This paper investigates a method to suppress the energy consumption of snake robots using parallel elastic actuators (PEA) during the low speed movement. It has been widely known that the PEA-based robot can greatly reduce the torque if its movement synchronizes with the resonance frequency. However, the PEA installation actually degrades the energy efficiency when the speed changes are dramatic since the PEA disturbs the movement. Therefore, even if the spring was designed so as to increase the efficiency during high speed movement, the spring prevented the movement during low speed movement. To solve this problem, we propose changing the initial winding angle of the serpenoid curve to suppress the energy consumption of the snake robot without changing the spring. To verify how the different initial winding angle changes the energy consumption without changing the springs, simulation and experiment were examined. From the results, although the losses incurred by friction in the gear chain and heating of the circuits inside the servo motors are not small, the different initial winding angle decreased the robot’s energy consumption.
This paper proposes a novel cooperative framework to develop the efficient voltage management system with social acceptance in the distribution network with photovoltaic generations (PV). In the developed framework, the voltage management problem using demand resources of consumers is formulated as the linear programming problem based on the cooperative game theory. The proposed method can efficiently compute an imputation in the non-empty core, i.e., a solution concept of the cooperative game, to realize the reliable voltage management system. Unlike previous related works, this paper explicitly addresses incentives of the consumers to the cooperative voltage management system. Moreover, although the computation of the core requires generally a huge computational time, this paper can efficiently allocate the payoff in the core by employing the linear production game (Owen, 1975). The proposed method is validated by computational experiments using a large scale distribution network model with PVs.
When compiling a program containing syntax errors, the error messages produced by a compiler may be confusing for beginners who are learning a programming language. This leads to difficulties in the debugging process for both teachers and learners of a programming course. We propose a system which provides additional messages to the compiler messages. The auxiliary messages are aimed at helping the beginners by indicating candidates for correcting the source code of the program, which leads to better educational effect.
It is an important task for mobile robots that search the target and learn the route while recognizing the unknown environment topology. Usually, reinforcement learning is used as a learning method to know the route to the target while exploring the environment. However, in an unknown environment, it is difficult to predict the number of state division. Particularly, when the state division is too fine, the amount of calculation increases exponentially. In this paper, we propose a method to dynamically construct the state space of the environment using Growing Neural Gas and simultaneously search and learn the route to the target using Q-Learning. We applied multiple autonomous mobile robots to increase searching efficiency. The experimental result shows the effectiveness of the proposed method that can respond to dynamic environmental change.