Obstacle avoidance using PSO

We briefly surveyed the existing obstacle avoidance algorithms; then a new obstacle avoidance learning framework based on learning from demonstration (LfD) is proposed. The main idea is to imitate the obstacle avoidance mechanism of human beings, in which humans learn to make a decision based on the sensor information obtained by interacting with environment. Firstly, we endow robots with obstacle avoidance experience by teaching them to avoid obstacles in different situations. In this process, a lot of data are collected as a training set; then, to encode the training set data, which is equivalent to extracting the constraints of the task, Gaussian mixture model (GMM) is used. Secondly, a smooth obstacle-free path is generated by Gaussian mixture regression (GMR). Thirdly, a metric of imitation performance is constructed to derive a proper control policy. The proposed framework shows excellent generalization performance, which means that the robots can fulfill obstacle avoidance task efficiently in a dynamic environment. More importantly, the framework allows learning a wide variety of skills, such as grasp and manipulation work, which makes it possible to build a robot with versatile functions. Finally, simulation experiments are conducted on a Turtlebot robot to verify the validity of our algorithms.

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“…. , } , (8) where indicates the trajectory number and indicates time in a trajectory. We can rewrite the above equation for velocities:̇r…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The emergence of PSO derived from the simulation of birds migrating behavior. Applications of particle swarm optimization to solve the path planning problem include [8][9][10]. GA, derived from the theory of evolution and genetics, is a kind of global probabilistic optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%