Shuai Liu’s research while affiliated with Shenyang Institute of Automation and other places

Indoor mobile robotics (IMR) has gained significant attention due to its potential applications in various domains, such as healthcare, logistics, and domestic assistance. However, navigating through indoor environments and performing safe manipulations still pose intractable challenges in terms of navigation accuracy and obstacle avoidance. To solve these issues, this paper presents an Artificial Intelligence (AI) embodied multimodal perception framework for IMR intelligent navigation and safe manipulation. To ensure the navigation accuracy and robustness, we employ the complementary forward RGB camera, downward QR vision sensor, and wheel encoder measurements in a unified framework. The visual residuals and wheel odometry residuals are jointly minimized to estimate the robot states. To guarantee the safety of robotic manipulation tasks, we have developed an AI model that integrates transformer network with convolutional neural network, to associate the long-range RGB & depth patches and aggregate the multi-scale obstacle features, enabling the precise detection and segmentation of obstacles in RGB-D images. Afterwards, the depths of detected obstacles are regressed, providing the robot with crucial information for collision avoidance. Eventually, we design a refined robot manipulation system that dynamically adjusts the robot behavior to ensure effective collision avoidance and to minimize potential damage to its mechanical components by constantly evaluating the spatial relationships between the robot and its surroundings. By incorporating advanced obstacle detection and the avoidance mechanism, mobile robots can navigate reliably in indoor environments with a reduced risk of collisions and real-time decision making. The presented method has been evaluated on the developed IMR platform. On the collected dataset, the estimated IMR absolute position and orientation errors are less than 0.18m and 5° respectively. Besides, it achieves 89% mAP on obstacle detection. The maximum of the estimated obstacle relative depth & orientation errors are less than 0.4m and 2° respectively, which proves competitiveness against the state-of-the- art in both robot navigation and safe manipulation.

Conventional wireless AGV systems based on WiFi or ZigBee suffer random network performance and fail to guarantee the ordered and reliable transmission in AGV applications. In this paper, we study the transmission scheduling with order constraints in WIA-FA based AGV systems. We first present the transmission process of data packets in the WIA-FA based AGV system, and design a novel superframe structure to support the ordered data exchange in the AGV system. Then, we design three heuristic rules for timeslot allocation to regulate the device transmissions subject to order constraints. Finally, inspired by the designed rules, we propose a Dynamic Expected packet Loss rate based Timeslot Allocation (DELTA) algorithm and prove its time complexity rigorously. Simulation results show that the proposed DELTA algorithm outperforms the existing works in terms of transmission reliability for different channel conditions and network scales.

Intelligent factory automation systems strongly rely on industrial wireless control networks which have to ensure timely and reliable data exchange among their components. This paper presents a comprehensive survey on recently approved International Electrotechnical Commission standard Wireless networks for Industrial Automation-Factory Automation (WIA-FA). This paper first introduces the system architecture of WIA-FA including network device, network topology, and system management, and then illustrates WIA-FA protocol stack and key technologies. Furthermore, two WIA-FA testbeds are described to demonstrate the high performance of WIA-FA. After that, three examples of practical applications are provided in this paper. One application deploys a WIA-FA network to monitor and control industrial robots in a digital workshop. The second application adopts the deployment of WIA-FA as a real-time wireless network that connects automated guided vehicles (AGVs) in a logistic sorting system. The last application coordinates multiple cooperative AGVs via the WIA-FA network to carry large and complex components. Finally, the open issues and future directions for WIA-FA networks are presented.

Addressing the extremely high reliability is a critical problem of wireless control system in actual industrial applications. In this paper fully considering the characteristics of the wireless network transmission and industrial automation application, a grouping TDMA access approach based on multiple access points is proposed. The main idea is that: redundant multiple access points are applied in communication to improve the system reliability, multiple time synchronizations are made because of beacon frames of multiple access points to improve the accuracy, and ACK is replied by reliable access point to reduce retransmission and improve resource utilization. Finally, a corresponding access device is designed and implemented. Experiments show that it can meet the harsh requirements for the communication in control loops and demonstrates the effectiveness of the proposed approach.

WIA-PA (Wireless Networks for Industrial Automation - Process Automation) is IEC international standard. In order to solve the primary problem of wireless network in industrial applications, i.e. anti-jamming problem, WIA-PA support adaptive hopping technology. Two adaptive hopping methods based on WIA-PA standard are proposed in this paper. The proposed methods is realized and evaluated based on the experimental platform, the result indicates the feasibility and availability of the proposed methods.