The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatl...The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.展开更多
The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We...The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.展开更多
A surgical manipulator has widely been used for laparoscopic surgery. It has been chosen mainly for the use in supporting human operations and in robot systems like the da Vinci surgical system. These manipulator syst...A surgical manipulator has widely been used for laparoscopic surgery. It has been chosen mainly for the use in supporting human operations and in robot systems like the da Vinci surgical system. These manipulator systems are suitable for careful work, but they have a few problems. One of the problems is that the manipulator is not equipped with haptic sensing functions. Therefore, the operator must know the advanced techniques for visually detecting the physical contact state during surgical operations. These haptic sensing functions thus need to be incorporated into a surgical manipulator. We have developed hydraulic-driven forceps with a micro bearing and a bellows tube that can convey haptic sense to the operator. This system can measure the small forces acting on the tips of the forceps using Pascal＇s principle. A model of the system is provided from the relationship between the internal pressure of the bellows tube and the refraction angles of the forceps. It was confirmed using this model that the developed system makes it possible to estimate both the strength and the direction of the external force applied to the forceps by measuring the internal pressure of the bellows tube. An operator using a three-dimensional haptic device was able to feel the force response during an experiment in which they used the forceps to hold a blood vessel. This report describes the most appropriate method for letting the operator feel the force conveyed by using our system.展开更多
基金supported by the Japanese Government,Grants-in-Aid for Scientific Research 2014 to 2016 under Grant No.26330296
文摘The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.
文摘The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.
文摘A surgical manipulator has widely been used for laparoscopic surgery. It has been chosen mainly for the use in supporting human operations and in robot systems like the da Vinci surgical system. These manipulator systems are suitable for careful work, but they have a few problems. One of the problems is that the manipulator is not equipped with haptic sensing functions. Therefore, the operator must know the advanced techniques for visually detecting the physical contact state during surgical operations. These haptic sensing functions thus need to be incorporated into a surgical manipulator. We have developed hydraulic-driven forceps with a micro bearing and a bellows tube that can convey haptic sense to the operator. This system can measure the small forces acting on the tips of the forceps using Pascal＇s principle. A model of the system is provided from the relationship between the internal pressure of the bellows tube and the refraction angles of the forceps. It was confirmed using this model that the developed system makes it possible to estimate both the strength and the direction of the external force applied to the forceps by measuring the internal pressure of the bellows tube. An operator using a three-dimensional haptic device was able to feel the force response during an experiment in which they used the forceps to hold a blood vessel. This report describes the most appropriate method for letting the operator feel the force conveyed by using our system.
