Wireless control systems (WCSs) are gaining rapid development in industrial automation. Compared to the star topology, mesh networks offer greater compatibility for large-scale applications that require high reliability, scalability, and extended coverage. In WCSs, multiple control loops share the multi-hop mesh network, leading to non-negligible and long- span communication latency in critical flows, which can severely degrade the overall control performance. Additionally, the criticality of each control flow largely depends on the features of the physical plant dynamics and the mesh network configuration, which is essential to properly and exactly represent. Moreover, the online scheduling and reconfiguration for large-scale mesh network for WCSs also pose unique challenges. In this paper, we propose a mesh network scheduling mechanism based on cyber-physical sensitivity. Firstly, we model each control loop as a switched system to represent the impact of arbitrary and fluctuating communication latency. Second, we propose a novel online criticality indicator, cyber-physical sensitivity (CP-Sensi), which accurately reflects the criticality of each control flow by synthesizing the switched model, runtime physical states, and network conditions. Finally, we design a CP-Sensi-based scheduling mechanism and an efficient piggyback-based network reconfiguration protocol tailored for mesh networks. Extensive studies with 12 control loops demonstrate that the proposed CP-Sensi and online mesh network scheduling achieve superior control performance compared to state-of-the-art approaches.

As industrial control applications scale up, mesh networked control systems (MNCSs) are gaining popularity, where multiple control loops share a multi-hop mesh network. However, these loops often suffer from long-span, time-varying delays caused by the multi-hop transmissions of multiple flows, which significantly degrade control performance, particularly stability. Existing studies on delay-aware stability conditions are usually independent with network scheduling, leading to a pessimistic stability analysis. Meanwhile, existing stability-aware scheduling approaches rely on coarse-grained designs, further worsening stability guarantees and limiting network capacity. In this work, we propose a stability-guaranteed scheduling mechanism for MNCSs with fine-grained timing. We first establish a stability condition that accounts for time-varying delays over an extended horizon spanning multiple superframes, which reduces the pessimism in stability analysis and enables more refined scheduling strategies. Based on this condition, We design a Long time-horizon and Fine-grained network scheduling mechanism with Stability Guarantee (LFSG), which deterministically maps the stability condition into the fine-grained network scheduling, considering fluctuating delays over the extended horizon. Furthermore, we provide a stability-capacity-aware LFSG (SCA-LFSG), which aims to maximize the number of stabilizable control loops and demonstrates its effectiveness through various application paradigms. Extensive studies demonstrate the advantages of stability analyses, LFSG, and SCA-LFSG over state-of-the-art approaches in terms of both control and timing performance.

State-of-the-art techniques for monocular camera reconstruction predominantly rely on the Structure from Motion (SfM) pipeline. However, such methods often yield reconstruction outcomes that lack crucial scale information, and over time, accumulation of images leads to inevitable drift issues. In contrast, mapping methods based on LiDAR scans are popular in large-scale urban scene reconstruction due to their precise distance measurements, a capability fundamentally absent in visual-based approaches. Researchers have made attempts to utilize concurrent LiDAR and camera measurements in pursuit of precise scaling and color details within mapping outcomes. However, the outcomes are subject to extrinsic calibration and time synchronization precision. In this paper, we propose a novel cost-effective reconstruction pipeline that utilizes a pre-established LiDAR map as a fixed constraint to effectively address the inherent scale challenges present in monocular camera reconstruction. To our knowledge, our method is the first to register images onto the point cloud map without requiring synchronous capture of camera and LiDAR data, granting us the flexibility to manage reconstruction detail levels across various areas of interest. To facilitate further research in this domain, we have released Colmap-PCD3, an open-source tool leveraging the Colmap algorithm, that enables precise fine-scale registration of images to the point cloud map.

Modern control systems are embracing multi-tier architectures integrating end devices and edge servers. However, due to the distinct control performance demands associated with each control task, it is a formidable challenge to optimize the control performance of multiple control tasks subject to stringent computation resource constraints while guaranteeing stability. Moreover, inherent contradictions exist in the timing aspect between the stability guarantee, which relies on offline analysis, and the run-time control performance, which should be enhanced online. It is essential to bridge the gap between the real-time scheduling of control tasks and their actual control performance. In this paper, we propose a novel real-time scheduling approach for multi-tier control systems, which leverages end devices for executing real-time control tasks and edge devices for runtime coordination. Specifically, we first introduce a new datadriven value function, called time/state/utility functions (TSUF), for modeling control system performance. TSUF captures not only timing but also the dynamic states of the physical plants. Subsequently, we propose value-based control scheduling (VCS), which is a multi-granularity scheduling mechanism based on our TSUF value function. VCS distinguishes the scheduling of stability jobs for ensuring system stability and performance jobs for optimizing real-time control performance based on run-time physical states. Finally, through realistic case studies involving multiple control loops, we demonstrate the advantages of VCS over existing scheduling approaches in terms of both control and real-time performance.

Industrial Internet of Things (IIoT)-based control is growing rapidly, such as smart factories and industrial automation. Sensing and transmitting physical state measurements is the first step and the prerequisite for IIoT-based control. However, sensor interference (e.g., electromagnetic interference on sensing, temperature, and humidity variations in the field) and network interference (e.g., metal obstacles and background noises) may destroy the control performance by interfering with sensing and communication processes. Most of the present upstream “fixed sensors-networking-state estimation” approaches cannot effectively deal with sensor and network interferences due to the fixed measurements/estimation and network resource limitations. To optimize the performance of IIoT-based control, we propose a smart sensing and communication co-design (SSCC) framework to select more potential sensors and establish the corresponding network scheduling. SSCC consists of a smart estimator (SE) and a sensing communication mode switching (SCMS) agent. The SE detects sensor interference and obtains resilient state estimation based on collaborative sensing. SCMS agent dynamically switches sensor selections and network configurations (routing and transmission number) in an integrated manner based on the network and plant states by solving a performance optimization problem. We propose a lightweight SCMS approach by searching a predefined mode table. We perform simulations integrating TOSSIM and MATLAB/Simulink, and semi-physical experiments on a real wireless sensor-actuator network composed of TelosB nodes. The results show that the SSCC framework can effectively improve the control performance and enhance network energy efficiency under various types of interference by dynamically selecting sensors and allocating network resources.

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the “short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of \(-\) 76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. The ten-loop case indicates the effectiveness and scalability of the proposed approaches.

Reorientation (turning in plane) plays a critical role for all robots in any field application, especially those that in confined spaces. While important, reorientation remains a relatively unstudied problem for robots, including limbless mechanisms, often called snake robots. Instead of looking at snakes, we take inspiration from observations of the turning behavior of tiny nematode worms C. elegans. Our previous work presented an in-place and in-plane turning gait for limbless robots, called an omega turn, and prescribed it using a novel two-wave template [1]. In this work, we advance omega turn-inspired controllers in three aspects: 1) we use geometric methods to vary joint angle amplitudes and forward wave spatial frequency in our turning equation to establish a wide and precise amplitude modulation and frequency modulation on omega turn; 2) we use this new relationship to enable robots with fewer internal degrees of freedom (i.e., fewer joints in the body) to achieve desirable performance, and 3) we apply compliant control methods to this relationship to handle unmodelled effects in the environment. We experimentally validate our approach on a limbless robot that the omega turn can produce effective and robust turning motion in various types of environments, such as granular media and rock pile.

Isolated mechanical systems—e.g., those floating in space, in free-fall, or on a frictionless surface—are able to achieve net rotation by cyclically changing their shape, even if they have no net angular momentum. Similarly, swimmers immersed in “perfect fluids” are able to use cyclic shape changes to both translate and rotate even if the swimmer-fluid system has no net linear or angular momentum. Finally, systems fully constrained by direct nonholonomic constraints (e.g., passive wheels) can push against these constraints to move through the world. Previous work has demonstrated that the displacement induced by these shape changes corresponds to the amount ofconstraint curvaturethat the gaits enclose. Properly assessing or optimizing the utility of a gait also requires considering the time or resources required to execute it: A gait that produces a small displacement per cycle, but that can be executed in a short time, may produce a faster average velocity than a gait that produces more displacement, but takes longer to complete a cycle at the same instantaneous effort. In this paper, we consider gaits under two instantaneous measures of effort. For each of these costs, we demonstrate that fixing the average instantaneous cost to a unit value allows us to transform the effort costs into time-to-execute costs for any given gait cycle. We then illustrate how the interaction between the constraint curvature and these costs leads to characteristic geometries for optimal cycles, in which the gait trajectories resemble elastic hoops distended from within by internal pressures.

Traditional geometric mechanics models used in locomotion analysis rely heavily on systems having symmetry in SE(2) (i.e., the dynamics and constraints are invariant with respect to a system’s position and orientation) to simplify motion planning. As a result, the symmetry assumption prevents locomotion analysis on non-flat surfaces because the system dynamics may vary as a function of position and orientation. In this paper, we develop geometric motion planning strategies for a mobile system moving on a position space whose manifold structure is a cylinder: constant non-zero curvature in one dimension and zero curvature in another. To handle this non-flat position space, we adapt conventional geometric mechanics tools - in particular the system connection and the constraint curvature function - to depend on the system orientation. In addition, we introduce a novel constraint projection method to a variational gait optimizer and demonstrate how to design gaits that allow the example system to move on the cylinder with optimal efficiency.