Multi-robot Field Exploration in Hex-Decomposed Environments for Dubins Vehicles

Kan, Xinyue; Teng, Hanzhe; Karydis, Konstantinos

doi:10.1109/robio49542.2019.8961849

Cited by 8 publications

(20 citation statements)

References 36 publications

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“…However, in HDCP, more area is covered when following the full-circle at the expense of exploration speed. M-HDE from our previous work [29] achieves the second fastest exploration, in which the lack of inner-tangent straight-line transitioning causes longer paths. 10 Hence, in scenarios when the speed of exploring more unknown space is the main concern, HDCP-E can be used to achieve fast exploration.…”

Section: Experiments Results and Discussionmentioning

confidence: 95%

“…Our proposed variants are evaluated in Gazebo simulation and experimentally with a non-holonomic wheeled robot. Their performance is compared against 1) online BA * [20], 2) online STC [19], and 3) M-HDE [29] in terms of total coverage area and average exploration speed.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…The proposed HDCP algorithm aims to cover the entire unknown (yet bounded) environment, whereas its variant, HDCP-E, is used for fast exploration. The proposed method is evaluated in Gazebo simulations in three forest/farm-like environments and in one baseline empty environment, and compared against Spanning Tree Coverage (STC) [19], Boustrophedon motions and the A* search algorithm (BA * ) [20], and Multi-robot Hex Decomposition Exploration (M-HDE) [29] in terms of covered area and exploration speed. We observe that unknown environments containing random obstacles can be fully covered.…”

Section: Introductionmentioning

confidence: 99%

“…3) We offer closed-form solutions for planning smooth paths that robots can follow at constant speed. The major difference between HDCP and our previous work M-HDE [29]-which also applies hexagonal cell decomposition in unknown environments-is the sensor-footprint-based decomposition strategy herein which ensures full coverage within each subregion. Further, M-HDE is developed mainly for online exploration tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Cube coordinates have three axes in the 2D case 2. More details can be found in[29] 3. We drop superscript H in cube coordinates for clarity of presentation.…”

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confidence: 99%

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Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

Kan

Teng

Karydis

2020

Preprint

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Online coverage planning can be useful in applications like field monitoring and search and rescue. Without prior information of the environment, achieving resolutioncomplete coverage considering the non-holonomic mobility constraints in commonly-used vehicles (e.g., wheeled robots) remains a challenge. In this paper, we propose a hierarchical, hexdecomposition-based coverage planning algorithm for unknown, obstacle-cluttered environments. The proposed approach ensures resolution-complete coverage, can be tuned to achieve fast exploration, and plans smooth paths for Dubins vehicles to follow at constant velocity in real-time. Gazebo simulations and hardware experiments with a non-holonomic wheeled robot show that our approach can successfully tradeoff between coverage and exploration speed and can outperform existing online coverage algorithms in terms of total covered area or exploration speed according to how it is tuned.

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Section: Experiments Results and Discussionmentioning

confidence: 95%

Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Cube coordinates have three axes in the 2D case 2. More details can be found in[29] 3. We drop superscript H in cube coordinates for clarity of presentation.…”

mentioning

confidence: 99%

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Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

Kan

Teng

Karydis

2020

Preprint

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Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

Kan

Teng

Karydis

2020

IEEE Robot. Autom. Lett.

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Task Planning on Stochastic Aisle Graphs for Precision Agriculture

Kan

Thayer

Carpin

et al. 2021

IEEE Robot. Autom. Lett.

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This work addresses task planning under uncertainty for precision agriculture applications whereby task costs are uncertain and the gain of completing a task is proportional to resource consumption (such as water consumption in precision irrigation). The goal is to complete all tasks while prioritizing those that are more urgent, and subject to diverse budget thresholds and stochastic costs for tasks. To describe agriculture-related environments that incorporate stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph (SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled by SAG, and tackles the task planning problem by simultaneously determining the optimal tasks to perform and an optimal time to exit (i.e. return to a base station), at runtime. The proposed approach is tested with both simulated data and real-world experimental datasets collected in a commercial vineyard, in both single-and multi-robot scenarios. In all cases, NBA-P outperforms other evaluated methods in terms of return per visited vertex, wasted resources resulting from aborted tasks (i.e. when a budget threshold is exceeded), and total visited vertices.Index Terms-Planning, robotics and automation in agriculture and forestry, scheduling and coordination, task and motion planning. I. INTRODUCTIONA UTONOMOUS agricultural mobile robots become increasingly more capable for persistent missions like monitoring crop health [1] and sampling specimens [2] across extended spatio-temporal scales to enhance efficiency and productivity in precision agriculture [3]. An autonomous robot (or a team of them) needs to perform certain tasks in distinct locations of the environment subject to a specific budget [4] on the actions Manuscript

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Multi-robot Field Exploration in Hex-Decomposed Environments for Dubins Vehicles

Cited by 8 publications

References 36 publications

Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

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