This study develops a multi-hypothesis extended Kalman filter (MH-EKF) for the online estimation of the bending angle of a 3D printed soft sensor attached to soft actuators. Despite the advantage of compliance and low interference, the 3D printed soft sensor is susceptible to the hysteresis property and nonlinear effects. Improving measurement accuracy for sensors with hysteresis is a common challenge. Current studies mainly apply complex models and highly nonlinear functions to characterize the hysteresis, requiring a complicated parameter identification process and challenging real-time applications. This study enhances the model simplicity and the real-time performance for the hysteresis characterization. We identify the hysteresis by combining multiple polynomial functions and improving the sensor estimation with the proposed MH-EKF. We examine the performance of the filter in the real-time closed-loop control system. Compared with the baseline methods, the proposed approach shows improvements in the estimation accuracy with low computational complexity.

Soft actuators have the advantages of compliance and adaptability when working with vulnerable objects, but the deformation shape of the soft actuators is difficult to measure or estimate. Soft sensors made of highly flexible and responsive materials are promising new approaches to the shape estimation of soft actuators, but suffer from highly nonlinear, hysteresis, and time-variant properties. A nonlinear and adaptive state observer is essential for the shape estimation from soft sensors. Current state estimation methods rely on complex nonlinear data-fitting models, and the robustness of the estimation methods is questionable. This study investigates the soft actuator dynamics and the soft sensor model as a stochastic process characterized by the Gaussian Process (GP) model. The unscented Kalman filter (UKF) is applied to the GP model for more reliable variance adjustment during the sequential state estimation process than conventional methods. In addition, a major limitation of the GP model is its computational complexity during online inference. To improve the real-time performance while guaranteeing accuracy, we introduce an edge server to decrease the onboard computational and memory overhead. The experiments showcase a significant improvement in estimation accuracy and real-time performance compared to baseline methods.

This study presents an innovative approach tooptimal gait control for a soft quadruped robot enabled by four compressible tendon-driven soft actuators. Soft quadruped robots, compared to their rigid counterparts, are widely recognized for offering enhanced safety, lower weight, and simpler fabrication and control mechanisms. However, their highly deformable structure introduces nonlinear dynamics, making precise gait locomotion control complex. To solve this problem, we propose a novel model-based reinforcement learning (MBRL) method. The study employs a multi-stage approach, including state space restriction, data-driven surrogate model training, and MBRL development. Compared to benchmark methods, the proposed approach significantly improves the efficiency and performance of gait control policies. The developed policy is both robust and adaptable to the robot’s deformable morphology. The study concludes by highlighting the practical applicability of these findings in real-world scenarios.

This study focuses on the locomotion capability improvement in a tendon-driven soft quadruped robot through an online adaptive learning approach. Leveraging the inverse kinematics model of the soft quadruped robot, we employ a central pattern generator to design a parametric gait pattern, and use Bayesian optimization (BO) to find the optimal parameters. Further, to address the challenges of modeling discrepancies, we implement a multi-fidelity BO approach, combining data from both simulation and physical experiments throughout training and optimization. This strategy enables the adaptive refinement of the gait pattern and ensures a smooth transition from simulation to real-world deployment for the controller. Compared to previous result using a fixed gait pattern, the multi-fidelity BO approach improves the robot’s average walking speed from 0.14 m/s to 0.214 m/s, an increase of 52.7%. Moreover, we integrate a computational task off-loading architecture by edge computing, which reduces the onboard computational and memory overhead, to improve real-time control performance and facilitate an effective online learning process. The proposed approach successfully achieves optimal walking gait design for physical deployment with high efficiency, effectively addressing challenges related to the reality gap in soft robotics.

Vehicular Edge Computing (VEC) systems exploit resources on both vehicles and Roadside Units (RSUs) to provide services for real-time vehicular applications that cannot be completed in the vehicles alone. Two types of decisions are critical for VEC: one is for task offloading to migrate vehicular tasks to suitable RSUs, and the other is for resource allocation at the RSUs to provide the optimal amount of computational resource to the migrated tasks under constraints on response time and energy consumption. Most of the published optimization-based methods determine the optimal solutions of the two types of decisions jointly within one optimization problem at RSUs, but the complexity of solving the optimization problem is extraordinary, because the problem is not convex and has discrete variables. Meanwhile, the nature of centralized solutions requires extra information exchange between vehicles and RSUs, which is challenged by the additional communication delay and security issues. The contribution of this paper is to decompose the joint optimization problem into two decoupled subproblems: task offloading and resource allocation. Both subproblems are reformulated for efficient solutions. The resource allocation problem is simplified by dual decomposition and can be solved at vehicles in a decentralized way. The task offloading problem is transformed from a discrete problem to a continuous convex one by a probability-based solution. Our new method efficiently achieves a near-optimal solution through decentralized optimizations, and the error bound between the solution and the true optimum is analyzed. Simulation results demonstrate the advantage of the proposed approach.

The decentralized approach is popular for the collaborative collision avoidance of connected vehicles in many scenarios. By modeling the task as a collaborative optimal control problem, Lagrangian methods are widely used to decouple the constraints and enable the decentralized solution. However, potential constraint-violating behavior will lead to oscillations during the Lagrangian update, resulting in more iterations and lower real-time efficiency. Existing methods generally neither address this shortcoming, nor explore the Lagrangian update mechanism. This study takes a control perspective, and solves this collaborative optimal control problem based on an extension of the Alternating Directions Method of Multipliers (ADMM) algorithm by performing the iteration update with a Proportional-Integral-(PI-) regulated controller. The link between the Lagrangian optimization and the PI controller improves the convergence performance during iterations. Simulation results in traffic intersection scenarios demonstrate the advantage of the proposed approach.

Communication delays in Connected and Auto-mated Vehicle (CAV) networks significantly impact decentralized optimization-based coordination, increasing risks of collisions and degrading system performance. Existing methods are limited by real-time computational challenges, vulnerability to outdated data, scalability constraints, and difficulties in managing uncertainties. This paper presents a Stochastic Decentralized Model Predictive Control (SDMPC) framework to mitigate the adverse effects of communication delays by incorporating a novel stochastic approximation method for modeling uncertainties. Our approach provides a tight probabilistic bound on safety constraints, ensuring accurate trajectory predictions and improved coordination. Simulation results show that the proposed SDMPC framework reduces the average trajectory deviation and lowers collision risks compared to conventional methods under various communication latency conditions. These improvements make SDMPC an effective solution for large-scale CAV networks, enhancing both safety and efficiency.