According to the current context of China's new urbanization and urban and rural transformation,this paper defines incremental planning,stock-based planning,and reduction planning.It further discusses the socio-ec...According to the current context of China's new urbanization and urban and rural transformation,this paper defines incremental planning,stock-based planning,and reduction planning.It further discusses the socio-economic foundation of incremental planning,the transformation of incremental planning to stock-based planning,and the emergence of reduction planning,as well as the characteristics of these three types of urban planning.Based on that,it finds that incremental planning is determined by China's unique urban growth pattern,and that the change of the urban growth mode leads to a transformation of urban planning.In addition,reduction planning can effectively cope with urban decline.After over 30 years of rapid economic development,more and more cities in China are approaching the bottleneck of growth.Therefore,the transformation of urban planning is unavoidable and will definitely become an important topic in planning circles.展开更多
Path planning of a mobile robot in the presence of multiple moving obstacles is found to be a complicated problem.A planning algorithm capable of negotiating both static and moving obstacles in an unpredictable(on-lin...Path planning of a mobile robot in the presence of multiple moving obstacles is found to be a complicated problem.A planning algorithm capable of negotiating both static and moving obstacles in an unpredictable(on-line)environment is proposed.The proposed incremental algorithm plans the path by considering the quadrants in which the current positions of obstacles as well as target are situated.Also,the governing equations for the shortest path are derived.The proposed mathematical model describes the motion(satisfying constraints of the mobile robot)along a collision-free path.Further,the algorithm is applicable to dynamic environments with fixed or moving targets.Simulation results show the effectiveness of the proposed algorithm.Comparison of results with the improved artificial potential field(iAPF)algorithm shows that the proposed algorithm yields shorter path length with less computation time.展开更多
文摘According to the current context of China's new urbanization and urban and rural transformation,this paper defines incremental planning,stock-based planning,and reduction planning.It further discusses the socio-economic foundation of incremental planning,the transformation of incremental planning to stock-based planning,and the emergence of reduction planning,as well as the characteristics of these three types of urban planning.Based on that,it finds that incremental planning is determined by China's unique urban growth pattern,and that the change of the urban growth mode leads to a transformation of urban planning.In addition,reduction planning can effectively cope with urban decline.After over 30 years of rapid economic development,more and more cities in China are approaching the bottleneck of growth.Therefore,the transformation of urban planning is unavoidable and will definitely become an important topic in planning circles.
文摘Path planning of a mobile robot in the presence of multiple moving obstacles is found to be a complicated problem.A planning algorithm capable of negotiating both static and moving obstacles in an unpredictable(on-line)environment is proposed.The proposed incremental algorithm plans the path by considering the quadrants in which the current positions of obstacles as well as target are situated.Also,the governing equations for the shortest path are derived.The proposed mathematical model describes the motion(satisfying constraints of the mobile robot)along a collision-free path.Further,the algorithm is applicable to dynamic environments with fixed or moving targets.Simulation results show the effectiveness of the proposed algorithm.Comparison of results with the improved artificial potential field(iAPF)algorithm shows that the proposed algorithm yields shorter path length with less computation time.
