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

该项目探索了神经网络的发展，用于在(a)已知静态工作环境下进行复杂计算的避碰，以及(b)机械臂动力学，这是从关节位置，速度和加速度到关节扭矩/力的映射。在避免碰撞方面，碰撞检测被视为一个模式识别问题，可以通过神经网络高效、方便地解决。为了计算机械臂动力学，研究了连续输入与输出之间映射的三层神经网络。避碰和机械臂动力学是计算量非常大的操作。对于实时控制的使用，这些计算必须在一个可接受的时间段内完成，通常是几十毫秒或更短。神经网络使用大量并行性，这使得这些计算可以实时进行。本研究为解决这些问题提供了一条可行的途径。

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.