基于前馈神经网络的点质量机器人避障研究

K. Chaudhary, Goel Lal, Avinesh Prasad, Vishal Chand, Sushita Sharma, Avinesh Lal
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引用次数: 2

摘要

机器学习目前被认为是包括机器人在内的许多领域研究的重要组成部分。在制造、运输、医疗保健、地雷、采矿、巡逻、救灾等困难、不妥协和危险的空间和部门中,使用机器人执行各种任务是显而易见的。机器人要完成指定的任务,通常需要在不发生碰撞的情况下安全地导航到不同的位置,这也意味着要了解它的工作环境,统称为机器人导航问题。本文研究了用神经网络求解机器人导航问题,特别是包含固定障碍物的路径规划问题。路径规划问题的目标是找到一条到达最终目的地的最优且无碰撞的路线。采用不同的训练算法和网络结构来构建模型,预测点质量机器人的转弯角度,以避免机器人到达目的地的路径上的障碍物。本文将对不同前馈神经网络模型的性能进行比较分析。结果表明,采用10个神经元的前馈神经网络模型和贝叶斯正则化的方法效果最好。该模型已用于两种不同环境中的障碍物避障。轨迹显示机器人已经安全避开了路径上的障碍物并到达了目的地。
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Obstacle Avoidance of a Point-Mass Robot using Feedforward Neural Network
Machine learning is presently acknowledged as a significant ingredient of research in many fields, including robotics. The use of robots to perform assorted tasks is evident in difficult, uncompromising, and hazardous spaces and sectors such as manufacturing, transportation, healthcare, landmines, mining, patrolling, disaster relief etc. For a robot to carry out its assigned task, it normally has to navigate safely without collisions to different locations, which also means understanding its working environment, collectively known as the robot navigation problem. This paper considers finding a solution using neural networks to the robot navigation problem, particularly the path planning problem that includes fixed obstacles. The objective of the path planning problem is to find a route to the final destination that is optimal and also collision-free. Different training algorithms and network structures are used to construct models that can predict a turning angle for the point-mass robot which will be used to avoid obstacles in the robot's path to the destination. This paper will present a comparative analysis of the performance of different feedforward neural network models. The results suggest that the feedforward neural network model with 10 neurons and Bayesian regularization performed the best. The model has been used to avoid obstacles in two different environments. The trajectories show that the robot has safely avoided obstacles in its path and reached the destination.
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