Robot path planning by leveraging the graph-encoded Floyd algorithm

Lyu, Desheng; Chen, Ziwei; Cai, Zixing; Piao, Songhao

doi:10.1016/j.future.2021.03.007

Cited by 44 publications

(23 citation statements)

References 5 publications

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“…This paper assumes that the shortest path of Floyd is the total length of each trip chain of EV. The core of the Floyd algorithm is to calculate the global optimal solution by using the local optimal solution [15]. It can be divided into two stages, finding the shortest path length and recording the path of finding length, that is, the optimal route can be found.…”

Section: A Floyd Algorithmmentioning

confidence: 99%

Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

et al. 2022

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The change of electric vehicle (EV) cluster schedulable capacity in each period under multitime scales has strong randomness and volatility. To ensure the trip demand and the charge and discharge cost of users, and increase the reliability of load demand curve prediction, this paper proposes a dynamic charge and discharge optimization strategy that takes the charge and discharge control coefficient in each response period as the control object. According to the trip chain, the proposed method models the trip time-space distribution of EV users, sorts each response period from small to large according to the starting time, comprehensively considers the interests of users and the peak shaving demand of the power grid, and uses Quantum Particle Swarm Optimization (QPSO) to solve the multi-objective optimization of charge and discharge control coefficient for the sorted response period. The charge and discharge control coefficient is modified by introducing virtual charge time and the virtual state of charge. Compared with the traditional method, the proposed method considers the mismatch between the expected parking time of users and the actual parking time of users and can update the load demand curve in real-time due to the dynamic changes in users' trip behavior, which is more practical. To verify the effectiveness of the method proposed in this paper, according to the simulation results of the time-space distribution of electric private car users, the load demand curves under different charge strategies, different optimization weights, and different vehicle-togrid (V2G) responsiveness are simulated and analyzed. The results show that the proposed method can effectively reduce the peak valley difference and variance of the load demand curve under the condition of ensuring the trip demand and economic benefits for EV owners.INDEX TERMS Charge and discharge strategy, electric vehicles, Quantum Particle Swarm Optimization, trip time-space distribution, vehicle-to-grid.

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Section: A Floyd Algorithmmentioning

confidence: 99%

Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

et al. 2022

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“…Lyu et al proposed a path planning algorithm based on graph search. The improved algorithm uses the Floyd algorithm to weight different paths, which greatly reduces the number of path points and effectively improves the smoothness of the path [10]. Turker et al study the problem of UAV radar evasion by regarding a radar as a circular nofly zone and realizing the path planning on a twodimensional plane via the simulated annealing algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

UAV Path Planning Based on Improved A $*$ and DWA Algorithms

Bai

Jiang

Cui

et al. 2021

International Journal of Aerospace Engineering

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This work proposes a path planning algorithm based on A ∗ and DWA to achieve global path optimization while satisfying security and speed requirements for unmanned aerial vehicles (UAV). The algorithm first preprocesses the map for irregular obstacles encountered by a UAV in flight, including grid preprocessing for arc-shaped obstacles and convex preprocessing for concave obstacles. Further, the standard A ∗ algorithm is improved based on UAV’s flight environment information and motion constraints. Further, the DWA algorithm’s limitations regarding local optimization and long planning time are mitigated by adaptively adjusting the evaluation function according to the UAV’s safety threshold, obstacles, and environment information. As a result, the global optimal path evaluation subfunction is constructed. Finally, the key points of the global path are selected as the subtarget points of the local path planning. Under the premise of the optimal path, the UAV real-time path’s efficiency and safety are effectively improved. The experimental results demonstrate that the path planning based on improved A ∗ and DWA algorithms shortens the path length, reduces the planning time, improves the UAV path smoothness, and enhances the safety of UAV path obstacle avoidance.

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“…It provides an optimal path between origin and destination in a known environment and considers more the static environment, such as geographic characteristics, meteorological information, etc. [6,11]. For long voyages, global path planning is particularly important to guide the ship to its destination.…”

Section: Introductionmentioning

confidence: 99%

“…For long voyages, global path planning is particularly important to guide the ship to its destination. The purpose of local path planning is to provide a specific trajectory or series of motions to ensure the moving object has a good obstacle avoidance ability considering the local environment [6,11]. Local paths are more critical in areas where there are many obstacles or ships, such as along the coast or in harbors.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Zhang

Mou

Chen

et al. 2021

JMSE

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In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

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Robot path planning by leveraging the graph-encoded Floyd algorithm

Cited by 44 publications

References 5 publications

Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

UAV Path Planning Based on Improved A $*$ and DWA Algorithms

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

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Robot path planning by leveraging the graph-encoded Floyd algorithm

Cited by 44 publications

References 5 publications

Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

Research on Orderly Charge and Discharge Strategy of EV Based on QPSO Algorithm

UAV Path Planning Based on Improved A∗and DWA Algorithms

Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

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UAV Path Planning Based on Improved A $*$ and DWA Algorithms