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Obstacle avoidance is one of the most important problems in autonomous robots. This paper suggests a collision avoidance system using reinforcement learning. Hand-crafted features are used to approximate Q value. With off-line learning, we develop a general collision avoidance system and use this system to unknown environment. Simulation results show that our mobile robot agent using reinforcement...
Differential Dynamic Programming (DDP) can effectively solve an optimal control problem; however, it cannot deal with temporally changing environments such as an appearance of a moving obstacle. In this paper, we present the segmentation of locally optimal trajectories under an environment with a moving obstacle. The agent finds locally optimal trajectories by sampling Gaussian samples according to...
We present a decentralized cooperative control algorithm for a box pushing problem using two ground robots. The main framework of the algorithm is composed of a sequence of three behaviors, Approach, Align, and Push. We define two rotational and one translational motion primitives to manipulate the box. A point-tracking controller is derived for differential wheeled mobile robots to push the point...
This paper presents an efficient backtracking strategy for frontier-based exploration in Sensor-based Random Tree (SRT). An existing backtracking of Frontier-Based SRT (FB-SRT) exploration is to just move back to the previous position which a robot already passed by. But in some cases, the robot generates a long detour to move to the position that has frontier areas. The proposed algorithm aims to...
Object localization using wireless sensor networks (WSN) often requires data from many sensor nodes and different types of sensors for position estimation. This incurs a heavy communication load, which can cause packet loss, communication delay and much energy consumption, deteriorating the performance of object localization. Here we employ an event-driven Gaussian process in order to learn the position...
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