A Hybrid Path Planning Algorithm for Unmanned Surface Vehicles in Complex Environment With Dynamic Obstacles

Chen, Zheng; Zhang, Youming; Zhang, Yougong; Nie, Yong; Tang, Jianzhong; Zhu, Shiqiang

doi:10.1109/access.2019.2936689

Cited by 69 publications

(31 citation statements)

References 33 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With known static obstacles, the general strategy is to first discretize the environment and turn it into a grid. Then the problem of going from point A to point B is thereby transformed into a graph search problem well suited for seminal algorithms like Dijkstra's, RRT (Rapidly-exploring Random Trees), or the most commonly used A * [32,16]. Faced with complex variables in a maritime environment, the major limiting factor of these algorithms is speed [32].…”

Section: Global Path Planningmentioning

confidence: 99%

See 1 more Smart Citation

A planned approach to high collision risk area

Li¹

2020

View full text Add to dashboard Cite

This thesis examines the transition of a vessel from the open ocean, where collisions are rare, to a high risk and heavy traffic area such as a Traffic Separation Scheme (TSS). Previous autonomy approaches generally view path planning and collision avoidance as two separate functions, i.e. a vessel will follow the planned path until conditions are met for collision avoidance algorithms to take over. Here an intermediate phase is proposed with the goal of adjusting the time of arrival to a high vessel density area so that the risk of collision is reduced. A general algorithm that calculates maximum future traffic density for all choices in the speed domain is proposed and implemented as a MOOS-IvP behavior. This behavior gives the vessel awareness of future collision risks and aids the collision avoidance process. This new approach improves the safety of the vessel by reducing the number of risky encounters that will likely require the vessel to maneuver for safety.

show abstract

Section: Global Path Planningmentioning

confidence: 99%

“…Vehicle use local planer within the circle, with the intersection with the global path as the goal.Figure from[16].…”

mentioning

confidence: 99%

A planned approach to high collision risk area

Li¹

2020

View full text Add to dashboard Cite

show abstract

“…Several studies on obstacle avoidance issues have been proposed in [21][22][23][24][25][26][27][28], and these studies have been validated to some extent through simulations. In [21], the authors presented an energy-efficient routing mechanism based on the clustering method for mobile sinks in WSNs with obstacles.…”

Section: Related Workmentioning

confidence: 99%

“…In [26], the authors focused on the dynamic obstacle avoidance and path planning problem of unmanned surface vehicles and attempted to solve it by constructing an automatic obstacle avoidance method based on the ant colony algorithm and the clustering algorithm. In [27], the authors proposed a hybrid algorithm that effectively combines global and local path planning. Global path planning involves generating a global path for an unmanned surface vehicle to enable it to reach the target point.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sinks

Chang

Jeng

Sheu

et al. 2020

Preprint

View full text Add to dashboard Cite

Wireless sensor networks with mobile sinks enable a mobile device to move into the sensing area for the purpose of collecting the sensing data. Mobile sinks increase the flexibility and convenience of data gathering in such systems. Taking the energy consumption of the mobile sink into account, the moving distance of the mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for mobile sinks in large-scale wireless sensor network systems. According to several greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel and efficient data collection path planning scheme is proposed to reduce the moving distances and to prolong the lifetimes of mobile sinks in wireless sensor networks. Considering the communication range limitations of sensor nodes and the obstacles within sensing areas, we design an inner center path planning algorithm to reduce the moving distance for the mobile sink. A back-routing avoidance method is included to address the moving path backpropagation problem. We account for the obstacles in sensing area. The reference point of obstacle avoidance is employed to address the obstacle problem. The proposed scheme makes an adaptive decision for creating the moving path of the mobile sink. A suitable moving path planning scheme can be achieved, and the moving distance of the mobile sink can be reduced. The proposed scheme is promising in large-scale wireless sensor networks. When the number of sensor nodes in the sensing area is increased by 50, the proposed scheme yields an average moving distance that is 1.1 km shorter than that of the heuristic tour-planning algorithm, where the sensing area is 5 km × 5 km. Simulation results demonstrate that the proposed data collection path planning scheme outperforms the previously developed greedy-based scheme in terms of the moving paths and moving distances of mobile sinks in wireless sensor networks.

show abstract

“…Correspondingly, target or obstacle detection methods by using data captured by at least one of these payloads have been studied in the past decade. Precise detection results can provide basic information for applications such as hazard avoidance and path planning of the USV (Chen et al, 2019;Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Binocular Visual Environment Perception Technology for Unmanned Surface Vehicle

Wang

Peng

Liu

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Binocular vision system is an essential way for target localization in many fields, which has been widely used as payload of unmanned surface vehicles (USV). High resolution cameras, which can provide richer information, are utilized more often on a USV. This brings challenges of computing tremendous data for target detection and localization in real-time. In this paper, we propose an framework to automatically detect and localize target using high resolution binocular cameras for environment perception of USV. Instead of processing the whole image, the feature extraction and matching are executed within the target region of interest determined by a deep convolution network. Then the target can be localized using triangulation principle with calibrated binocular camera parameters. Experiments show that our proposed strategy can achieve both precise detection and high accurate localization results in real-time applications.

show abstract

A Hybrid Path Planning Algorithm for Unmanned Surface Vehicles in Complex Environment With Dynamic Obstacles

Cited by 69 publications

References 33 publications

A planned approach to high collision risk area

A planned approach to high collision risk area

An Efficient Data Collection Path Planning Scheme in Wireless Sensor Networks with Mobile Sinks

Binocular Visual Environment Perception Technology for Unmanned Surface Vehicle

Contact Info

Product

Resources

About