SHOP2: An HTN Planning System

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains

SHOP2: An HTN planning system

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.open17833

Algorithms for Rapidly Dispersing Robot Swarms in Unknown Environments

We develop and analyze algorithms for dispersing a swarm of primitive robots in an unknown environment, R. The primary objective is to minimize the makespan, that is, the time to fill the entire region. An environment is composed of pixels that form a connected subset of the integer grid. There is at most one robot per pixel and robots move horizontally or vertically at unit speed. Robots enter R by means of k>=1 door pixels Robots are primitive finite automata, only having local communication, local sensors, and a constant-sized memory. We first give algorithms for the single-door case (i.e., k=1), analyzing the algorithms both theoretically and experimentally. We prove that our algorithms have optimal makespan 2A-1, where A is the area of R. We next give an algorithm for the multi-door case (k>1), based on a wall-following version of the leader-follower strategy. We prove that our strategy is O(log(k+1))-competitive, and that this bound is tight for our strategy and other related strategies.Comment: 17 pages, 4 figures, Latex, to appear in Workshop on Algorithmic Foundations of Robotics, 200

Execution cost optimization for hierarchical planning in the now

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 73-75).For robots to effectively interact with the real world, they will need to perform complex tasks over long time horizons. This is a daunting challenge, but human ability to routinely solve these problems leads us to believe that there is underlying structure we can leverage to find solutions. Recent advances using hierarchical planning [19] have been able to solve these problems by breaking a single long-horizon problem into several short-horizon problems. While this approach is able to effectively solve real world robotics planning problems, it makes no effort to account for the execution cost of an abstract plan and often arrives at poor quality plans. In this thesis, we analyze situations that lead to execution cost inefficiencies in hierarchical planners. We argue that standard optimization techniques from flat planning or search are likely to be ineffective in addressing these issues. We outline an algorithm, RCHPN, that improves a hierarchical plan by considering peephole optimizations during execution. We frame the underlying question as one of evaluating the resource needs of an abstract operator and propose a general way to approach estimating them. We introduce the marsupial logistics domain to study the effectiveness of this approach. We present experiments in large problem instances from marsupial logistics and observed up to 30% reduction in execution cost when compared with a standard hierarchical planner.by Dylan Hadfield-Menell.M. Eng

An investigation of the needs and the design of an orbiting space station with growth capabilities

An architectural approach to the evolutionary growth of an orbiting space station from a small manned satellite to a fully independent, self-sustainable space colony facility is presented. Social and environmental factors, ease of transportation via the space shuttle, and structural design are considered

View-Invariant Regions and Mobile Robot Self-Localization

This paper addresses the problem of mobile robot self-localization given a polygonal map and a set of observed edge segments. The standard approach to this problem uses interpretation tree search with pruning heuristics to match observed edges to map edges. Our approach introduces a preprocessing step in which the map is decomposed into 'view-invariant regions' (VIRs). The VIR decomposition captures information about map edge visibility, and can be used for a variety of robot navigation tasks. Basing self-localization search on VIRs greatly reduces the branching factor of the search tree and thereby simplifies the search task. In this paper we define the VIR decomposition and give algorithms for its computation and for self-localization search. We present results of simulations comparing standard and VIR-based search, and discuss the application of the VIR decomposition to other problems in robot navigation

Optimal Planning of Container Terminal Operations

Due to globalization and international trade, moving goods using a mixture of transportation modes has become a norm; today, large vessels transport 95% of the international cargos. In the first part of this thesis, the emphasis is on the sea-land intermodal transport. The availability of different modes of transportation (rail/road/direct) in sea-land intermodal transport and container flows (import, export, transhipment) through the terminal are considered simultaneously within a given planning time horizon. We have also formulated this problem as an Integer Programming (IP) model and the objective is to minimise storage cost, loading and transportation cost from/to the customers. To further understand the computational complexity and performance of the model, we have randomly generated a large number of test instances for extensive experimentation of the algorithm. Since, CPLEX was unable to find the optimal solution for the large test problems; a heuristic algorithm has been devised based on the original IP model to find near „optimal‟ solutions with a relative error of less than 4%. Furthermore, we developed and implemented Lagrangian Relaxation (LR) of the IP formulation of the original problem. The bounds derived from LR were improved using sub-gradient optimisation and computational results are presented. In the second part of the thesis, we consider the combined problems of container assignment and yard crane (YC) deployment within the container terminal. A new IP formulation has been developed using a unified approach with the view to determining optimal container flows and YC requirements within a given planning time horizon. We designed a Branch and Cut (B&C) algorithm to solve the problem to optimality which was computationally evaluated. A novel heuristic approach based on the IP formulation was developed and implemented in C++. Detailed computational results are reported for both the exact and heuristic algorithms using a large number of randomly generated test problems. A practical application of the proposed model in the context of a real case-study is also presented. Finally, a simulation model of container terminal operations based on discrete-event simulation has been developed and implemented with the view of validating the above optimisation model and using it as a test bed for evaluating different operational scenarios

Logistic system design of an underground freight pipeline system

"July 2014."Dissertation Supervisor: Dr. James Noble.Includes vita.Underground Freight Pipeline (UFP) systems utilize the underground space in metro areas that is otherwise not utilized for freight transportation. Two fundamental logistics issues in the design of a UFP system are network configuration and capsule control. This research develops two capsule control models that minimize total tardiness squared of cargo delivery and associated heuristic algorithms to solve large-scale problems. Two network design models are introduced that minimizes both operational and construction cost of UFP system. The UFP network design Comprehensive Model can only be solved to optimality for small sized problem. To reduce the computational complexity, the UFP network design Two Step Model that is able to generate high quality network design solutions is developed. Then, a case study of a UFP network design in Greater New York area is presented.Includes bibliographical references (pages 159-162)