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
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
文摘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.
基金supported by the National Key Research and Development Program of China[grant number 2019YFC1804304]the National Natural Science Foundation of China[grant number 41771478]the Fundamental Research Funds for the Central Universities[grant number 2019B02514].
文摘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.
