This paper proposes and designs a best path selection algorithm, which can solve the problem of path planning for intelligent driving vehicles in the case of restricted driving, traffic congestions and accidents. We tried to solve the problem under these emergency situations in path planing process for there's no driver in intelligent driving vehicle. We designed a new method of the best path selection with length priority based on the prior knowledge applied reinforcement learning strategy, and improved the search direction setting of A* shortest path algorithm in the program. This best path planing algorithm can effectively help different types of intelligent driving vehicles to select the best path in the traffic network with limited height, width and weight, accidents and traffic jams. Through simulation experiments and practical test, it is proved that the proposed algorithm has good stability, high efficiency and practicability.

/pdf/a-new-algorithm-of-the-best-path-selection-based-on-machine-4dpjdh9pbr.pdf

A New Algorithm of the Best Path Selection Based on Machine Learning

A multilayer path planner for a USV under complex marine environments

As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.

/pdf/global-path-planning-for-unmanned-surface-vehicle-based-on-3ypj5v10us.pdf

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.

https://www.mdpi.com/1424-8220/19/11/2640/pdf

An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle.

Autonomous vessels: State of the art and potential opportunities in logistics

A study on path optimization method of an unmanned surface vehicle under environmental loads using genetic algorithm

In the present study we verified performance of feed-forward control algorithm using short term prediction of ship motion information by taking advantage of developed numerical simulation model of FPSO motion. Up until now, various studies have been conducted about thrust control and allocation for dynamic positioning systems maintaining positions of ships or marine structures in diverse sea environmental conditions. In the existing studies, however, the dynamic positioning systems consist of only feedback control gains using a motion of vessel derived from environmental loads such as current, wind and wave. This study addresses dynamic positioning systems which have feedforward control gain derived from forecasted value of a motion of vessel occurred by current, wind and wave force. In this study, the future motion of vessel is forecasted via Brown`s Exponential Smoothing after calculating the vessel motion via a selected mathematical model, and the control force for maintaining the position and heading angle of a vessel is decided by the feedback controller and the feedforward controller using PID theory and forecasted vessel motion respectively. For the allocation of thrusts, the Lagrange Multiplier Method is exploited. By constructing a simulation code for a dynamic positioning system of FPSO, the performance of feedforward control system which has feedback controller and feedforward controller was assessed. According to the result of this study, in case of using feedforward control system, it shows smaller maximum thrust power than using conventional feedback control system.

http://www.koreascience.or.kr:80/article/JAKO201610254116373.pdf

A Study on the Feedforward Control Algorithm for Dynamic Positioning System Using Ship Motion Prediction

심해에 매장된 자원을 시추하기 위한 선박의 수요가 증가하면 서 DPS가 적용된 Drill ship, Semi-RIG, FPSO 등 선박 및 해양 플랜트 수주가 증가하고 있다. 이에 따라 DP 제어 알고리즘이 적용된 선박의 위치유지(station keeping) 성능 개선 또는 추력기 Power 절감을 위한 제어 알고리즘에 관한 연구가 많이 진행되고 있다. DP 제어 알고리즘은 원하는 위치 및 선수각(heading angle) 을 유지시키기 위하여 Surge, Sway, Yaw 3자유도 수평면 상에 서 제어를 수행한다. DP 제어 알고리즘이 적용된 제어 대상은 환경외란이 작용하는 조건에서 오랜 시간 작업을 수행해야하므 로 다양한 환경외란 조건에서 작업이 가능한 제어 알고리즘이 필 요하다. 국외연구의 경우, 다양한 환경외란 조건을 고려한 DP 제어 알 고리즘에 관한 연구로는 환경외란이 작용하는 선박의 최적 선수 각 제어를 통해 연료 절감이 가능한 Weather Optimal Positioning Control(WOPC) 알고리즘이 연구된 바 있다 (Fossen & Strand, 2001). 환경외란에 지속적으로 노출되어 있는 선박의 위치 및 선수각 유지를 위해 계측된 가속도를 피드백 제어에 이 용하는 Acceleration Feedback(AFB) 제어 알고리즘에 대한 연구 (Lindegaard, 2003)와 극심한 환경외란 조건이 선박에 미치는 영향 및 그 조건에서 작업을 수행할 수 있는 개선된 제어 알고리 퍼지게인 스케쥴링 PID 제어이론을 이용한 동적 위치 유지 제어기법에 관한 연구 전마로 .김희수 .김재학 .김수정 .송순석 .김상현† 인하대학교 조선해양공학과

Ma-Ro Jeon

Papers

A study on path optimization method of an unmanned surface vehicle under environmental loads using genetic algorithm

A Study on the Feedforward Control Algorithm for Dynamic Positioning System Using Ship Motion Prediction

A Study on the Dynamic Positioning Control Algorithm Using Fuzzy Gain Scheduling PID Control Theory