A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction

Kusnur, Tushar; Mukherjee, Shohin; Saxena, Dhruv Mauria; Fukami, Tomoya; Koyama, Takayuki; Salzman, Oren; Likhachev, Maxim

doi:10.1007/978-981-15-9460-1_32

Cited by 15 publications

(16 citation statements)

References 23 publications

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“…We contextualize and evaluate our approaches within the persistent-coverage framework established in previous work [8]. This is a centralized framework that continuously computes goal locations to which UAVs should fly and kinodynamically feasible, globally deconflicting plans for them to do so, in a prioritized planning setting [33].…”

Section: A Persistent Coverage Frameworkmentioning

confidence: 99%

“…This is a centralized framework that continuously computes goal locations to which UAVs should fly and kinodynamically feasible, globally deconflicting plans for them to do so, in a prioritized planning setting [33]. While it is a multi-UAV system, we plan for UAVs independently-plans between UAVs are not explictly coordinated in [8] and is out of the scope of this paper. Our specific contribution lies in planning robot and sensor trajectories for a single UAV navigating to a goal.…”

Section: A Persistent Coverage Frameworkmentioning

confidence: 99%

“…Our specific contribution lies in planning robot and sensor trajectories for a single UAV navigating to a goal. The framework in [8] assumes a circular sensor footprint directly underneath the UAV. In this paper, we extend the system to incorporate a rectangular footprint offset from the UAV-consequently, different sensor headings correspond to the UAV observing different areas of the environment around it.…”

Section: A Persistent Coverage Frameworkmentioning

confidence: 99%

“…The cost of the primitive is equal to the time taken for the UAV to execute it. More details about the motion primitives can be found in [8].…”

Section: Cost Functionmentioning

confidence: 99%

“…In Sec. VI, we evaluate our approaches and show their benefits in the context of a previously established planning framework for persistent coverage with multiple UAVs [8], where individual UAVs are tasked with generating collisionfree trajectories that maximize information gain while navigating to an assigned goal. Note that our contribution lies in planning robot and sensor trajectories for a single UAV navigating to a goal (we do not attempt to solve the problem of coordinating UAV plans for coverage).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks

Kusnur

Saxena

Likhachev

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Path planning for robotic coverage is the task of determining a collision-free robot trajectory that observes all points of interest in an environment. Robots employed for such tasks are often capable of exercising active control over onboard observational sensors during navigation. In this paper, we tackle the problem of planning robot and sensor trajectories that maximize information gain in such tasks where the robot needs to cover points of interest with its sensor footprint. Searchbased planners in general guarantee completeness and provable bounds on suboptimality with respect to an underlying graph discretization. However, searching for kinodynamically feasible paths in the joint space of robot and sensor state variables with standard search is computationally expensive. We propose two alternative search-based approaches to this problem. The first solves for robot and sensor trajectories independently in decoupled state spaces while maintaining a history of sensor headings during the search. The second is a two-step approach that first quickly computes a solution in decoupled state spaces and then refines it by searching its local neighborhood in the joint space for a better solution. We evaluate our approaches in simulation with a kinodynamically constrained unmanned aerial vehicle performing coverage over a 2D environment and show their benefits.

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Section: A Persistent Coverage Frameworkmentioning

confidence: 99%

Section: A Persistent Coverage Frameworkmentioning

confidence: 99%

Section: A Persistent Coverage Frameworkmentioning

confidence: 99%

“…The cost of the primitive is equal to the time taken for the UAV to execute it. More details about the motion primitives can be found in [8].…”

Section: Cost Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks

Kusnur

Saxena

Likhachev

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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A Framework for Multi-UAV Persistent Search and Retrieval with Stochastic Target Appearance in a Continuous Space

Day

Salmon

2021

J Intell Robot Syst

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Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning

Wang

Sahin

Bhattacharya

2023

J Intell Robot Syst

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We consider the problem of multi-robot path planning in a complex, cluttered environment with the aim of reducing overall congestion in the environment, while avoiding any inter-robot communication or coordination. Such limitations may exist due to lack of communication or due to privacy restrictions (for example, autonomous vehicles may not want to share their locations or intents with other vehicles or even to a central server). The key insight that allows us to solve this problem is to stochastically distribute the robots across different routes in the environment by assigning them paths in different topologically distinct classes, so as to lower congestion and the overall travel time for all robots in the environment. We outline the computation of topologically distinct paths in a spatio-temporal configuration space and propose methods for the stochastic assignment of paths to the robots. A fast replanning algorithm and a potential field based controller allow robots to avoid collision with nearby agents while following the assigned path. Our simulation and experiment results show a significant advantage over shortest path following under such a coordination-free setup.

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A Planning Framework for Persistent, Multi-UAV Coverage with Global Deconfliction

Cited by 15 publications

References 23 publications

Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks

Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks

A Framework for Multi-UAV Persistent Search and Retrieval with Stochastic Target Appearance in a Continuous Space

Coordination-free Multi-robot Path Planning for Congestion Reduction Using Topological Reasoning

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