This paper presents a motion planning approach for rigid-soft hybrid robots that incorporates continuous expression and input mapping, aiming to achieve both tracking and collision avoidance. The conventional piecewise constant curvature model utilized in soft robots suffers from issues of discontinuity and singularity. These inherent drawbacks pose significant challenges when attempting to address the nonlinear problems associated with motion planning. To overcome these limitations, we establish a relationship between the length of drive cable and the coordination of robot terminal. Through the application of Taylor transformation, we effectively eliminate the control variation present in the denominator of this expression. Then, the nonlinear model predictive control (NMPC) with this model expression and input mapping is proposed for motion planning. By directly constructing the current input and output of the motion planning based on the combination of previous data and substituting the linearized part of the model with this data, we mitigate the impact of model inaccuracies on the solution process. Both the obstacles and robot are modeled as polyhedra, and the collision problem is reformulated in the form of continuous nonlinear inequalities. These expressions are then incorporated into the NMPC optimization function as collision avoidance constraints. This approach enables the attainment of a larger feasible movement area and more precise tracking results during the motion planning process. Finally, experimental and simulation results conducted on a rigid-soft hybrid robot demonstrate the feasibility and superiority of the proposed method, validating its effectiveness in practical applications.

This study proposes a mixed traffic flow control structure, utilizing multi-lane fleets composed of a small portion of connected and autonomous vehicles (CAVs) in mixed flow as mobile actuators. Multiple fleets formed within the mixed traffic flow feature a novel structure and a valve-like function that regulates both traffic volume and speed, referred to as “valve fleet”. Specifically, the travel speed and structural spacing of valve fleets are controllable parameters, which can regulate the surrounding traffic speed and the flow through the fleet based on the downstream traffic state to decongest bottlenecks. Then, a control-oriented mobile cell transmission model (MCTM) is developed to characterize the macroscopic traffic dynamics with the presence and influence of valve fleets and bottleneck areas on freeways. Moreover, a hierarchical framework for mixed traffic flow regulation is designed, where the upper-level traffic optimization model dynamically determines all fleets’ parameters in a rolling-horizon fashion to minimize total travel time and suppress local congestion. The decentralized lower-level fleet controller adopts model predictive control (MPC) to coordinate CAVs’ motions and handles interactions between CAVs and human-driven vehicles (HDVs). To evaluate the proposed method, we conduct microscopic experiments in the SUMO simulator to implement the proposed traffic control method in realistic traffic environments. The comprehensive comparison results demonstrate the proposed method’s superiority in enhancing traffic efficiency and alleviating congestion at freeway bottleneck segments.

Research on the longitudinal control of mixed platoons has garnered significant attention, particularly addressing the uncertainties of human-driven vehicles (HDVs) and the computational inefficiency of centralized control for large platoons. This paper presents a novel distributed control strategy for multiple mixed platoons, utilizing a flexible model structure to account for dynamic uncertainties and lateral disturbances. A distributed control framework is introduced, where each platoon’s states and constraints are coupled with neighboring platoons, improving scalability and resilience in large-scale mixed traffic environments. Each platoon’s dynamic uncertainties are characterized by convex hulls in the subsystem model. A distributed data-driven input mapping method is proposed, where subsystems dynamically map the process data linearly from both local and neighboring platoons to local control law, eliminating the need for pre-collected datasets or offline training. To reduce iterations and communication requirements, a distributed implementation algorithm is proposed by designing subsystem basic laws with robust model predict control, then the control problem is solved online using convex quadratic programming, enabling fast computation of control actions and enhancing adaptability to real-time traffic variations. Additionally, a theoretical analysis guarantees the stability and optimality of the system, providing conditions that assist in subsystem parameter design for practical implementation. Simulation results validate the effectiveness and efficiency of the proposed approach, particularly in comparison to traditional methods that rely on pre-collected data.

Path tracking is a key problem in autonomous vehicles, but uncertainties in vehicle models and environmental parameters as well as the couple of lateral and longitudinal behaviors pose a major challenge to path following accuracy and efficiency. To solve this problem, a layered vehicle lateral and longitudinal controller based on model predictive control is proposed in this paper. Firstly, a lateral controller based on a synthesis of robust model predictive control is used to reduce the influence of model uncertainty. Then a longitudinal nonlinear model predictive controller is proposed to achieve the coordination between the lateral following error and the longitudinal forward speed of the vehicle. Finally, the bottom PID controller is used to control the throttle and brake of the vehicle. The effectiveness of the proposed method is verified by experiments, which show that it is robust to the uncertain parameters and the computation is relatively small.

The coordination of traffic flow among regions is necessary for a large-scale road traffic network to avoid local congestions and improve the overall traffic efficiency. In this paper, by incorporating the random characteristic of traffic flow, we formulate the problem of perimeter traffic flow control for a multi-region traffic network as a Markov decision process with adaptive state definition. Based on stochastic macroscopic fundamental diagrams (MFD) of regions, a state transition probability model is proposed to describe the state changes of the multi-region traffic network under different perimeter control policies. With the stochastic MFD-based state transition probabilities rather than counting from the historical data, a policy iteration algorithm with perturbation analysis is introduced to get the optimal perimeter control policy in real-time without the requirement of online or offline learning. The proposed method is compared with the classic perimeter control methods by simulation, which indicates its effectiveness in mitigating the congestion and improving the network throughput, as well as the promising implementation prospect.

This study proposes a cooperative platoon formation scheme in a multi-lane traffic environment with connected and autonomous vehicles (CAVs). It coordinates the lane-changing decisions and longitudinal trajectories of CAVs to form platoons based on each vehicle’s target lane, aiming to reduce the negative impact of lane-changing maneuvers on traffic flow and improve platoon formation efficiency. Mathematically, a vehicle model for platoon formation is developed which couples the lateral lane-changing and longitudinal car-following behaviors of vehicles. A model predictive control-based mixed integer linear programming (MILP) problem is formulated, which optimizes all vehicles’ lateral and longitudinal trajectories and improves maneuverability and efficiency. In contrast to the majority of existing studies, the configurations of platoons to be formed are not predefined and can be jointly optimized to further improve flexibility. Moreover, the framework that integrates configuration decisions, trajectory planning and control is executed dynamically with a rolling horizon based on real-time traffic states to enhance reliability. To validate the proposed scheme, we conduct simulation experiments in SUMO to implement the cooperative platoon formation in a typical three-lane traffic flow with different traffic demand levels. The extensive comparison results indicate the superiority of the proposed method in improving traffic flow speed and platoon formation efficiency.

The robust iterative learning control (RILC) can deal with the systems with unknown time-varying uncertainty to track a repeated reference signal. However, the existing robust designs consider all the possibilities of uncertainty, which makes the design conservative and causes the controlled process converging to the reference trajectory slowly. To eliminate this weakness, a data-driven method is proposed. The new design intends to employ more information from the past input-output data to compensate for the robust control law and then to improve performance. The proposed control law is proved to guarantee convergence and accelerate the convergence rate. Ultimately, the experiments on a robot manipulator have been conducted to verify the good convergence of the trajectory errors under the control of the proposed method.

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles spatial interactions using a graph convolutional network (GCN), and captures temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM). Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance.

This paper presents a new method to estimate real-time queue lengths at a signalized intersection by utilizing limited data extracted from surveillance videos. This method focuses on reconstructing vehicles' trajectories on an entire road segment. The real-time queue length can be derived from these reconstructed trajectories. In order to improve the accuracy of the trajectory reconstruction, a built-up car-following model is proposed to reconstruct the trajectories of vehicles joining and leaving the queue, respectively, which are further corrected by a fusion algorithm. The proposed method was validated in the Next Generation Simulation dataset, and the queue length for each signal cycle can be estimated with a high accuracy. The results show that the proposed method has a higher precision compared to three baseline models.

System deterioration detection and root cause analysis is crucial for today's industrial society. However, the design and operation of mechanic system is getting more and more complex, which makes it hard at identifying deterioration with noisy data. Our research focuses on solving such problem on time-evolving sensor graphs in a streaming setting. Given a sequence of graphs, the ability to identify 1) any gradual and stable structured change and 2) the root cause components is of importance for early warning and system diagnosis. Existing methods either raise too many false alerts on instant changes or are too sensitive to noise. To address these problems, we propose Robust Failure Detection and Diagnosis (RoFaD). RoFaD can capture failure propagation given a time series of graph. By optimizing a matrix-based Taylor expansion, RoFaD can identify system deterioration in the presence of noise and immediate changes, and diagnose the root cause components. Experiments on both synthetic and real world datasets demonstrate that RoFaD is more effective than the popular baselines.

Power plant is a complex and nonstationary system for which the traditional machine learning modeling approaches fall short of expectations. The ensemble-based online learning methods provide an effective way to continuously learn from the dynamic environment and autonomously update models to respond to environmental changes. This paper proposes such an online ensemble regression approach to model power plant performance, which is critically important for operation optimization. The experimental results on both simulated and real data show that the proposed method can achieve performance with less than 1% mean average percentage error, which meets the general expectations in field operations.

Data-driven predictive analytics are in use today across a number of industrial applications, but further integration is hindered by the requirement of similarity among model training and test data distributions. This paper addresses the need of learning from possibly nonstationary data streams, or under concept drift, a commonly seen phenomenon in practical applications. A simple dual-learner ensemble strategy, alternating learners framework, is proposed. A long-memory model learns stable concepts from a long relevant time window, while a short-memory model learns transient concepts from a small recent window. The difference in prediction performance of these two models is monitored and induces an alternating policy to select, update and reset the two models. The method features an online updating mechanism to maintain the ensemble accuracy, and a concept-dependent trigger to focus on relevant data. Through empirical studies the method demonstrates effective tracking and prediction when the steaming data carry abrupt and/or gradual changes.