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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 proposes a vision-based obstacle avoidance strategy in a dynamic environment for a fixed-wing unmanned aerial vehicle (UAV). In order to apply a nonlinear model predictive control (NMPC) framework to image-based visual servoing (IBVS), a dynamic model from UAV control input to image features is derived. From this dynamics, a visual information-based obstacle avoidance strategy in an unknown...
This paper presents a model-predictive approach for trajectory generation of unmanned ground vehicles (UGVs) combined with a tire model. An optimal tracking problem while avoiding collision with obstacles is formulated in terms of cost minimization under constraints. Information on obstacles is incorporated online in the nonlinear model-predictive framework as they are sensed within a limited sensing...
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