Modelling, Positioning, and Deep Reinforcement Learning Path Tracking Control of Scaled Robotic Vehicles: Design and Experimental Validation

Mobile robotic systems are becoming increasingly popular. These systems are used in various indoor applications, raging from warehousing and manufacturing to test benches for assessment of advanced control strategies, such as artificial intelligence (AI)-based control solutions, just to name a few. Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF), and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator to expedite the learning phase and increase robustess to the simulation-to-reality gap. The paper also presents the formulation of a vehicle model along with an effective yet simple procedure for identifying tis paramters. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm. Experimental results confirm the ability of the FEKF to improve the estimate of the mobile robot's position. Furthermore, the effectiveness of the DRL path tracking strateguy is experimentally tested along manoeuvres not considered during training, showing also the ability of the AI-based solution to outpeform model-based control strategies and the demonstrator. The comparison with benchmraking controllers is quantitavely evalueted through a set of key performance indicators.

Imitation Learning for Autonomous Trajectory Learning of Robot Arms in Space

This work adds on to the on-going efforts to provide more autonomy to space robots. Here the concept of programming by demonstration or imitation learning is used for trajectory planning of manipulators mounted on small spacecraft. For greater autonomy in future space missions and minimal human intervention through ground control, a robot arm having 7-Degrees of Freedom (DoF) is envisaged for carrying out multiple tasks like debris removal, on-orbit servicing and assembly. Since actual hardware implementation of microgravity environment is extremely expensive, the demonstration data for trajectory learning is generated using a model predictive controller (MPC) in a physics based simulator. The data is then encoded compactly by Probabilistic Movement Primitives (ProMPs). This offline trajectory learning allows faster reproductions and also avoids any computationally expensive optimizations after deployment in a space environment. It is shown that the probabilistic distribution can be used to generate trajectories to previously unseen situations by conditioning the distribution. The motion of the robot (or manipulator) arm induces reaction forces on the spacecraft hub and hence its attitude changes prompting the Attitude Determination and Control System (ADCS) to take large corrective action that drains energy out of the system. By having a robot arm with redundant DoF helps in finding several possible trajectories from the same start to the same target. This allows the ProMP trajectory generator to sample out the trajectory which is obstacle free as well as having minimal attitudinal disturbances thereby reducing the load on ADCS.