scholarly journals An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Yiquan Du ◽  
Xiuguo Zhang ◽  
Zhiying Cao ◽  
Shaobo Wang ◽  
Jiacheng Liang ◽  
...  
Keyword(s):  
Path Planning ◽  
Short Term Memory ◽  
Convergence Speed ◽  
Planning Method ◽  
Learning Ability ◽  
Stability And Convergence ◽  
State Information ◽  
Reward Function ◽  
Discrete Action ◽  
The Impact

Deep Reinforcement Learning (DRL) is widely used in path planning with its powerful neural network fitting ability and learning ability. However, existing DRL-based methods use discrete action space and do not consider the impact of historical state information, resulting in the algorithm not being able to learn the optimal strategy to plan the path, and the planned path has arcs or too many corners, which does not meet the actual sailing requirements of the ship. In this paper, an optimized path planning method for coastal ships based on improved Deep Deterministic Policy Gradient (DDPG) and Douglas–Peucker (DP) algorithm is proposed. Firstly, Long Short-Term Memory (LSTM) is used to improve the network structure of DDPG, which uses the historical state information to approximate the current environmental state information, so that the predicted action is more accurate. On the other hand, the traditional reward function of DDPG may lead to low learning efficiency and convergence speed of the model. Hence, this paper improves the reward principle of traditional DDPG through the mainline reward function and auxiliary reward function, which not only helps to plan a better path for ship but also improves the convergence speed of the model. Secondly, aiming at the problem that too many turning points exist in the above-planned path which may increase the navigation risk, an improved DP algorithm is proposed to further optimize the planned path to make the final path more safe and economical. Finally, simulation experiments are carried out to verify the proposed method from the aspects of plan planning effect and convergence trend. Results show that the proposed method can plan safe and economic navigation paths and has good stability and convergence.

scholarly journals Path Planning of Coastal Ships Based on Optimized DQN Reward Function

2021 ◽  
Vol 9 (2) ◽  
pp. 210
Author(s):  
Siyu Guo ◽  
Xiuguo Zhang ◽  
Yiquan Du ◽  
Yisong Zheng ◽  
Zhiying Cao
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Convergence Speed ◽  
Stability And Convergence ◽  
Experimental Comparison ◽  
Target Point ◽  
A Algorithm ◽  
Reward Function ◽  
Space Action ◽  
Status Information

Path planning is a key issue in the field of coastal ships, and it is also the core foundation of ship intelligent development. In order to better realize the ship path planning in the process of navigation, this paper proposes a coastal ship path planning model based on the optimized deep Q network (DQN) algorithm. The model is mainly composed of environment status information and the DQN algorithm. The environment status information provides training space for the DQN algorithm and is quantified according to the actual navigation environment and international rules for collision avoidance at sea. The DQN algorithm mainly includes four components which are ship state space, action space, action exploration strategy and reward function. The traditional reward function of DQN may lead to the low learning efficiency and convergence speed of the model. This paper optimizes the traditional reward function from three aspects: (a) the potential energy reward of the target point to the ship is set; (b) the reward area is added near the target point; and (c) the danger area is added near the obstacle. Through the above optimized method, the ship can avoid obstacles to reach the target point faster, and the convergence speed of the model is accelerated. The traditional DQN algorithm, A* algorithm, BUG2 algorithm and artificial potential field (APF) algorithm are selected for experimental comparison, and the experimental data are analyzed from the path length, planning time, number of path corners. The experimental results show that the optimized DQN algorithm has better stability and convergence, and greatly reduces the calculation time. It can plan the optimal path in line with the actual navigation rules, and improve the safety, economy and autonomous decision-making ability of ship navigation.

scholarly journals Research on path planning of robotic ultrasonic surface strengthening for turbine blade based on dynamic response of ultrasonic surface strengthening

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989696
Author(s):  
Shanxiang Fang ◽  
Qinjian Zhang ◽  
Weidong Cheng ◽  
Jiwu Wang ◽  
Chang Liu ◽  
...  
Keyword(s):  
Path Planning ◽  
Dynamic Response ◽  
Turbine Blade ◽  
Kinematic Hardening ◽  
Turbine Blades ◽  
Planning Method ◽  
Response Model ◽  
Surface Strengthening ◽  
Processing Accuracy ◽  
The Impact

In order to realize the automatic strengthening for turbine blades, a path planning method for robotic ultrasonic surface strengthening is proposed. A constitutive model of nonlinear isotropic strengthening–kinematic hardening is analyzed to establish the dynamic response model of ultrasonic surface strengthening on the turbine blade. According to the dynamic response model, the impact depth of the ultrasonic working head was obtained. Then, a path planning method of robotic ultrasonic surface strengthening for turbine blades is proposed on the basis of impact depth of working head, and it can improve both the uniformity of path distribution and contour accuracy. It not only ensures the processing accuracy but also meets the uniformity requirement of coverage. This path planning method provides a new surface strengthening technology for turbine blades.

scholarly journals Research on the Energy-Saving Strategy of Path Planning for Electric Vehicles Considering Traffic Information

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3601
Author(s):  
Zhu ◽  
Lin ◽  
Liu ◽  
He
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Path Planning ◽  
Energy Saving ◽  
Travel Time ◽  
Electric Vehicles ◽  
Lower Energy ◽  
Planning Method ◽  
Traffic Information ◽  
The Impact

Battery-powered electric vehicles (EVs) have a limited on-board energy storage and present the problem of driving mileage anxiety. Moreover, battery energy storage density cannot be effectively improved in a short time, which is a technical bottleneck of EVs. By considering the impact of traffic information on energy consumption forecasting, an energy-saving path planning method for EVs that takes traffic information into account is proposed. The modeling process of the EV model and the construction process of the traffic simulation model are expounded. In addition, the long-term, short-term memory neural network (LSTM) model is selected to predict the energy consumption of EVs, and the sequence to sequence technology is used in the model to integrate the driving condition data of EVs with traffic information. In order to apply the predicted energy consumption to travel guidance, a road planning method with the optimal coupling of energy consumption and distance is proposed. The experimental results show that the energy-based economic path uses 9.9% lower energy consumption and 40.2% shorter travel time than the distance-based path, and a 1.5% lower energy consumption and 18.6% longer travel time than the time-based path.

scholarly journals Three-Dimensional Path Planning Method for Autonomous Underwater Vehicle Based on Modified Firefly Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chang Liu ◽  
Yuxin Zhao ◽  
Feng Gao ◽  
Liqiang Liu
Keyword(s):  
Path Planning ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Convergence Speed ◽  
Planning Method ◽  
Operating Process ◽  
Modified Firefly Algorithm

Path planning is a classic optimization problem which can be solved by many optimization algorithms. The complexity of three-dimensional (3D) path planning for autonomous underwater vehicles (AUVs) requires the optimization algorithm to have a quick convergence speed. This work provides a new 3D path planning method for AUV using a modified firefly algorithm. In order to solve the problem of slow convergence of the basic firefly algorithm, an improved method was proposed. In the modified firefly algorithm, the parameters of the algorithm and the random movement steps can be adjusted according to the operating process. At the same time, an autonomous flight strategy is introduced to avoid instances of invalid flight. An excluding operator was used to improve the effect of obstacle avoidance, and a contracting operator was used to enhance the convergence speed and the smoothness of the path. The performance of the modified firefly algorithm and the effectiveness of the 3D path planning method were proved through a varied set of experiments.

A PROPOSED EVALUATION PLAN TO ASSESS THE IMPACT OF A YOUTH DEVELOPMENT PROGRAMME FACILITATED BY THE STATE INFORMATION TECHNOLOGY AGENCY (SITA) IN RURAL SOUTH AFRICA

2017 ◽  
Vol 14 (1) ◽  
pp. 174-181
Author(s):  
Maura Mbunyuza-deHeer Menlah
Keyword(s):  
Information Technology ◽  
Youth Development ◽  
Rural Areas ◽  
Secondary Data ◽  
The State ◽  
State Information ◽  
Evaluation Plan ◽  
Lessons Learnt ◽  
Slow Development ◽  
The Impact

This article reports on a proposed evaluation plan that has been developed to assess the work done by the State Information Technology Agency (SITA). The SITA programme was implemented in response to the South African government’s call to improve the lives of the populations in some rural areas through technology. The programme was meant to address slow development in  rural  areas  that  lack  technological  innovations  and  advances.  In  the proposed evaluation plan a review is made of secondary data, deciding how strategic priorities are to be determined, as well as analysis of the rural context environment. The researcher gives an account of how the evaluation strategies are to be piloted and rolled out thereafter. Lessons learnt are recorded and reported upon. A proposed evaluation plan will be developed, based on the lessons learnt in line with the objectives of the project.

Fluoxetine Protects against Dendritic Spine Loss in Middle-aged APPswe/PSEN1dE9 Double Transgenic Alzheimer’s Disease Mice

2020 ◽  
Vol 17 (1) ◽  
pp. 93-103 ◽  
Author(s):  
Jing Ma ◽  
Yuan Gao ◽  
Wei Tang ◽  
Wei Huang ◽  
Yong Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Early Stage ◽  
Learning Ability ◽  
Middle Aged ◽  
Protein Levels ◽  
Spine Pathology ◽  
The Impact

Background: Studies have suggested that cognitive impairment in Alzheimer’s disease (AD) is associated with dendritic spine loss, especially in the hippocampus. Fluoxetine (FLX) has been shown to improve cognition in the early stage of AD and to be associated with diminishing synapse degeneration in the hippocampus. However, little is known about whether FLX affects the pathogenesis of AD in the middle-tolate stage and whether its effects are correlated with the amelioration of hippocampal dendritic dysfunction. Previously, it has been observed that FLX improves the spatial learning ability of middleaged APP/PS1 mice. Objective: In the present study, we further characterized the impact of FLX on dendritic spines in the hippocampus of middle-aged APP/PS1 mice. Results: It has been found that the numbers of dendritic spines in dentate gyrus (DG), CA1 and CA2/3 of hippocampus were significantly increased by FLX. Meanwhile, FLX effectively attenuated hyperphosphorylation of tau at Ser396 and elevated protein levels of postsynaptic density 95 (PSD-95) and synapsin-1 (SYN-1) in the hippocampus. Conclusion: These results indicated that the enhanced learning ability observed in FLX-treated middle-aged APP/PS1 mice might be associated with remarkable mitigation of hippocampal dendritic spine pathology by FLX and suggested that FLX might be explored as a new strategy for therapy of AD in the middle-to-late stage.

scholarly journals An Autonomous Path Planning Method for Unmanned Aerial Vehicle Based on a Tangent Intersection and Target Guidance Strategy

2020 ◽  
pp. 1-13
Author(s):  
Huan Liu ◽  
Xiamiao Li ◽  
Mingfeng Fan ◽  
Guohua Wu ◽  
Witold Pedrycz ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Planning Method ◽  
Aerial Vehicle
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