A Model-Based Recurrent Neural Network With Randomness for Efficient Control With Applications

Abstract

Recently, Recurrent Neural Network (RNN) control schemes for redundant manipulators have been extensively studied. These control schemes demonstrate superior computational efficiency, control precision, and control robustness. However, they lack planning completeness. This paper explains why RNN control schemes suffer from the problem. Based on the analysis, this work presents a new random RNN control scheme, which 1) introduces randomness into RNN to address the planning completeness problem, 2) improves control precision with a new optimization target, 3) improves planning efficiency through learning from exploration. Theoretical analyses are used to prove the global stability, the planning completeness, and the computational complexity of the proposed method. Software simulation is provided to demonstrate the improved robustness against noise, the planning completeness and the improved planning efficiency of the proposed method over benchmark RNN control schemes. Real-world experiments are presented to demonstrate the application of the proposed method.

Keywords: Motion Planning; Random Neural Networks; Recurrent Neural Networks; Redundant Manipulator; Robot.

Fig. 1:

The Architecture of the Proposed Random Recurrent Neural Network. The proposed method consists a single layer Recurrent Neural Network(RNN) and a single cell Short Term Memory (STM). The STM learns from exploration and controls the RNN to generate precise, efficient and robust motion planning results for redundant manipulators.

Fig. 2:

Explanation to the Reason that Classical RNN Control Schemes Suffer from the Local Minimum Problem and Lack of Planning Completeness. In the simple 2-dimensional environment, classical RNN control schemes fail to find a valid pathway to move from the pose indicated by the solid line to the pose indicated by the dashed line because the configuration space is concave due to the existence of the obstacle.

Fig. 3:

Tracking Precision Comparison. Different levels of process noise have been injected to verify the robustness of the RNN control schemes.

Fig. 4:

Example Planning Results in Environments with a Plane-shaped Obstacle or a Window-shaped Obstacle. The semitransparentplanes denote the obstacle, the red globe denotes the target and the colored lines indicate the manipulator trajectories.

Fig. 5:

Example RRNN_STM Neuron Activity Changes in Exploration. The neural network learns from exploration. In simpler environments, it tends to perform the heuristic search; in complex environments, it leans to the random exploration.

Fig. 6:

Applying the Proposed Method to Simulate Autonomous Robotic Endoscopic Surgery. The Raven II robot automatically reaches the surgical target under the control of the proposed method. (a): the Raven II surgical robot. (b): the experimental setup and the initial manipulator position. The zoomed-in area shows the manipulator reaches the goal. (c): the surgical robot trajectory.