RandomBug: Novel Path Planning Algorithm in Unknown Environment

Xu, Qi-lei

doi:10.2174/1874129001408010252

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…According to the fuzzy membership function, it is determined to follow the left or right border of the obstacle to improve efficiency. Xu [23] proposed the RandomBug algorithm, which is highly correlated with rapidly-exploring random trees. When detecting obstacles, by generating random points at the detection zone of the sensor, it selects the best point and generates a motion vector towards the point, by which to shorten the path length.…”

Section: Introductionmentioning

confidence: 99%

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

Wang,

Yin,

Jing

2023

JMSE

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Due to the complicated and changing circumstances of the sea environment, path planning technology is essential for unmanned surface vehicles (USVs) to fulfill search tasks. In most cases, the location of the underwater target is unknown, so it is necessary to completely cover the search area. In this paper, the global static path is planned using a parallel line scan search. When encountering unknown obstacles, the improved Bug algorithm is used for local dynamic path planning according to the sensor detection information. This paper first sets up the safe expansion area to ensure the safety of the USV during the obstacle avoidance process and optimizes the movement direction considering the operation and behavior characteristics of the USV. To meet the requirement of USV steering, the Bezier curve is used to smooth the path points, which greatly improves the smoothness of the path. In this paper, the multi-mode switching strategy of the Bug algorithm based on obstacle boundary width obtained by the sensor is proposed, which ensures no area omissions and meets the requirement of search area coverage during the process of bypassing obstacles. The simulation results show that the improved Bug algorithm can maintain a safe distance along the obstacle boundary to bypass the obstacle. Moreover, the improved Bug algorithm effectively improves the path oscillation phenomenon of traditional Bug and shortens the path length and operating time. Finally, through the global search path planning simulation and comparison experiments, the effectiveness of the proposed method is verified.

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Section: Introductionmentioning

confidence: 99%

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

Wang,

Yin,

Jing

2023

JMSE

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“…Te Bug algorithm is a kind of obstacle avoidance algorithm for an agent without knowing the global information of a map and obstacle shape, such as the Bug2 algorithm [27,28], the IBug algorithm [29], the FuzzyBug algorithm [30], and the RandomBug algorithm [31,32]. In this kind of algorithm, the agent frstly moves along the boundary of obstacles when encountering some, then leaves these obstacles based on certain judgment criteria, and then continues to move towards the target in a straight line.…”

Section: Introductionmentioning

confidence: 99%

Obstacle Boundary Point and Expected Velocity-Based Flocking of Multiagents with Obstacle Avoidance

Sun

et al. 2023

International Journal of Intelligent Systems

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Obstacle avoidance is a key technology of multiagents flocking control. However, most existing research studies assume that the agent can obtain global information about obstacles and have specific constraints on the shape and boundary of obstacles, which easily limit their practical applications. To relax these constraints, we assume that the agent can only perceive the position of obstacle boundary points within its sensing radius and propose an obstacle boundary point and an expected velocity-based flocking algorithm of multiagents with obstacle avoidance. In this algorithm, the attraction/repulsion potential is designed to avoid collisions between agents and between the agent and obstacle boundary points, the expected velocity is designed to tow the agent to move along the boundary of obstacles, and the virtual leader is designed to lead all agents to realize the group objective. Finally, the sufficient conditions that the agent does not collide are demonstrated, and the performance of the proposed algorithm is further verified through simulations.

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A comparative study of bug algorithms for robot navigation

McGuire

Croon

Tuyls

2019

Robotics and Autonomous Systems

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This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these methods seem to provide an efficient navigation paradigm, ideal for implementations on tiny robots with limited resources. Closer inspection, however, shows that many of these Bug Algorithms assume perfect global position estimate of the robot which in GPS-denied environments implies considerable expenses of computation and memory -relying on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry (VO) methods. We compare a selection of Bug Algorithms in a simulated robot and environment where they endure different types noise and failure-cases of their on-board sensors. From the simulation results, we conclude that the implemented Bug Algorithms' performances are sensitive to many types of sensor-noise, which was most noticeable for odometry-drift. This raises the question if Bug Algorithms are suitable for real-world, on-board, robotic navigation as is. Variations that use multiple sensors to keep track of their progress towards the goal, were more adept in completing their task in the presence of sensor-failures. This shows that Bug Algorithms must spread their risk, by relying on the readings of multiple sensors, to be suitable for real-world deployment.

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RandomBug: Novel Path Planning Algorithm in Unknown Environment

Cited by 5 publications

References 12 publications

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

Obstacle Boundary Point and Expected Velocity-Based Flocking of Multiagents with Obstacle Avoidance

A comparative study of bug algorithms for robot navigation

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