Research Initiation: Manipulator Collision Avoidance and Dynamic Computation Using Neural Networks

该项目探讨了神经网络的发展， 在（a）避免碰撞方面进行复杂的计算 在已知的静态工作环境下，以及（B）操纵器 动力学，这是一个映射从关节位置，速度和 加速度到关节扭矩/力。 为了避免碰撞， 碰撞检测被视为模式识别 问题，这可以有效地解决，很容易通过神经网络 网络. 对于计算机械手动力学，一个三层 神经网络正在研究连续- 有价值的投入和产出。 碰撞避免和机械手动力学是非常 计算密集型操作。 实时控制 使用时，这些计算必须在可接受的 时间周期，其通常为几十毫秒或更短。 神经网络使用大规模并行性，这使得这些 真实的计算是可能的。 拟议研究 这是解决这些问题的一个可行途径。

The project explores the development of neural networks for performing complicated computations in (a) collision avoidance under known, static working environments, and (b) manipulator dynamics, which is a mapping from joint positions, velocities and accelerations to joint torques/forces. For collision avoidance, the detection of collisions is treated as a pattern recognition problem, which can be solved efficiently and easily by neural networks. For computing manipulator dynamics, a three-layer neural network is being studied for mapping between continuous- valued input and output. Collision avoidance and manipulator dynamics are very computationally intensive operations. For real-time control usage, these computations must be accomplished in an acceptable time period, which is usually tens of milliseconds or less. Neural networks use massive parallelism, which allows these computations to be possible in real time. The proposed research represents a feasible approach towards these problems.