Robotic covert path planning: A survey

Marzouqi, Mohamed Al; Jarvis, Ray

doi:10.1109/ramech.2011.6070460

Cited by 21 publications

(13 citation statements)

References 19 publications

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“…where the process noise covariance is ten times larger for feature 1 (upper left) than for feature 2: Q (1) = 10I, Q (2) = I. The higher noise level implies that a higher measurement rate is required to keep the state estimate of the first feature accurate, compared to the second feature.…”

Section: Numerical Illustrationsmentioning

confidence: 99%

“…The problem of planning paths to minimize the exposure to adversarial observers, i.e., computing stealthy paths, has received some attention in the literature, and a survey of different approaches can be found in [1]. We will make use of binary variables to indicate whether the adversarial observer obtains measurements of the mobile sensor.…”

Section: Avoiding Adversarial Observersmentioning

confidence: 99%

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On Informative Path Planning for Tracking and Surveillance

Boström-Rost

2019

Linköping Studies in Science and Technology. Licentiate Thesis

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Section: Numerical Illustrationsmentioning

confidence: 99%

Section: Avoiding Adversarial Observersmentioning

confidence: 99%

On Informative Path Planning for Tracking and Surveillance

Boström-Rost

2019

Linköping Studies in Science and Technology. Licentiate Thesis

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“…Our study deals with multi-robot systems that traverse through the threats, while maintaining a connected formation. In [18], [19], [20], [21] a team of robots is considered in the problem of robot navigation under threats, but the robots carry out their traverses one at a time, sequentially, and do not travel in a formation.…”

Section: Background and Related Workmentioning

confidence: 99%

Path planning for optimizing survivability of multi-robot formation in adversarial environments

Shapira

Agmon

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Multi robot formation is a canonical problem in robotic research. The problem has been examined in neutral environments, where the robots' goal is usually to maintain the formation despite changes in the environment. The problem of multi robot formation has been motivated by natural phenomena such as schools of fish or flocks of birds. While in the natural phenomena the team behavior is responsive to threats, in robotics research of team formation, adversarial presence has been ignored. In this paper we present the problem of adversarial formation, in which a team of robots travels in a connected formation through an adversarial environment that includes threats that may harm the robots. The robots' goal is, therefore, to maximize their chance of traveling through the environment unharmed, where the formation may be used as a mean to achieve this goal. We formally define the problem, present a quantitative measure for evaluating the survivability of the team, and suggest possible solutions to a variant of the problem under certain threat characteristics, optimizing different team survivability criteria. Finally, we discuss the challenges raised by transitioning the discrete representation to a continuous environment in simulation.

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“…In addition, it does not scale well to handle constraint combinations and generally can result in non‐halting behaviors and inability to solve many problems. Computing global trajectories that account for spatial constraints with respect to obstacles and other agents is still a challenging problem that is of value to the community.…”

Section: Introductionmentioning

confidence: 99%

Planning approaches to constraint‐aware navigation in dynamic environments

Ninomiya

Kapadia

Shoulson

et al. 2014

Computer Animation & Virtual

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Path planning is a fundamental problem in many areas, ranging from robotics and artificial intelligence to computer graphics and animation. Although there is extensive literature for computing optimal, collision-free paths, there is relatively little work that explores the satisfaction of spatial constraints between objects and agents at the global navigation layer. This paper presents a planning framework that satisfies multiple spatial constraints imposed on the path. The type of constraints specified can include staying behind a building, walking along walls, or avoiding the line of sight of patrolling agents. We introduce two hybrid environment representations that balance computational efficiency and search space density to provide a minimal, yet sufficient, discretization of the search graph for constraint-aware navigation. An extended anytime dynamic planner is used to compute constraint-aware paths, while efficiently repairing solutions to account for varying dynamic constraints or an updating world model. We demonstrate the benefits of our method on challenging navigation problems in complex environments for dynamic agents using combinations of hard and soft, attracting and repelling constraints, defined by both static obstacles and moving obstacles.Keywords path planning, spatial constraints, navigation, anytime dynamic planning, potential fields Disciplines Computer Sciences | Engineering | Graphics and Human Computer Interfaces CommentsThe preprint version title is Constraint-Aware Navigation in Dynamic Environments; it was published in its final form as Planning Approaches to Constraint-Aware Navigation in Dynamic Environments.This journal article is available at ScholarlyCommons: http://repository.upenn.edu/hms/139 Constraint-Aware Navigation in Dynamic Environments AbstractPath planning is a fundamental problem in many areas ranging from robotics and artificial intelligence to computer graphics and animation. While there is extensive literature for computing optimal, collision-free paths, there is little work that explores the satisfaction of spatial constraints between objects and agents at the global navigation layer. This paper presents a planning framework that satisfies multiple spatial constraints imposed on the path. The type of constraints specified could include staying behind a building, walking along walls, or avoiding the line of sight of patrolling agents. We introduce a hybrid environment representation that balances computational efficiency and discretization resolution, to provide a minimal, yet sufficient discretization of the search graph for constraintaware navigation. An extended anytime-dynamic planner is used to compute constraint-aware paths, while efficiently repairing solutions to account for dynamic constraints. We demonstrate the benefits of our method on challenging navigation problems in complex environments for dynamic agents using combinations of hard and soft constraints, attracting and repelling constraints, on static obstacles and moving obstacles.

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Robotic covert path planning: A survey

Cited by 21 publications

References 19 publications

On Informative Path Planning for Tracking and Surveillance

On Informative Path Planning for Tracking and Surveillance

Path planning for optimizing survivability of multi-robot formation in adversarial environments

Planning approaches to constraint‐aware navigation in dynamic environments

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