Autonomous navigation and obstacle avoidance for unmanned surface vehicles

We study problems of intercepting single and multiple invasive intruders on a boundary of a planar region by employing a team of autonomous unmanned surface vehicles. First, the problem of intercepting a single intruder has been studied and then the proposed strategy has been applied to intercepting multiple intruders on the region boundary. Based on the proposed decentralised motion control algorithm and decision making strategy, each autonomous vehicle intercepts any intruder, which tends to leave the region by detecting the most vulnerable point of the boundary. An efficient and simple mathematical rules based control algorithm for navigating the autonomous vehicles on the boundary of the see region is developed. The proposed algorithm is computationally simple and easily implementable in real life intruder interception applications. In this paper, we obtain necessary and sufficient conditions for the existence of a real-time solution to the considered problem of intruder interception. The effectiveness of the proposed method is confirmed by computer simulations with both single and multiple intruders.

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Autonomous system to control a mobile robot

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.2380 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1711-1717

Author(s):

Ayman Abu Baker ◽

Yazeed Yasin Ghadi

Keyword(s):

Mobile Robot ◽

Obstacle Avoidance ◽

Autonomous Navigation ◽

Fuzzy Controller ◽

Computational Time ◽

Ongoing Effort ◽

Unstructured Environment ◽

Adaptive Inference ◽

Real Time Applications ◽

Linguistic Labels

This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.

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Autonomous Navigation of Mobile Robot with Obstacle Avoidance: A Review

Communications in Computer and Information Science - Futuristic Trends in Network and Communication Technologies ◽

10.1007/978-981-16-1483-5_28 ◽

2021 ◽

pp. 305-316

Author(s):

Mahvish Bijli ◽

Neerendra Kumar

Keyword(s):

Mobile Robot ◽

Obstacle Avoidance ◽

Autonomous Navigation

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The development of autonomous navigation and obstacle avoidance for a robotic mower using machine vision technique

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2019.12.517 ◽

2019 ◽

Vol 52 (30) ◽

pp. 173-177 ◽

Cited By ~ 1

Author(s):

Kosuke Inoue ◽

Yutaka Kaizu ◽

Sho Igarashi ◽

Kenji Imou

Keyword(s):

Machine Vision ◽

Obstacle Avoidance ◽

Autonomous Navigation ◽

Vision Technique

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Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT

Sensors ◽

10.3390/s20154082 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4082 ◽

Cited By ~ 2

Author(s):

Zhengjun Qiu ◽

Nan Zhao ◽

Lei Zhou ◽

Mengcen Wang ◽

Liangliang Yang ◽

...

Keyword(s):

Obstacle Avoidance ◽

Processing Speed ◽

Paddy Field ◽

Autonomous Navigation ◽

Vision System ◽

Obstacle Detection ◽

Paddy Fields ◽

Future Research ◽

Moving Obstacles ◽

Agricultural Machines

Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) was used to detect and track typical moving obstacles, and figure out the center point positions of the obstacles in paddy fields. The improved Yolov3 has 23 residual blocks and upsamples only once, and has new loss calculation functions. Results showed that the improved Yolov3 obtained mean intersection over union (mIoU) score of 0.779 and was 27.3% faster in processing speed than standard Yolov3 on a self-created test dataset of moving obstacles (human and water buffalo) in paddy fields. An acceptable performance for detecting and tracking could be obtained in a real paddy field test with an average processing speed of 5–7 frames per second (FPS), which satisfies actual work demands. In future research, the proposed system could support the intelligent agriculture machines more flexible in autonomous navigation.

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Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Sensors ◽

10.3390/s19143096 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3096 ◽

Cited By ~ 5

Author(s):

Junfeng Xin ◽

Shixin Li ◽

Jinlu Sheng ◽

Yongbo Zhang ◽

Ying Cui

Keyword(s):

Particle Swarm Optimization ◽

Real Time ◽

Sensor Fusion ◽

Autonomous Navigation ◽

Particle Swarm ◽

Sensor Data ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization ◽

Unmanned Surface Vehicles ◽

Inertia Weight

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

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