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An Improved ACO Path Planning Algorithm for Navigation in Weighed Lattice Map 认领 引用
1
作者 WANG Bofan MA Ziqing +2 位作者 SONG Zeyuan YAO Haizheng YUAN Quan 《同济大学学报（自然科学版）》 EI CAS CSCD 北大核心 2025年第S1期236-247,共12页
In autonomous navigation and robotics,particularly within intelligent transportation systems,efficient and precise path planning is essential for navigation through complex environments.While traditional path planning... In autonomous navigation and robotics,particularly within intelligent transportation systems,efficient and precise path planning is essential for navigation through complex environments.While traditional path planning algorithms such as ACO show potential,they frequently encounter limitations in directionality and local optima challenges.This paper introduces an enhanced algorithm—ACO-ESD.Through the implementation of a Step Direction Judgement mechanism that considers pheromone concentrations,heuristic functions,and supplementary indices,the ACOESD algorithm significantly improves path search directionality,expedites convergence,and effectively circumvents local optima.Simulation results indicate that the ACO-ESD algorithm surpasses traditional ACO algorithms in path efficiency,accuracy,and convergence rate,offering an effective solution for path planning in complex weighted lattice maps. 展开更多
关键词 path planning algorithm improved ant colony optimization weighted lattice map enhanced step direction mechanism multi-objective function elite ant selection strategy
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RESEARCH ON JUMPING SEQUENCE PLANNING ISSUES OF HOPPING ROBOTS 认领 引用 被引量：1
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作者 LIUZhuang-zhi ZHUJian-ying 《Transactions of Nanjing University of Aeronautics and Astronautics》 2004年第2期116-121,共6页
The wheeled or crawled robots often suffer from big obstacles or ditches, so a hopping robot needs to fit the tough landform in the field environments. In order to jump over obstacles rapidly, a jumping sequence must ... The wheeled or crawled robots often suffer from big obstacles or ditches, so a hopping robot needs to fit the tough landform in the field environments. In order to jump over obstacles rapidly, a jumping sequence must be generated based on the landform information from sensors or user input. The planning method for planar mobile robots is compared with that of hopping robots. Several factors can change the planning result. Adjusting these coefficients, a heuristic searching algorithm for the jumping sequence is developed on a simplified landform. Calculational result indicates that the algorithm can achieve safety and efficient control sequences for a desired goal. 展开更多
关键词 robots path planning algorithm heuristic search
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A novel fuzzy inference method for urban incomplete road weight assignment 认领 引用
3
作者 Longhao Wang Xiaoping Rui 《Geo-Spatial Information Science》 SCIE EI CSCD 2024年第6期2008-2022,共15页
One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information.To facilitate the estimation of the road's weight,Global Position System(GPS)data are comm... One of the keys in time-dependent routing is determining the weight of each road network link based on traffic information.To facilitate the estimation of the road's weight,Global Position System(GPS)data are commonly used in obtaining real-time traffic information.However,the information obtained by taxi-GPS does not cover the entire road network.Aiming at incomplete traffic information on urban roads,this paper proposes a novel fuzzy inference method.It considers the combined effect of road grade,traffic information,and other spatial factors.Taking the third law of geography as the basic premise,that is,the more similar the geographical environment,the more similar the characteristics of the geographical target will be.This method uses a Typical Link Pattern(TLP)model to describe the geographical environment.The TLP represents typical road sections with complete information.Then,it determines the relationship between roads lacking traffic information and the TLPs according to their related factors.After obtaining the TLPs,this method ascertains the weight of road links by calculating their similarities with TLPs based on the theory of fuzzy inference.Aiming at road links at different places,the dividing-conquering strategy and globe algorithm are also introduced to calculate the weight.These two strategies are used to address the excessively fragmented or lengthy links.The experimental results with the case of Newcastle show robustness in that the average Root Mean Square Error(RMSE)is 1.430 mph,and the bias is 0.2%;the overall RMSE is 11.067 mph,and the bias is 0.6%.This article is the first to combine the third law of geography with fuzzy inference,which significantly improves the estimation accuracy of road weights with incomplete information.Empirical application and validation show that the method can accurately predict vehicle speed under incomplete information. 展开更多
关键词 Weight assignment path planning algorithm fuzzy inference road network
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A comprehensive review of obstacle avoidance for autonomous agricultural machinery in multi-operational environment 认领 引用 被引量：1
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作者 Zhijian Chen Jianjun Yin +4 位作者 Sheikh Muhammad Farhan Lu Liu Ding Zhang Maile Zhou Junhui Cheng 《Artificial Intelligence in Agriculture》 SCIE EI CSCD 2026年第1期139-163,共25页
As automation becomes increasingly adopted to mitigate labor shortages and boost productivity,autonomous technologies such as tractors,drones,and robotic devices are being utilized for various tasks that include plowi... As automation becomes increasingly adopted to mitigate labor shortages and boost productivity,autonomous technologies such as tractors,drones,and robotic devices are being utilized for various tasks that include plowing,seeding,irrigation,fertilization,and harvesting.Successfully navigating these changing agricultural landscapes necessitates advanced sensing,control,and navigation systems that can adapt in real time to guarantee effective and safe operations.This review focuses on obstacle avoidance systems in autonomous farming machinery,highlighting multi-functional capabilities within intricate field settings.It analyzes various sensing technologies,LiDAR,visual cameras,radar,ultrasonic sensors,GPS/GNSS,and inertial measurement units(IMU)for their individual and collective contributions to precise obstacle detection in fluctuating field conditions.The review examines the potential of multi-sensor fusion to enhance detection accuracy and reliability,with a particular emphasizing on achieving seamless obstacle recognition and response.It addresses recent advancements in control and navigation systems,particularly focusing on path-planning algorithms and real-time decision-making.It enables autonomous systems to adjust dynamically across multi-functional agricultural environments.The methodologies used for path planning,including adaptive and learning-based strategies,are discussed for their ability to optimize navigation in complicated field conditions.Real-time decision-making frameworks are similarly evaluated for their capacity to provide prompt,data-driven reactions to changing obstacles,which is critical for maintaining operational efficiency.Moreover,this review discusses environmental and topographical challenges like variable terrain,unpredictable weather,complex crop arrangements,and interference from co-located machinery that hinder obstacle detection and necessitate adaptive,resilient system responses.In addition,the paper emphasizes future research opportunities,highlighting the significance of advancements in multi-sensor fusion,deep learning for perception,adaptive path planning,model-free control strategies,artificial intelligence,and energy-efficient designs.Enhancing obstacle avoidance systems enables autonomous agricultural machinery to transform modern farming by increasing efficiency,precision,and sustainability.The review highlights the potential of these technologies to support global efforts for sustainable agriculture and food security,aligning agricultural innovation with the needs of a swiftly growing population. 展开更多
关键词 Autonomous navigation Obstacle avoidance Precision agriculture Path planning algorithms Multi-sensor fusion
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