1 edition of **A stochastic approach to path planning in the Weighted-Region Problem** found in the catalog.

A stochastic approach to path planning in the Weighted-Region Problem

Mark Richard Kindl

- 333 Want to read
- 32 Currently reading

Published
**1991**
by Naval Postgraduate School, Available from the National Technical Information Service in Monterey, Calif, Springfield, Va
.

Written in English

**Edition Notes**

Contributions | Shing, Man-Tak |

The Physical Object | |
---|---|

Pagination | 261 p. |

Number of Pages | 261 |

ID Numbers | |

Open Library | OL25506581M |

recent international planning competition, and shows com-petitive performance with top ranking systems. This is the ﬁrst demonstration of feasibility of this approach and it shows that abstraction through compact representation is a promis-ing approach to stochastic planning. Introduction Relational MDPs have been recently investigated as a for-. stochastic shortest-path problem can have actions that incur positive or negative costs. But several subsequent researchers, including Barto et al. [Barto, Bradtke, and Singh ], assume that a stochastic shortest-path problem only has actions that incur positive costs. The latter assumption is in keeping with the model of planning problems.

Stochastic (from from Greek στόχος (stókhos) 'aim, guess'.) is any randomly determined process. In mathematics the terms stochastic process and random process are interchangeable.. Stochastic processes appear in many different fields, including the physical sciences such as biology, chemistry, ecology, neuroscience, and physics as well as technology and engineering fields such as image. Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space. Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and .

The book is rounded off by an appendix providing mathematical underpinning on subjects such as ordinary differential equations, probabilistic measure theory and stochastic modeling, which will help the non-expert-mathematician to appreciate the text. Cem Ünsal Chapter 3. Stochastic Learning Automata 33 w F(•,•): Φ × β → Φ is a function that maps the current state and input into the next state. F can be deterministic or stochastic: φ (n + 1) = F[φ (n),β (n)] ()w H(•,•): Φ × β → α is a function that maps the current state and input into the current output. If the current output depends on only the current state.

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Full text of "A stochastic approach to path planning in the Weighted-Region Problem." See other formats. A stochastic approach to path planning in the Weighted-Region Problem. By Mark Richard Kindl. Get PDF (13 MB) Abstract.

Approved for public release; distribution is unlimitedPlanning efficient long-range movement is a fundamental requirement of most military operations. Weighted-region problem, Path annealing, A* search Author: Mark Richard Kindl. The objective is to find a minimum-cost path through the weighted regions from a start point to a goal point.

Our efficient algorithm combines heuristic search with probabilistic optimization by simulated annealing. It explores constrained random perturbations to the sequence of edges through which the path passes; then for each sequence, it solves a convex local-optimization subproblem to find the locally-optimal path and its cost.

This paper presents an efficient heuristic algorithm for planning near-optimal high-level paths for a point agent through complex terrain modeled by the Weighted-Region Problem. The input to the Weighted-Region Problem is a set of non-overlapping convex homogeneous-cost regions on Author: Mark R.

Kindl, Neil C. Rowe and Man-Tak Shing. Two-stage stochastic programming approach for path planning problems under travel time and availability uncertainties SARAVANAN VENKATACHALAM1, MANISH BANSAL2, JONATHON M. SMEREKA 3, JOSEPH LEE4 1Department of Industrial and Systems Engineering, Wayne State University 2Department of Industrial and Systems Engineering, Virginia Tech 3Ground Vehicle Cited by: 1.

Significant advances in sensing, robotics, and wireless networks have enabled the collaborative utilization of autonomous aerial, ground and underwater vehicles for various applications. However, to successfully harness the benefits of these unmanned ground vehicles (UGVs) in homeland security operations, it is critical to efficiently solve UGV path planning problem which lies at the heart.

Before a stochastic approach to the conjugate heat transfer is detailed, we consider first the issue of temperature fluctuations within a layer of solid material only; the fluctuations are driven by a variable temperature at the boundary.

To start with, let us recall the analytical solution of the following problem: determine the time-evolving temperature field T(y, t) in a semi-infinite solid. Another approach to solve the path planning problem is found in (Ba rraquand & Latombe, ), where a special kind of planners, named RPP (Random Path Planners), is proposed: local minima are.

Planning, replanning, Stochastic Shortest Path Problems 1. INTRODUCTION The problem of an autonomous agent moving in an en-vironment to ﬁnd objects while minimizing the search cost is ubiquitous in the real world, e.g., a taxi driver looking for passengers and minimizing the.

Step 2: Use the sample path from Step 1 and nite di erence method to solve the SDE for the particular choice of sample path.

Step 3: Repeat Step 1 and 2 many times. This gives a probability distribution of the random stochastic process f(t;B. Monte Carlo simulation is based on the idea that the resulting. Request PDF | Short-sighted stochastic shortest path problems | Two extreme approaches can be applied to solve a probabilistic planning problem, namely closed loop algorithms and open loop (a.k.a.

– Optimal if guaranteed to find the shortest path (if it exists) Approaches –Cell decomposition –Roadmaps –Sampling Techniques (RRT, DRT, PRM.) –On-line algorithms D*, ARA*. In all cases: Reduce the intractable problem in continuous C-space to a tractable problem in a discrete space Use all of the techniques we know (A*, stochastic.

The determination of the critical path (CP) in stochastic networks is difficult. It is partly due to the randomness of path durations and partly due to the probability issue of the selection of the critical path in the network.

What we are confronted with is not only the complexity among random variables but also the problem of path dependence of the network. This paper describes an efficient stochastic algorithm for planning near-optimal paths for a point agent moving through two-dimensional weighted-region terrain from a specified start point to a specified goal point.

Abstract. This paper surveys recent results for stochastic discrete programming models of hierarchical planning problems. Practical problems of this nature typically involve a sequence of decisions over time at an increasing level of detail and with increasingly accurate information.

In stochastic but time-invariant networks, least expected time paths can be determined by setting each random arc weight to its expected value and solving an equivalent deterministic problem. This paper addresses the problem of determining least expected time paths in stochastic.

We consider a stochastic version of the classical shortest path problem whereby for each node of a graph, we must choose a probability distribution over the set of successor nodes so as to reach a certain destination node with minimum expected cost.

The costs of transition between successive nodes can be positive as well as negative. The Wiener process is a stochastic process with stationary and independent increments that are normally distributed based on the size of the increments. The Wiener process is named after Norbert Wiener, who proved its mathematical existence, but the process is also called the Brownian motion process or just Brownian motion due to its historical connection as a model for Brownian movement in.

Stochastic programming is an approach for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known pa- problem () separates into the sum of optimal values of problems of the form () with d=dk.

Such decomposable structure is typical for two-stage linear stochastic. The general OP is defined as a path planning problem, but in most applications the aim is to find a tour. The selective traveling salesperson problem and the time-constrained traveling salesman problem are always defined as a tour planning problem.

The OP has many interesting applications in logistics, tourism and defense. Stochastic Shortest Path Problems 1In this chapter, we study a stochastic version of the shortest path problem of chapter 2, where only probabilities of transitions along diﬀerent arcs can be controlled, and the objective is to minimize the expected length of the path.

We discuss Bellman’s equation, value and policy iteration, for the case of a.Motion planning, also path planning (also known as the navigation problem or the piano mover's problem) is computational problem to find a sequence of valid configurations that moves the object from the source to destination.

The term is used in computational geometry, computer animation, robotics and computer games. For example, consider navigating a mobile robot inside a building to a.demands. In Escudero et al. (), a multi-stage stochastic programming approach was proposed to address a MPMP production planning model with random demand.

Sox and Muckstadt () provided a formulation and solution algorithm for the finite-horizon capacitated production planning problem with random demand for multiple products.