A Novel Vector-Based Dynamic Path Planning Method in Urban Road Network

Cai, Zhi; Cui, Xuerui; Su, Xing; Mi, Qing; Guo, Limin; Ding, Zhiming

doi:10.1109/access.2019.2962392

Cited by 11 publications

(4 citation statements)

References 36 publications

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“…Ant colony optimization technique was incorporated in references [9,27] to design and investigate vehicle pathways. In order to increase vehicle driving economy, Reference [28] suggested a route optimization approach based on traffic ŕow prediction, paired with an effective dynamic enhanced A* route search algorithm. The aforementioned techniques, which are all non-negotiation algorithms, can all help shorten the distance travelled by a single vehicle, but they do not account for the routing effects of other cars when routing simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

Ding,

Yao,

Khezri

2023

Wireless Netw

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The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city's limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only considers the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

Ding,

Yao,

Khezri

2023

Wireless Netw

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“…In [23], the authors proposed an improved A * algorithm. The improved algorithm extends the traditional 8neighborhood search to 24 neighborhoods and uses guidance vectors to optimize the number of neighborhoods, eliminate redundant nodes, improve search efficiency, and optimize smooth paths.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Xiao

Liu

et al. 2021

Journal of Sensors

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Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

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“…The path planning capability of the robot intuitively reflects its operational and risk management capabilities, and its main goal is to allow the target object to find a collision-free and safe path from the starting point to the end point within a defined area [4]. Path planning has been widely used in high technology, daily life, logistics management and other fields, such as: manipulator path planning [5,6], aircraft trajectory planning [7], cruise missile path planning [8], traveling salesman problem [9], path planning based on road networks [10], electronic maps GPS navigation path search and planning [11], and other areas [12,13]. However, most of the current path planning algorithms are for known environments or static models, which are less robust to environmental changes, errors and noise.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

Li Song,

Li Song

2023

Journal of Computers

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<p>With the complexity of the robot operating environment increases, there becoming higher demands on the optimal path planning for robots. Most of the path planning is performed in known environments and static models. However, there are still challenges for robots to perform path planning in complex unknown or dynamic environments, which will suffer from deadlock problems and obstacle avoidance failures. Reinforcement learning (RL) can help fuzzy algorithm to optimize the strategy. However, the difficulty of designing the rewards in RL makes the algorithm require a large number of samples to learn the strategy, resulting in computational complexity. To solve these problems, a new local path planning based on the improved fuzzy and Q(λ)-learning algorithms is proposed, aiming to plan the shortest path and avoid obstacles. For solving the problems of breaking through and avoiding obstacles, a fuzzy controller is designed. The distance of nearest obstacle in front of the mobile robot and the distance between the obstacles in the two breakout directions are regarded as the two inputs for this controller. And the two fuzzy quantities of the mobile robot’s running angle and the safe step length are outputted. In the path planning, the Q(λ)-learning algorithm are used to optimize the weights of the running angle and the safe step, obtaining a more accurate robot position and speeding up path planning efficiency. Furthermore, to solve the overlap problems among the starting point, end point, and obstacles, a safer running environment is designed considering radiuses of these objects. Besides, the mobile robot breakout scheme and sustainable obstacle avoidance scheme are designed to solve the deadlock problem and “large obstacle” avoidance problem, respectively. Simulation results in the sparse and complex operating environment show that our proposed algorithm can plan a relatively optimal and safe path, improving the success rate of path planning.</p> <p> </p>

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A Novel Vector-Based Dynamic Path Planning Method in Urban Road Network

Cited by 11 publications

References 36 publications

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

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