Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control.

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments.

Semantic-Structure-Aware Multi-Level Information Fusion for Robust Global Orientation Optimization of Autonomous Mobile Robots.

Multi-camera-based simultaneous localization and mapping (SLAM) has been widely applied in various mobile robots under uncertain or unknown environments to accomplish tasks autonomously. However, the conventional purely data-driven feature extraction methods cannot utilize the rich semantic information in the environment, which leads to the performance of the SLAM system being susceptible to various interferences. In this work, we present a semantic-aware multi-level information fusion scheme for robust global orientation estimation. Specifically, a visual semantic perception system based on the synthesized surround view image is proposed for the multi-eye surround vision system widely used in mobile robots, which is used to obtain the visual semantic information required for SLAM tasks. The original multi-eye image was first transformed to the synthesized surround view image, and the passable space was extracted with the help of the semantic segmentation network model as a mask for feature extraction; moreover, the hybrid edge information was extracted to effectively eliminate the distorted edges by further using the distortion characteristics of the reverse perspective projection process. Then, the hybrid semantic information was used for robust global orientation estimation; thus, better localization performance was obtained. The experiments on an intelligent vehicle, which was used for automated valet parking both in indoor and outdoor scenes, showed that the proposed hybrid multi-level information fusion method achieved at least a 10-percent improvement in comparison with other edge segmentation methods, the average orientation estimation error being between 1 and 2 degrees, much smaller than other methods, and the trajectory drift value of the proposed method was much smaller than that of other methods.

Variational Information Bottleneck Regularized Deep Reinforcement Learning for Efficient Robotic Skill Adaptation.

Deep Reinforcement Learning (DRL) algorithms have been widely studied for sequential decision-making problems, and substantial progress has been achieved, especially in autonomous robotic skill learning. However, it is always difficult to deploy DRL methods in practical safety-critical robot systems, since the training and deployment environment gap always exists, and this issue would become increasingly crucial due to the ever-changing environment. Aiming at efficiently robotic skill transferring in a dynamic environment, we present a meta-reinforcement learning algorithm based on a variational information bottleneck. More specifically, during the meta-training stage, the variational information bottleneck first has been applied to infer the complete basic tasks for the whole task space, then the maximum entropy regularized reinforcement learning framework has been used to learn the basic skills consistent with that of basic tasks. Once the training stage is completed, all of the tasks in the task space can be obtained by a nonlinear combination of the basic tasks, thus, the according skills to accomplish the tasks can also be obtained by some way of a combination of the basic skills. Empirical results on several highly nonlinear, high-dimensional robotic locomotion tasks show that the proposed variational information bottleneck regularized deep reinforcement learning algorithm can improve sample efficiency by 200-5000 times on new tasks. Furthermore, the proposed algorithm achieves substantial asymptotic performance improvement. The results indicate that the proposed meta-reinforcement learning framework makes a significant step forward to deploy the DRL-based algorithm to practical robot systems.

Robust tracking control for magnetic wheeled mobile robots using adaptive dynamic programming.

To handle the tracking control problem of the magnetic wheeled mobile robot (MWMR), this paper developed an online robust tracking control scheme by adaptive dynamic programming (ADP). The problem, that how to achieve optimal tracking control of continuous-time (CT) MWMR system with the time-varying unknown uncertainty, can be solved indirectly through matching the optimal tracking control of the associated nominal system . A single critic NN-based actor-critic structure is tailored for simpler controller architecture. By minimizing the Bellman error with gradient descending and least-squares updating laws, the critic NN weights can be optimized online. Thus the optimal cost function and the optimal control signal can be approximated with high precision. Using the Lyapunov stability theorem, the convergence of the critic NN weights, and the stability of the closed-loop system is provided. Simulations, in comparison with robust PD control and adaptive control, are presented to illustrate the effectiveness of the proposed tracking control method for the MWMR.

Task-Oriented Deep Reinforcement Learning for Robotic Skill Acquisition and Control.

Reinforcement learning (RL) and imitation learning (IL), especially equipped with deep neural networks, have been widely studied for autonomous robotic skill acquisition and control tasks. However, these methods and their extensions require extensive environmental interactions during training, which greatly prevents them from being applied to real-world robots. To alleviate this problem, we present an efficient model-free off-policy actor-critic algorithm for robotic skill acquisition and continuous control, by fusing the task reward with a task-oriented guiding reward, which is formulated by leveraging few and imperfect expert demonstrations. In this framework, the agent can explore the environment more intentionally, thus sampling efficiency can be achieved; moreover, the agent can also exploit the experience more effectively, thereby substantially improved performance can be realized simultaneously. The empirical results on robotic locomotion tasks show that the proposed scheme can lower sample complexity by 2-10 times in contrast with the state-of-the-art baseline deep RL (DRL) algorithms, while achieving performance better than that of the expert. Furthermore, the proposed algorithm achieves significant improvement in both sampling efficiency and asymptotic performance on tasks with sparse and delayed reward, wherein those baseline DRL algorithms struggle to make progress. This takes a substantial step forward to implement these methods to acquire skills autonomously for real robots.

A Heuristic algorithm for robustly stable generalized disturbance observer synthesis with closed loop consideration.

This paper investigate the problem of disturbance observer (DOB) based disturbance rejection control with closed loop performance guaranteed. A generalized DOB based control framework is presented. Specifically, the novel DOB framework if obtained by taking advantage of Youla factorization of a two degree-of-freedom controller in a nontraditional way. Closed loop analysis clarifies that generalized DOB inherits advantages of the traditional one, while mitigating its restrictions. Through appropriate reconfiguration, the Q-filter synthesizing is transformed into reduced-order controller designing. By taking advantage of the Kalman-Yakubovich-Popov (KYP) lemma and projection lemma, a two-stage heuristic algorithm is proposed: an initial full information controller is firstly established for the reconfigured system, which is used to heuristically obtain the reduced-order controller by alternately solving LMIs in the second stage. Finally, we illustrate the application of these results to minimum phase and non-minimum phase plants. Elaborated comparisons between the two-stage heuristic algorithm based generalized DOB and the state-of-art H∞ based DOB are conducted, the results verify the effectiveness and advantages.