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101. Curve Shortening and the Rendezvous Problem for Mobile Autonomous Robots

Author

Smith, Stephen L., Broucke, Mireille E., and Francis, Bruce A.

Subjects
Computer Science - Robotics, Computer Science - Multiagent Systems, I.2.9
Abstract

If a smooth, closed, and embedded curve is deformed along its normal vector field at a rate proportional to its curvature, it shrinks to a circular point. This curve evolution is called Euclidean curve shortening and the result is known as the Gage-Hamilton-Grayson Theorem. Motivated by the rendezvous problem for mobile autonomous robots, we address the problem of creating a polygon shortening flow. A linear scheme is proposed that exhibits several analogues to Euclidean curve shortening: The polygon shrinks to an elliptical point, convex polygons remain convex, and the perimeter of the polygon is monotonically decreasing., Comment: 15 pages, 18 figures

Published
2006

102. Objective Assessment of Cognitive Impairment in Parkinson’s Disease Using Evolutionary Algorithm

Author

Picardi, Chiara, Cosgrove, Jeremy, Smith, Stephen L., Jamieson, Stuart, Alty, Jane E., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Squillero, Giovanni, editor, and Sim, Kevin, editor

Published
2017
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106. Monotonic Target Assignment for Robotic Networks

Author

Smith, Stephen L and Bullo, Francesco

Subjects
Asymptotic performance, distributed algorithms, optimization, target assignment, task allocation
Abstract

Consider an equal number of mobile robotic agents and distinct target locations dispersed in an environment. Each agent has a limited communication range and either: 1) knowledge of every target position or 2) a finite-range sensor capable of acquiring target positions and no a priori knowledge of target positions. In this paper we study the following target assignment problem: design a distributed algorithm with which the agents divide the targets among themselves and, simultaneously, move to their unique target. We evaluate an algorithm's performance by characterizing its worst-case asymptotic time to complete the target assignment; that is the task completion time as the number of agents (and targets) increases, and the size of the environment scales to accommodate them. We introduce the intuitive class of monotonic algorithms, and give a lower bound on its worst-case completion time. We design and analyze two algorithms within this class: the ETSP Assgmt algorithm which works under assumption 1), and the Grid Assgmt algorithm which works under either assumption 1) or 2). In “sparse environments,” where communication is infrequent, the ETSP Assgmt algorithm is within a constant factor of the optimal monotonic algorithm for worst-case initial conditions. In “dense environments,” where communication is more prevalent, the Grid Assgmt algorithm is within a constant factor of the optimal monotonic algorithm for worst-case initial conditions. In addition we characterize the performance of the Grid Assgmt algorithm for uniformly distributed targets and agents, and for the case when there are more agents than targets.

Published
2009

111. Scholarship

Author

Smith, Stephen L. J., Dwyer, Larry, Pearce, Philip L., Jafari, Jafar, editor, and Xiao, Honggen, editor

Published
2016
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112. Curve shortening and the rendezvous problem for mobile autonomous robots

Author

Smith, Stephen L, Broucke, Mireille E., and Francis, Bruce. A.

Subjects
curve shortening, distributed control, mobile autonomous robots
Abstract

If a smooth, closed, and embedded curve is deformed along its normal vector field at a rate proportional to its curvature, it shrinks to a circular point. This curve evolution is called Euclidean curve shortening and the result is known as the Gage-Hamilton-Grayson theorem. Motivated by the rendezvous problem for mobile autonomous robots, we address the problem of creating a polygon shortening flow. A linear scheme is proposed that exhibits several analogues to Euclidean curve shortening: The polygon shrinks to an elliptical point, convex polygons remain convex, and the perimeter of the polygon is monotonically decreasing.

Published
2007

113. Medical Applications of Evolvable Hardware

Author

Lones, Michael A., Smith, Stephen L., Rozenberg, Grzegorz, Series editor, Bäck, Thomas, Series editor, Eiben, A.E., Series editor, Kok, Joost N., Series editor, Spaink, Herman P., Series editor, Trefzer, Martin A., and Tyrrell, Andy M.

Published
2015
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115. Automated Motion Analysis of Adherent Cells in Monolayer Culture

Author

Zhang, Zhen, Bedder, Matthew, Smith, Stephen L., Walker, Dawn, Shabir, Saqib, Southgate, Jennifer, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lones, Michael, editor, Tyrrell, Andy, editor, Smith, Stephen, editor, and Fogel, Gary, editor

Published
2015
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116. Evolving Ensembles: What Can We Learn from Biological Mutualisms?

Author

Lones, Michael A., Lacy, Stuart E., Smith, Stephen L., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lones, Michael, editor, Tyrrell, Andy, editor, Smith, Stephen, editor, and Fogel, Gary, editor

Published
2015
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119. Evolving Computational Dynamical Systems to Recognise Abnormal Human Motor Function

Author

Lones, Michael A., Smith, Stephen L., Tyrrell, Andy M., Alty, Jane E., Jamieson, D. R. Stuart, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lones, Michael A., editor, Smith, Stephen L., editor, Teichmann, Sarah, editor, Naef, Felix, editor, Walker, James A., editor, and Trefzer, Martin A., editor

Published
2012
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120. Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy

Author

Dutta, Shamak (author), Wilde, N. (author), Tokekar, Pratap (author), Smith, Stephen L. (author), Dutta, Shamak (author), Wilde, N. (author), Tokekar, Pratap (author), and Smith, Stephen L. (author)

Abstract

We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the resources used (samples or tour length) while guaranteeing estimation accuracy. We give approximation algorithms for both problems in convex environments. These improve previously known results, both in terms of theoretical guarantees and in simulations. In addition, we disprove an existing claim in the literature on a lower bound for a solution to the sample placement problem., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Published
2023
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121. Optimizing Task Waiting Times in Dynamic Vehicle Routing

Author

Botros, Alexander (author), Gilhuly, Barry (author), Wilde, N. (author), Sadeghi, Armin (author), Alonso-Mora, J. (author), Smith, Stephen L. (author), Botros, Alexander (author), Gilhuly, Barry (author), Wilde, N. (author), Sadeghi, Armin (author), Alonso-Mora, J. (author), and Smith, Stephen L. (author)

Abstract

We study the problem of deploying a fleet of mobile robots to service tasks that arrive stochastically over time and at random locations in an environment. This is known as the Dynamic Vehicle Routing Problem (DVRP) and requires robots to allocate incoming tasks among themselves and find an optimal sequence for each robot. State-of-the-art approaches only consider average wait times and focus on high-load scenarios where the arrival rate of tasks approaches the limit of what can be handled by the robots while keeping the queue of unserviced tasks bounded, i.e., stable. To ensure stability, these approaches repeatedly compute minimum distance tours over a set of newly arrived tasks. This letter is aimed at addressing the missing policies for moderate-load scenarios, where quality of service can be improved by prioritizing long-waiting tasks. We introduce a novel DVRP policy based on a cost function that takes the p-norm over accumulated wait times and show it guarantees stability even in high-load scenarios. We demonstrate that the proposed policy outperforms the state-of-the-art in both mean and 95th percentile wait times in moderate-load scenarios through simulation experiments in the Euclidean plane as well as using real-world data for city scale service requests., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Published
2023
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122. Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems

Author

Botros, Alexander (author), Sadeghi, Armin (author), Wilde, N. (author), Alonso-Mora, J. (author), Smith, Stephen L. (author), Botros, Alexander (author), Sadeghi, Armin (author), Wilde, N. (author), Alonso-Mora, J. (author), and Smith, Stephen L. (author)

Abstract

Many problems in robotics seek to simultaneously optimize several competing objectives under constraints. A conventional approach to solving such multi-objective optimization problems is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto optimal solutions to the original multi-objective problem. However, finding an accurate representation of a Pareto front remains an important challenge. Using uniformly spaced weight vectors is often inefficient and does not provide error bounds. Thus, we address the problem of computing a finite set of weight vectors such that for any other weight vector, there exists an element in the set whose error compared to optimal is minimized. To this end, we prove fundamental properties of the optimal cost as a function of the weight vector, including its continuity and concavity. Using these, we propose an algorithm that greedily adds the weight vector least-represented by the current set, and provide bounds on the error. Finally, we illustrate that the proposed approach significantly outperforms uniformly distributed weights for different robot planning problems with varying numbers of objective functions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control

Published
2023
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126. Anytime Replanning of Robot Coverage Paths for Partially Unknown Environments

Author

Ramesh, Megnath, Imeson, Frank, Fidan, Baris, and Smith, Stephen L.

Abstract

In this article, we propose a method to replan coverage paths for a robot operating in an environment with initially unknown static obstacles. Existing coverage approaches reduce coverage time by covering along the minimum number of coverage lines (straight-line paths). However, recomputing such paths online can be computationally expensive resulting in robot stoppages that increase coverage time. A naive alternative is greedy detour replanning, i.e., replanning with minimum deviation from the initial path, which is efficient to compute but may result in unnecessary detours. In this work, we propose an anytime coverage replanning approach named OARP-Replan that performs near-optimal replans to an interrupted coverage path within a given time budget. We do this by solving linear relaxations of integer linear programs to identify sections of the interrupted path that can be optimally replanned within the time budget. We validate OARP-Replan in simulation and perform comparisons against a greedy detour replanner and other state-of-the-art coverage planners. We also demonstrate OARP-Replan in experiments using an industrial-level autonomous robot.

Published
2024
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127. Regret-Based Sampling of Pareto Fronts for Multiobjective Robot Planning Problems

Author

Botros, Alexander, Wilde, Nils, Sadeghi, Armin, Alonso-Mora, Javier, and Smith, Stephen L.

Abstract

Many problems in robotics seek to simultaneously optimize several competing objectives. A conventional approach is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto optimal solutions to the original multiobjective problem. However, finding an accurate representation of a Pareto front remains an important challenge. Uniformly spaced weights are often inefficient and do not provide error bounds. We address the problem of computing a finite set of weights whose optimal solutions closely approximate the solution of any other weight vector. To this end, we prove fundamental properties of the optimal cost as a function of the weight vector. We propose an algorithm that greedily adds the weight vector least-represented by the current set, and provide bounds on the regret. We extend our method to include suboptimal solvers for the scalarized optimization, and handle stochastic inputs to the planning problem. Finally, we illustrate that the proposed approach significantly outperforms baseline approaches for different robot planning problems with varying numbers of objective functions.

Published
2024
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128. Predictive Dead Reckoning for Online Peer-to-Peer Games

Author

Walker, Tristan, Gilhuly, Barry, Sadeghi, Armin, Delbosc, Matt, and Smith, Stephen L.

Abstract

In online peer-to-peer games, players send periodic updates to each other and each player must locally reconstruct the position of their opponents in between these updates. In scenarios where players are driving cars, high speeds produce more pronounced errors in local replication of online opponents. In this work, we propose a new method of replicating opponents with less data sent and up to 45% less error compared to the state-of-the-art. We use a neural network-based approach to predict an opponent's position, combined with a path tracking controller from the field of mobile robotics, to produce smooth, believable trajectories for opponents' vehicles. We also propose a neural network-based approach to predict a replicated opponent's trajectory following a collision with a static obstacle.

Published
2024
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129. Spatio-Temporal Lattice Planning Using Optimal Motion Primitives

Author

Botros, Alexander and Smith, Stephen L.

Abstract

Lattice-based planning techniques simplify the motion planning problem for autonomous vehicles by limiting available motions to a pre-computed set of primitives. These primitives are combined online to generate complex maneuvers. A set of motion primitives $t$ -span a lattice if, given a real number $t\geq 1$ , any configuration in the lattice can be reached via a sequence of motion primitives whose cost is no more than a factor of $t$ from optimal. Computing a minimal $t$ -spanning set balances a trade-off between computed motion quality and motion planning performance. In this work, we formulate this problem for an arbitrary lattice as a mixed integer linear program. We also propose an A*-based algorithm to solve the motion planning problem using these primitives and an algorithm that removes the excessive oscillations from planned motions – a common problem in lattice-based planning. Our method is validated for autonomous driving in both parking lot and highway scenarios.

Published
2023
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145. Multiple Network CGP for the Classification of Mammograms

Author

Völk, Katharina, Miller, Julian F., Smith, Stephen L., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Giacobini, Mario, editor, Brabazon, Anthony, editor, Cagnoni, Stefano, editor, Di Caro, Gianni A., editor, Ekárt, Anikó, editor, Esparcia-Alcázar, Anna Isabel, editor, Farooq, Muddassar, editor, Fink, Andreas, editor, and Machado, Penousal, editor

Published
2009
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146. Hardware Acceleration of an Immune Network Inspired Evolutionary Algorithm for Medical Diagnosis

Author

Smith, Stephen L., Greensted, Andrew, Timmis, Jon, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Hornby, Gregory S., editor, Sekanina, Lukáš, editor, and Haddow, Pauline C., editor

Published
2008
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150. Positional Independence and Recombination in Cartesian Genetic Programming

Author

Cai, Xinye, Smith, Stephen L., Tyrrell, Andy M., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Collet, Pierre, editor, Tomassini, Marco, editor, Ebner, Marc, editor, Gustafson, Steven, editor, and Ekárt, Anikó, editor

Published
2006
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