) and solves it for the best game configurations to run. To analyze the benefits and limitations of this approach, we implemented a system, called GAMELAB, for recording player metrics in Second Life.

The Symposium on Combinatorial Search

Abstract: The purpose of the Symposium on Combinatorial Search (SoCS) is to promote the study and understanding of combinatorial search algorithms through the organization of scientific meetings, publications, tutorials, and other public scientific and educational activities. The most prominent among its activities is the annual Symposium on Combinatorial Search that has been organized regularly since 2008 as a result of the success of other scientific meetings, typically workshops co-located with high-level Artificial Intelligence conferences (AAAI and IJCAI). This paper reviews the history of the Symposium making emphasis on the last edition that was held in July 2011 in Barcelona (Spain).

Motion Primitive-Based Graph Planning for Mobile Manipulation With Closed-Chain Systems

Abstract: Motion primitive-based (lattice-based) graphs have been used extensively in navigation, but application to high-dimensional state-spaces has remained limited by computational complexity. In this work, we show how these graphs can be applied to mobile manipulation. The formation of closed chains in tasks that involve contacts with the environment may reduce the number of available degrees of freedom but add complexity in terms of constraints in the high-dimensional state space. We propose a novel method to reduce dimensionality by abstracting away the constraints associated with closed-chain systems. Proofs are introduced for the application to graph-search and its theoretical guarantees of optimality. The dimensionality-reduction is done in a manner that enables finding optimal solutions to low-dimensional problems which map to correspondingly optimal full-dimensional solutions. We demonstrate the usefulness of our method with simulation results; we apply our approach to moving an object in 2D using a mobile manipulation platform with a planar arm. Disciplines Engineering Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-12-06. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/968

Efficiently Finding Optimal Winding-Constrained Loops in the Plane: Extended Abstract.

Abstract: We present a method to efficiently find winding-constrained loops in the plane that are optimal with respect to a minimumcost objective and in the presence of obstacles. Our approach is similar to a typical graph-based search for an optimal path in the plane, but with an additional state variable that encodes information about path homotopy. Upon finding a loop, the value of this state corresponds to a line integral over the loop that indicates how many times it winds around each obstacle, enabling us to reduce the problem of finding paths satisfying winding constraints to that of searching for paths to suitable states in this augmented state space. We give an intuitive interpretation of the method based on fluid mechanics and show how this yields a way to perform the necessary calculations efficiently. Results are given in which we use our method to find optimal routes for autonomous surveillance and intruder containment.