Vision-Based Fuzzy Controller for Quadrotor Tracking a Ground Target

Chen, Xu Chao; Cao, Zhi; Yang, Yue; Zhou, Chao

doi:10.4028/www.scientific.net/ast.101.121

Cited by 3 publications

(4 citation statements)

References 12 publications

(14 reference statements)

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“…The path planning and motion control problem of an autonomous robotic system have been the focus of many researchers in the last few years [11]- [16]. With the enormous evolution of computation power of modern processors, computer vision techniques have become a major contributor in robotic applications [17], [18]. In [17], vision algorithms play an important role in detecting obstacles in the robot's environment.…”

Section: Introductionmentioning

confidence: 99%

“…Then, a non-holonomic mobile robot tracks the path generated with the help of the vision system. Moreover, in [18] an autonomous system that calibrates a vision system and a robot is presented. The proposed system consists of multiple calibrated cameras to localize obstacles or objects of interest with reference to the robot's initial position.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Path Quality of Real-Time Path Planning Algorithms for Mobile Robots: A Sequential Linear Paths Approach

et al. 2020

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The traditional trade-off between execution speed and path quality has forced real-time robotic path planning algorithms to sacrifice path quality in order to execute in real-time. Producing a path planning algorithm that targets enhancing both, the path quality and swiftness is a challenging problem. However, this article proposes a novel path planning strategy that aims to break this traditional trade-off, by targeting both, increasing the swiftness, and enhancing the path quality represented by the path length and smoothness. The proposed strategy is based on the observation that most path planning algorithms waste the processing efforts in less critical areas of the map. Therefore, the proposed path planning strategy tends to focus on critical areas such as the areas around obstacles and areas around the goal point, and exhausts the processing power on these critical areas. This is done by neglecting all static obstacles that do not lie between the robot and the destination. For obstacles that intersect with the linear line from the robot to the destination, a basis traditional path planning algorithm such as A*, D* or the Probabilistic RoadMap (PRM) technique is only implemented around the obstacles in order to find a feasible path around each selected obstacle. This procedure would minimize the computational efforts compared to applying the basis algorithm on the whole map. Finally, the path quality is enhanced by finding any linear shortcuts between any two points in the path and fix these shortcuts as the final path from the starting point to the goal point. The proposed path planning strategy was tested on a P3-DX Pioneer mobile robot using a kinematic controller. The experimental results have proven that the path planning strategy was able to show a superior advantage over other path planning techniques in both aspects, computational time (reached up to 97.05% improvement) and path quality (reached up to 16.21% improvement for path length and 98.50% for smoothness).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Path Quality of Real-Time Path Planning Algorithms for Mobile Robots: A Sequential Linear Paths Approach

et al. 2020

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“…In recent years, with the massive improvements in the processing power of modern computers, cameras (vision) became a widely used tool since it is the most human-like sensor available. [11][12][13][14] In ref. [13], a non-holonomic mobile robot tracks a pre-defined path generated with the help of a vision system.…”

Section: Introductionmentioning

confidence: 99%

“…The authors presented in ref. [12] a sliding mode control design to stabilize the mobile robot to a trajectory. A vision-based control system in ref.…”

Section: Introductionmentioning

confidence: 99%

Investigating Reduced Path Planning Strategy for Differential Wheeled Mobile Robot

Fareh¹,

Baziyad²,

Rahman³

et al. 2019

Robotica

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SummaryThis paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

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