Modeling and Path Planning for Persistent Surveillance by Unmanned Ground Vehicle

Wang, Tong; Huang, Panfeng; Dong, Gangqi

doi:10.1109/tase.2020.3013288

Cited by 25 publications

(6 citation statements)

References 48 publications

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“…90% of the time penalty is allocated to servicing. 4 Defined as the total area coverage for all vehicles.…”

Section: Scenario 1: 2 Normal Vehiclesmentioning

confidence: 99%

“…Unmanned ground vehicles (UGVs) have seen an increase in popularity amongst operations researchers in recent years. Spanning over several applications such as autonomous cleaning [1,2], lawn mowing [3], surveillance [4], tillage [5], and street-sweeping [6], UGVs can be applied seamlessly to many coverage path planning (CPP) problems. Additionally, UGVs can be used individually or in fleets.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Parsons,

Baghyari,

Seo

et al. 2024

Robotics

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In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage.

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“…90% of the time penalty is allocated to servicing. 4 Defined as the total area coverage for all vehicles.…”

Section: Scenario 1: 2 Normal Vehiclesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Parsons,

Baghyari,

Seo

et al. 2024

Robotics

View full text Add to dashboard Cite

show abstract

“…Refinement-based methods are typically employed during the refinement stage of a coarse-to-fine planning process [23,24]. Numerical optimization is a common approach employed in the refinement stage [3,25,26]. However, numerical optimization has two limitations.…”

Section: A Related Workmentioning

confidence: 99%

Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths

Wang

et al. 2023

IEEE Trans. Intell. Veh.

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Path planning is an essential function in an intelligent vehicle, especially when driving in scenarios cluttered by large-scale static obstacles. Traditional path planners often struggle to find a balance among speed, accuracy, and optimality in their solutions. In this paper, we introduce an Adaptive Pure Pursuit (APP) planner, which is designed to be fast and near-optimal for autonomous driving in cluttered environments. The APP planner generates feasible paths through a simulated closed-loop tracking control process of a virtual vehicle. If a derived path encounters obstacles, an adaptive refinement step is taken to locally reduce these collisions. Unlike search-based planners that suffer from the "curse of dimensionality" and optimization-based methods that often run slowly, the APP planner operates extremely fast. The high speed stems from the fact that both the virtual controller simulation and the refinement step involve computations with zero degrees of freedom. The proposed APP planner outperforms the prevalent optimizationbased and search-based path planners, as shown by comparative simulations. Real-world experiments were also conducted to validate the APP planner, and its source codes are provided at https://github.com/libai1943/Adaptive_Pure_Pursuit_Planner.

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“…An incremental sampling-based algorithm is proposed (Lan and Schwager, 2013) to plan a periodic monitoring trajectory for sensing robots in Gaussian Random Fields. Aiming to solve the road-network persistent monitoring problem, (Wang et al, 2020) designed a heuristic path planning algorithm from a decision-making perspective, which enables a UGV to persistently monitor the viewpoints of the road network without traversing them. (Pinto et al, 2020) described the multi-agent persistent monitoring process as a hybrid system, and the Infinitesimal perturbation analysis (IPA) was adopted to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring

Zhao¹,

Zou²,

Li³

2022

Front. Control Eng.

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This paper investigates the multi-agent persistent monitoring problem via a novel distributed submodular receding horizon control approach. In order to approximate global monitoring performance, with the definition of sub-modularity, the original persistent monitoring objective is divided into several local objectives in a receding horizon framework, and the optimal trajectories of each agent are obtained by taking into account the neighborhood information. Specifically, the optimization horizon of each local objective is derived from the local target states and the information received from their neighboring agents. Based on the sub-modularity of each local objective, the distributed greedy algorithm is proposed. As a result, each agent coordinates with neighboring agents asynchronously and optimizes its trajectory independently, which reduces the computational complexity while achieving the global performance as much as possible. The conditions are established to ensure the estimation error converges to a bounded global performance. Finally, simulation results show the effectiveness of the proposed method.

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Modeling and Path Planning for Persistent Surveillance by Unmanned Ground Vehicle

Cited by 25 publications

References 48 publications

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths

A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring

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