With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS sea...A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.展开更多
The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path pla...The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path planning.The modeling process of wild environment map is designed.Three optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning modes.Furthermore,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional DWA.For the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple elements.The improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.展开更多
In this paper,a novel,dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive contr...In this paper,a novel,dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive control framework.The planned path adds information on the connectivity of the free space to the obstacle avoidance capabilities of the dynamic window approach.This allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds.The framework is applied in a combined simulation/hardware implementation to demonstrate the computational feasibility and the ability to cope with the constraints of a dynamic system.展开更多
A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirement...A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.展开更多
Combining the advantages of partial matched filter（PMF） and fast Fourier transform（FFT）,an improved fast acquisition method for GPS C/A code is proposed.According to PMF-FFT acquisition architecture,the greater th...Combining the advantages of partial matched filter（PMF） and fast Fourier transform（FFT）,an improved fast acquisition method for GPS C/A code is proposed.According to PMF-FFT acquisition architecture,the greater the number of PMF will bring out the more slowly amplitude decreasing of the amplitude-frequency response,the smaller scale of the corresponding PMF,and the larger computation of the FFT.In order to compensate the frequency spectrum attenuation caused by spectrum leakage and fence effect,adding window function to PMF-FFT is presented.Through comparing the influences to the acquisition performance based on rectangular,Hamming,Blackman and Rife-Vincent（Ⅲ） window functions,an improved Rife-Vincent Ⅲ windowing algorithm is recommended for the fast acquisition based on PMF-FFT.展开更多
This paper introduces an Improved Bidirectional Jump Point Search(I-BJPS)algorithm to address the challenges of the traditional Jump Point Search(JPS)in mobile robot path planning.These challenges include excessive no...This paper introduces an Improved Bidirectional Jump Point Search(I-BJPS)algorithm to address the challenges of the traditional Jump Point Search(JPS)in mobile robot path planning.These challenges include excessive node expansions,frequent path inflexion points,slower search times,and a high number of jump points in complex environments with large areas and dense obstacles.Firstly,we improve the heuristic functions in both forward and reverse directions to minimize expansion nodes and search time.We also introduce a node optimization strategy to reduce non-essential nodes so that the path length is optimized.Secondly,we employ a second-order Bezier Curve to smooth turning points,making generated paths more suitable for mobile robot motion requirements.Then,we integrate the Dynamic Window Approach(DWA)to improve path planning safety.Finally,the simulation results demonstrate that the I-BJPS algorithm significantly outperforms both the original unidirectional JPS algorithm and the bidirectional JPS algorithm in terms of search time,the number of path inflexion points,and overall path length,the advantages of the I-BJPS algorithm are particularly pronounced in complex environments.Experimental results from real-world scenarios indicate that the proposed algorithm can efficiently and rapidly generate an optimal path that is safe,collision-free,and well-suited to the robot’s locomotion requirements.展开更多
Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable d...Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.展开更多
As a core technology of Intemet of Things （loT）, Wireless Sensor Network （WSN） has become a research hotspot recently. More and more WSNs are being deployed in highly mobile environments. The fast moving sensor no...As a core technology of Intemet of Things （loT）, Wireless Sensor Network （WSN） has become a research hotspot recently. More and more WSNs are being deployed in highly mobile environments. The fast moving sensor nodes bring significant challenges for the routing decision. In this paper, we propose an efficient logical location method, and designe a mobility estimating metric and derive a novel Green Mobility Estirmtion- based Routing protocol （G-MER） for WSNs. We also set up a full framework to evaluate its per- formance. Simulation results illustrate that G-MER achieves a fairly better perforrmnce in terrm of broadcast times and link failures than AODV. What＇s more, it decreases the mean hops by about 0.25 and reduces energy consumption by about 10% during the whole experiment. All the results show that G-MER can be effectively used in fast- moving and limited resource scenarios.展开更多
The publisher of International Journal of Intelligent Computing and Cybernetics wishes to retract the article by Isiaka,F.,Abdulkarim,S.A.,Mwitondi,K.and Adamu,Z.(2022),“Emotion detection on webpages using biosensors...The publisher of International Journal of Intelligent Computing and Cybernetics wishes to retract the article by Isiaka,F.,Abdulkarim,S.A.,Mwitondi,K.and Adamu,Z.(2022),“Emotion detection on webpages using biosensors integrated to a window-based dynamic control system”,International Journal of Intelligent Computing and Cybernetics,Vol.15 No.2,pp.277-301,https://doi.org/10.1108/IJICC-05-2021-0080.展开更多
文摘With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
基金National Natural Science Foundation of China(No.62241503)Natural Science Foundation of Shanghai,China(No.22ZR1401400)。
文摘A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.
基金Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation(No.USCAST2022-11)。
文摘The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path planning.The modeling process of wild environment map is designed.Three optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning modes.Furthermore,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional DWA.For the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple elements.The improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.
文摘In this paper,a novel,dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with generic path planning techniques through a dual-mode model predictive control framework.The planned path adds information on the connectivity of the free space to the obstacle avoidance capabilities of the dynamic window approach.This allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds.The framework is applied in a combined simulation/hardware implementation to demonstrate the computational feasibility and the ability to cope with the constraints of a dynamic system.
基金supported by the National Nature Science Foundation of China(62203299,62373246,62388101)the Research Fund of State Key Laboratory of Deep-Sea Manned Vehicles(2024SKLDMV04)+1 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2023MS007)the Startup Fund for Young Faculty at SJTU(24X010502929)。
文摘A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A^(*)algorithm with the dynamic window approach(DWA).The traditional A^(*)algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A^(*)path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.
基金Supported by the Ministerial Level Foundation(B222006060)
文摘Combining the advantages of partial matched filter（PMF） and fast Fourier transform（FFT）,an improved fast acquisition method for GPS C/A code is proposed.According to PMF-FFT acquisition architecture,the greater the number of PMF will bring out the more slowly amplitude decreasing of the amplitude-frequency response,the smaller scale of the corresponding PMF,and the larger computation of the FFT.In order to compensate the frequency spectrum attenuation caused by spectrum leakage and fence effect,adding window function to PMF-FFT is presented.Through comparing the influences to the acquisition performance based on rectangular,Hamming,Blackman and Rife-Vincent（Ⅲ） window functions,an improved Rife-Vincent Ⅲ windowing algorithm is recommended for the fast acquisition based on PMF-FFT.
基金supported by the Xinjiang Uygur Autonomous Region Central Guided Local Science and Technology Development Fund Project(No.ZYYD2025QY17).
文摘This paper introduces an Improved Bidirectional Jump Point Search(I-BJPS)algorithm to address the challenges of the traditional Jump Point Search(JPS)in mobile robot path planning.These challenges include excessive node expansions,frequent path inflexion points,slower search times,and a high number of jump points in complex environments with large areas and dense obstacles.Firstly,we improve the heuristic functions in both forward and reverse directions to minimize expansion nodes and search time.We also introduce a node optimization strategy to reduce non-essential nodes so that the path length is optimized.Secondly,we employ a second-order Bezier Curve to smooth turning points,making generated paths more suitable for mobile robot motion requirements.Then,we integrate the Dynamic Window Approach(DWA)to improve path planning safety.Finally,the simulation results demonstrate that the I-BJPS algorithm significantly outperforms both the original unidirectional JPS algorithm and the bidirectional JPS algorithm in terms of search time,the number of path inflexion points,and overall path length,the advantages of the I-BJPS algorithm are particularly pronounced in complex environments.Experimental results from real-world scenarios indicate that the proposed algorithm can efficiently and rapidly generate an optimal path that is safe,collision-free,and well-suited to the robot’s locomotion requirements.
基金supported by the Postgraduate Research&Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(NUAA)(No.xcxjh20251502)。
文摘Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.
基金This paper was partially supported by the National Natural Science Foundation of China under Crants No. 61003283, No. 61001122 Beijing Natural Science Foundation of China under Crants No. 4102064+2 种基金 the Natural Science Foundation of Jiangsu Province under Crant No. BK2011171 the National High-Tech Research and Development Program of China under Crant No. 2011 AA010701 the Fundamental Research Funds for the Cen- tral Universities under Ccants No. 2011RC0507, No. 2012RO3603.
文摘As a core technology of Intemet of Things （loT）, Wireless Sensor Network （WSN） has become a research hotspot recently. More and more WSNs are being deployed in highly mobile environments. The fast moving sensor nodes bring significant challenges for the routing decision. In this paper, we propose an efficient logical location method, and designe a mobility estimating metric and derive a novel Green Mobility Estirmtion- based Routing protocol （G-MER） for WSNs. We also set up a full framework to evaluate its per- formance. Simulation results illustrate that G-MER achieves a fairly better perforrmnce in terrm of broadcast times and link failures than AODV. What＇s more, it decreases the mean hops by about 0.25 and reduces energy consumption by about 10% during the whole experiment. All the results show that G-MER can be effectively used in fast- moving and limited resource scenarios.
文摘The publisher of International Journal of Intelligent Computing and Cybernetics wishes to retract the article by Isiaka,F.,Abdulkarim,S.A.,Mwitondi,K.and Adamu,Z.(2022),“Emotion detection on webpages using biosensors integrated to a window-based dynamic control system”,International Journal of Intelligent Computing and Cybernetics,Vol.15 No.2,pp.277-301,https://doi.org/10.1108/IJICC-05-2021-0080.
