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This paper studies a reconfiguration algorithm for heterogeneous cubic modular robots in environments with obstacles. Tunneling is suitable for the transformation of cubic modular robots in such an environment because a tunneling robot only passes spaces that are occupied by the robot in the start and goal configurations. We have also designed a permutation algorithm that can be executed in the space...
The magnetic measurement of large objects suchas the air cargo fixes magnetic sensor and we move an object andmeasure the amount of change. In this case, in a device movingan object, a problem occurs. It is a problem of the strength ofmaterials and the manufacture cost. Therefore the manufacturerasks the specialized company for the magnetic measurement ofthe large object in many cases. However, in...
This paper presents a full-resolution reconfiguration algorithm for a heterogeneous modular robot composed of sliding cube modules with a limited motion primitive. We assume that a cube module is not allowed to make convex motions around other modules but is allowed to slide across other modules' surfaces. The availability of reconfiguration algorithm for such a module with limited mobility overcomes...
This paper presents a reconfiguration algorithm for a robot composed of sliding cube modules with a limited motion primitive. We assume that a cube module is not allowed to make convex motions around other modules but is allowed to slide across other modules' surfaces. Such a motion primitive limitation complicates the development of the reconfiguration algorithm; however, it simplifies the design...
Multi-agent reinforcement learning (MARL) is one of the most promising methods for solving the problem of multi-robot control. One approach for MARL is cooperative Q-learning (CoQ), which uses learning state space containing states and actions of all agents. Inspite of the mathematical foundation for learning convergence, CoQ often suffers from a state space explosion caused by the increase in the...
In applying reinforcement learning (RL) to multi-robot control, the size of the learning state space easily explodes because the state space has a high dimension. Hierarchical reinforcement learning (HRL) is one of the most practical approaches to solve the problem; however, automatically decomposing a plain MDP state space into sub-spaces has not been studied thoroughly enough to be applied to practical...
Blimp-type unmanned aerial vehicles (BUAVs) do not consume any energy in keeping their longitudinal position in the air, so they are anticipated as a platform that works near the ground but does not touch the ground (for example, for mine search missions). In the path planning of a BUAV flying near the ground, the effects of wind disturbance on the BUAV's motion must be well considered. We have studied...
A blimp-type unmanned aerial vehicle (BUAV) maintains its longitudinal motion using buoyancy provided by the air around it. This means the density of a BUAV equals that of the surrounding air. Because of this, the motion of a BUAV is seriously affected by flow disturbances, whose distribution is usually non-uniform and unknown. In addition, the inertia in the heading motion is very large. There is...
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