4,354 research outputs found
Experience-Based Planning with Sparse Roadmap Spanners
We present an experienced-based planning framework called Thunder that learns
to reduce computation time required to solve high-dimensional planning problems
in varying environments. The approach is especially suited for large
configuration spaces that include many invariant constraints, such as those
found with whole body humanoid motion planning. Experiences are generated using
probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which
provides asymptotically near-optimal coverage of the configuration space,
making storing, retrieving, and repairing past experiences very efficient with
respect to memory and time. The Thunder framework improves upon past
experience-based planners by storing experiences in a graph rather than in
individual paths, eliminating redundant information, providing more
opportunities for path reuse, and providing a theoretical limit to the size of
the experience graph. These properties also lead to improved handling of
dynamically changing environments, reasoning about optimal paths, and reducing
query resolution time. The approach is demonstrated on a 30 degrees of freedom
humanoid robot and compared with the Lightning framework, an experience-based
planner that uses individual paths to store past experiences. In environments
with variable obstacles and stability constraints, experiments show that
Thunder is on average an order of magnitude faster than Lightning and planning
from scratch. Thunder also uses 98.8% less memory to store its experiences
after 10,000 trials when compared to Lightning. Our framework is implemented
and freely available in the Open Motion Planning Library.Comment: Submitted to ICRA 201
A Complete Coverage Algorithm for 3D Structural Inspection using an Autonomous Unmanned Aerial Vehicle
This thesis presents a novel algorithm for complete coverage of three-dimensional structures to address the problem of autonomous structural inspection using an Unmanned Aerial Vehicle (UAV). The proposed approach uses a technique of cellular decomposition based on Morse decomposition to decompose the 3D target structure into 2D coverable faces that are subsequently connected using a graph-based representation. We then use graph traversal techniques such as the Traveling Salesman Problem (TSP) to generate a flight coverage path through the decomposed faces for a UAV to completely cover the target structure, while reducing the coverage time and distance. To test the validity of our proposed approach, we have performed a series of experiments using a simulated AscTec Firefly UAV in different environments with 3D structures of different sizes and geometries, within the Robot Operating System (ROS) Gazebo simulator. Our results show that our approach guarantees complete coverage of the target structure. Comparison of our coverage strategy with other strategies shows that our proposed TSP-based coverage strategy performs up to 50% better in reducing the flight path with an average of 30% fewer turns and 12% less coverage duration than a largest-area-first approach
Efficient Multi-Robot Coverage of a Known Environment
This paper addresses the complete area coverage problem of a known
environment by multiple-robots. Complete area coverage is the problem of moving
an end-effector over all available space while avoiding existing obstacles. In
such tasks, using multiple robots can increase the efficiency of the area
coverage in terms of minimizing the operational time and increase the
robustness in the face of robot attrition. Unfortunately, the problem of
finding an optimal solution for such an area coverage problem with multiple
robots is known to be NP-complete. In this paper we present two approximation
heuristics for solving the multi-robot coverage problem. The first solution
presented is a direct extension of an efficient single robot area coverage
algorithm, based on an exact cellular decomposition. The second algorithm is a
greedy approach that divides the area into equal regions and applies an
efficient single-robot coverage algorithm to each region. We present
experimental results for two algorithms. Results indicate that our approaches
provide good coverage distribution between robots and minimize the workload per
robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), 201
A Game-theoretic Formulation of the Homogeneous Self-Reconfiguration Problem
In this paper we formulate the homogeneous two- and three-dimensional
self-reconfiguration problem over discrete grids as a constrained potential
game. We develop a game-theoretic learning algorithm based on the
Metropolis-Hastings algorithm that solves the self-reconfiguration problem in a
globally optimal fashion. Both a centralized and a fully distributed algorithm
are presented and we show that the only stochastically stable state is the
potential function maximizer, i.e. the desired target configuration. These
algorithms compute transition probabilities in such a way that even though each
agent acts in a self-interested way, the overall collective goal of
self-reconfiguration is achieved. Simulation results confirm the feasibility of
our approach and show convergence to desired target configurations.Comment: 8 pages, 5 figures, 2 algorithm
Incremental Sampling-based Algorithms for Optimal Motion Planning
During the last decade, incremental sampling-based motion planning
algorithms, such as the Rapidly-exploring Random Trees (RRTs) have been shown
to work well in practice and to possess theoretical guarantees such as
probabilistic completeness. However, no theoretical bounds on the quality of
the solution obtained by these algorithms have been established so far. The
first contribution of this paper is a negative result: it is proven that, under
mild technical conditions, the cost of the best path in the RRT converges
almost surely to a non-optimal value. Second, a new algorithm is considered,
called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost
of the best path in the RRG converges to the optimum almost surely. Third, a
tree version of RRG is introduced, called the RRT algorithm, which
preserves the asymptotic optimality of RRG while maintaining a tree structure
like RRT. The analysis of the new algorithms hinges on novel connections
between sampling-based motion planning algorithms and the theory of random
geometric graphs. In terms of computational complexity, it is shown that the
number of simple operations required by both the RRG and RRT algorithms is
asymptotically within a constant factor of that required by RRT.Comment: 20 pages, 10 figures, this manuscript is submitted to the
International Journal of Robotics Research, a short version is to appear at
the 2010 Robotics: Science and Systems Conference
Sampling-based Algorithms for Optimal Motion Planning
During the last decade, sampling-based path planning algorithms, such as
Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have
been shown to work well in practice and possess theoretical guarantees such as
probabilistic completeness. However, little effort has been devoted to the
formal analysis of the quality of the solution returned by such algorithms,
e.g., as a function of the number of samples. The purpose of this paper is to
fill this gap, by rigorously analyzing the asymptotic behavior of the cost of
the solution returned by stochastic sampling-based algorithms as the number of
samples increases. A number of negative results are provided, characterizing
existing algorithms, e.g., showing that, under mild technical conditions, the
cost of the solution returned by broadly used sampling-based algorithms
converges almost surely to a non-optimal value. The main contribution of the
paper is the introduction of new algorithms, namely, PRM* and RRT*, which are
provably asymptotically optimal, i.e., such that the cost of the returned
solution converges almost surely to the optimum. Moreover, it is shown that the
computational complexity of the new algorithms is within a constant factor of
that of their probabilistically complete (but not asymptotically optimal)
counterparts. The analysis in this paper hinges on novel connections between
stochastic sampling-based path planning algorithms and the theory of random
geometric graphs.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics
Researc
A survey on active simultaneous localization and mapping: state of the art and new frontiers
Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics
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