Velocity planning for a mobile robot to track a moving target — a potential field approach

Huang, L.

doi:10.1016/j.robot.2008.02.005

Cited by 129 publications

(63 citation statements)

References 7 publications

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“…Moreover, the new proposed approach is compared to the modified potential field method (Luh and Liu 2008;Huang 2009) for performance evaluation. One drawback of the potential field based method is the LMP, where the robot was trapped in a position waiting for an obstacle or the target to change their positions which does not occur in static environments.…”

Section: Results and Comparisonsmentioning

confidence: 99%

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

2011

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A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.

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Section: Results and Comparisonsmentioning

confidence: 99%

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

2011

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“…It is important in surveillance, and it has received increasing attentions more recently [1], [4]- [8].One task for the intelligent control of a mobile robot in this study is moving target tracking or capturing and obstacles avoidance. It is assumed that the target moves along a trajectory that is either well-defined and known a priori or unknown.…”

Section: Related Workmentioning

confidence: 99%

“…In [1], a potential field method was developed for velocity planning of a mobile robot to track a moving target in the presence of moving obstacles. In [4], using velocity vectors of the robot relative to each obstacle, an online navigation method based on calculating the best feasible direction close to an optimal direction to the target is proposed for pursuing a moving target amidst dynamic and static obstacles.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Control System for Mobile Robot Navigation Tasks in Surveillance

Lin

et al. 2014

Robot Intelligence Technology and Applications 2

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Abstract. In recent years, the autonomous mobile robot has found diverse applications such as home/health care system, surveillance system in civil and military applications and exhibition robot. For surveillance tasks such as moving target pursuit or following and patrol in a region using mobile robot, this paper presents a fuzzy Q-learning, as an intelligent control for cost-based navigation, for autonomous learning of suitable behaviors without the supervision or external human command. The Q-learning is used to select the appropriate rule of interval type-2 fuzzy rule base. The initial testing of the intelligent control is demonstrated by simulation as well as experiment of a simple wall-following based patrolling task of autonomous mobile robot.

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“…Virtual potential field (VPF) methods can also be applied to problems mentioned above [5], [6]. These methods are mostly applied in single vehicle problems.…”

Section: Introductionmentioning

confidence: 99%

Formation control of a large group of UAVs with safe path planning and obstacle avoidance

Regula¹,

Lantos

2014

2014 European Control Conference (ECC)

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Abstract-In this article we propose a hierarchical control structure for multi-agent systems. The main objective is to perform formation change manoeuvres, with guaranteed safe distance between each two vehicles throughout the whole mission, even if motion is restricted to a limited space and static obstacles are present. The key components that ensure safety are a robust control algorithm that is capable of stabilising the group of vehicles in a desired formation and a higher level path generation method that provides all the vehicles with safe paths, based on graph theoretic considerations. The method can efficiently handle a large group of any type of vehicles. As an illustration, the results are applied to a group of 18 quadrotor UAVs, where 2 of the UAVs cannot receive information from the others.

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Velocity planning for a mobile robot to track a moving target — a potential field approach

Cited by 129 publications

References 7 publications

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

An Intelligent Control System for Mobile Robot Navigation Tasks in Surveillance

Formation control of a large group of UAVs with safe path planning and obstacle avoidance

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