A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

Li, Peng; Yang, Caiyun; Wang, Rui; Wang, Shuo

doi:10.1177/1729881420903207

Cited by 7 publications

(4 citation statements)

References 48 publications

(104 reference statements)

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“…Recent extensions have moved away from the limited grid approximations and worked on continuous work-spaces using cellular decomposition or continuous potential-field methods [37,12,38]. In addition, information-based extensions to these search methods have provided effective strategies for robots exploring unstructured areas [4,39,40]. However, as search-based methods moved towards continuous spaces, coverage guarantees become more difficult to obtain, especially under the presence of distributed information.…”

Section: A Coverage-based and Ergodic Search Methodsmentioning

confidence: 99%

Time Optimal Ergodic Search

Dong,

Berger,

Abraham

2023

Robotics: Science and Systems XIX

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Robots with the ability to balance time against the thoroughness of search have the potential to provide time-critical assistance in applications such as search and rescue. Current advances in ergodic coverage-based search methods have enabled robots to completely explore and search an area in a fixed amount of time. However, optimizing time against the quality of autonomous ergodic search has yet to be demonstrated. In this paper, we investigate solutions to the time-optimal ergodic search problem for fast and adaptive robotic search and exploration. We pose the problem as a minimum time problem with an ergodic inequality constraint whose upper bound regulates and balances the granularity of search against time. Solutions to the problem are presented analytically using Pontryagin's conditions of optimality and demonstrated numerically through a direct transcription optimization approach. We show the efficacy of the approach in generating time-optimal ergodic search trajectories in simulation and with drone experiments in a cluttered environment. Obstacle avoidance is shown to be readily integrated into our formulation, and we perform ablation studies that investigate parameter dependence on optimized time and trajectory sensitivity for search.

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Section: A Coverage-based and Ergodic Search Methodsmentioning

confidence: 99%

Time Optimal Ergodic Search

Dong,

Berger,

Abraham

2023

Robotics: Science and Systems XIX

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“…Strader et al [22] report experiments of active perception in a Mars-analogue environment. Finally, assistive mapping examples for officelike environments can be found in [30], [251], [273]. Aerial applications of active SLAM are significantly less common.…”

Section: G Practical Applicationsmentioning

confidence: 99%

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed

Strader

Carrillo³

et al. 2023

IEEE Trans. Robot.

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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 work, we survey the state-ofthe-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 beliefspace planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

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“…Strader et al [23] report experiments of active perception in a Mars-analogue environment. Finally, assistive mapping examples for officelike environments can be found in [31], [241], [263]. Aerial applications of active SLAM are significantly less common.…”

Section: G Practical Applicationsmentioning

confidence: 99%

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed¹,

Strader²,

Carrillo³

et al. 2022

Preprint

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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 work, 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 classical 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 modern techniques based on deep reinforcement learning, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

show abstract

A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

Cited by 7 publications

References 48 publications

Time Optimal Ergodic Search

Time Optimal Ergodic Search

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

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