This paper considers the presence of movable bridges in inland waterway transport, and presents a control framework for the joint dynamic coordination of bridge operations and autonomous vessel navigation to minimize waiting times of vessels at bridges. Simultaneous evolution of bridge occupancy and vessel position is captured by a control-oriented model that incorporates qualitative behavior in the form of propositional logic expressions. A model predictive control (MPC) strategy is designed considering adaptable bridge opening regimes to exploit vessel passage demand, and operational preferences of both vessel skippers and bridge operators are taken into account to reach fair trade-off decisions. A realistic case study pertaining to the Rhine-Alpine corridor is used to demonstrate the effectiveness of the approach. Appropriate key performance indicators (KPIs) are defined and employed for a quantitative comparison with a mixed-integer programming (MIP) strategy with fixed opening regimes. Furthermore, sensitivity to the main MPC parameters is examined by carrying out extensive testing to assess the effect of each design parameter on the solution.

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented to help follower agents acquire knowledge and experience from a single well-trained pioneer agent. Notably, the designed approach can transfer both the policy representation and sample experience provided by the pioneer policy in the off-policy learning. More importantly, the proposed method can adaptively adjust the learning weight of prior experience and exploration according to the Wasserstein distance between the policy probability distributions of the pioneer and the follower. Case studies show that the distributed agents trained by the proposed method can complete a collaborative task and acquire the maximum rewards while minimizing the violation of constraints. Moreover, the proposed method can also achieve satisfactory performance in terms of learning speed and success rate.

This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.

This paper investigates a novel routing problem of a multi-robot station in a manufacturing cell. In the existing literature, the objective is to minimize the cycle time or energy consumption separately. The routing problem considered in this paper aims to reduce the cycle time and energy consumption jointly for each robot while avoiding collisions between these robots. For this routing problem, we propose a new flexible time-space network model that allows us to reduce energy consumption while minimizing the cycle time. The corresponding optimization problem is Mixed-Integer Nonlinear Programming (MINLP). For addressing its computational complexity, this paper designs a metaheuristic algorithm tailored to the studied problem and proposes an$\varepsilon $-constraint algorithm to study the trade-off between these two objectives. We conduct industrially relevant simulation experiments of case studies to show its effectiveness, in comparison to a conventional method, two state-of-the-art solvers, and two commonly-used metaheuristics. The results show that the proposed methodology can reduce energy consumption by up to 30% without compromising the cycle time. Meanwhile, the proposed algorithm can provide efficient solutions within a reasonable computation time.Note to Practitioners—This paper is motivated by the problem of improving energy efficiency when routing cooperative robots in a manufacturing station. In current approaches for routing multi-robot stations, the cycle time and energy consumption are minimized separately. This paper focuses on the movement of the robot end-effector and its connected joint and suggests a new approach to minimize these two objectives jointly by proposing a new mathematical model. The resulting planning problem is computationally intractable. A customized metaheuristic algorithm is thus designed for efficiently solving this planning problem. Our meta-heuristic algorithm is integrated with the$\varepsilon $-constraint method to study the relationship between these two objectives. Simulation experiments suggest that this approach can reduce energy consumption considerably, for the shortest cycle time, compared with the current approaches. In future research, the movements of multi-joints will be investigated whereby 3-D collision-free trajectory planning will be considered.

Vessels sailing in a single platoon could reduce resistance from the perspective of the whole platoon and the individual vessel, and contribute to improving energy benefits. Moreover, transportation energy costs and traffic efficiency are essential indicators for measuring waterborne transportation systems. We attempt to minimize transportation energy costs by coordinating platoon formation using a distributed framework of controllers. A large-scale coordinated vessel platooning program is proposed to minimize transportation energy costs and optimize traffic efficiency while guaranteeing safety. The control framework covers routing, energy consumption-dependent cooperative platooning decision and speed optimization based on graph search algorithm, cluster analysis, optimal control approach and model predictive control. Firstly, a local scheduling strategy combined with the leader vessel selection algorithm is adopted. Furthermore, we used cluster analysis to create a series of mergeable vessel platooning sets. Then, we used the mathematical planning method and a two-step hybrid optimal control approach to calculate the improvement and optimization of each vessel platoon’s path and speed. Finally, the scalability of the scheduling strategy is elucidated. In a simulation of large scale inland waterborne network, savings surpassed 3.5% when six hundreds vessels participated in the system. These simulation results reveal that the scheduling strategy coordinating vessels into vessel platooning, which improves transportation efficiency as well as descends cost, comparing to a fixed origin route in the waterway network.

Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0–1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment.

This paper addresses the flexible formation problem for unmanned surface vehicles in the presence of obstacles. Building upon the leader-follower formation scheme, a hybrid line-of-sight based flexible platooning method is proposed for follower vehicle to keep tracking the leader ship. A fusion artificial potential field collision avoidance approach is tailored to generate optimal collision-free trajectories for the vehicle to track. To steer the vehicle towards and stay within the neighborhood of the generated collision-free trajectory, a nonlinear model predictive controller is designed. Experimental results are presented to validate the efficiency of proposed method, showing that the unmanned surface vehicle is able to track the leader ship without colliding with the surrounded static obstacles in the considered experiments.

This paper proposes a multi-objective cooperative control method for a ship-towing system in congested water traffic environments. The control objectives are to coordinate multiple autonomous tugboats for transporting a ship to: (i) follow the waypoints, (ii) adjust the heading, (iii) track the speed profile, and (iv) resolve collisions. The problem is tackled by the design of multiple control agents distributed in two control layers. Based on the strategy of model predictive control (MPC), the supervisory controller in the higher layer calculates the towing angles and forces of the ship, the tug controller in the lower layer computes the tug thruster forces and moment. The consensus between the lower and higher layer control is achieved by using the altering direction method of multipliers (ADMM) that makes the predicted tug position and heading approach to the desired tug trajectory. Simulation experiments indicate that the proposed method coordinates multiple autonomous tugboats to transport a ship smoothly and effectively and succeeds in multiple control objectives, in the meantime, the avoidance operation complies with COLREGS rules.

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An$\epsilon $-constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods.

This article investigates a new collision-free routing problem of a multirobot system. The objective is to minimize the cycle time of operation tasks for each robot while avoiding collisions. The focus is set on the operation of the end-effector and its connected joint, and the operation is projected onto a circular area on the plane. We propose to employ a time-space network (TSN) model that maps the robot location constraints into the route planning framework, leading to a mixed integer programming (MIP) problem. A dedicated genetic algorithm is proposed for solving this MIP problem and a new encoding scheme is designed to fit the TSN formulation. Simulation experiments indicate that the proposed model can obtain the collision-free route of the considered multirobot system. Simulation results also show that the proposed genetic algorithm can provide fast and high-quality solutions, compared to two state-of-the-art commercial solvers and a practical approach.

Urban waterways have great potential in cargo transport to relieve the congestion in the overloaded road networks. This paper explores the potential of applying cooperative multi-vessel systems (CMVSs) to improve the safety and efficiency of transport in urban waterway networks. A framework consisting of vessel train formation (VTF) and cooperative waterway intersection scheduling (CWIS) is proposed. Two types of controllers are introduced. Intersection controllers solve the CWIS problems and assign each vessel a desired time of arrival and vessel controllers are responsible for the VTF in waterway segments and the timely arrival at the intersections. An alternating direction method of multipliers (ADMM)-based negotiation framework is proposed for the cooperation among the controllers. The simulation experiments involving the scenarios in which up to 50 vessels sailing in the canal network in Amsterdam are carried out to illustrate the effectiveness of the proposed approach. In the simulation of an isolated intersection, rescheduling is triggered when some vessels cannot arrive on time. Although some ASVs arrive later, the time that is needed for all the ASVs to pass through is the same after rescheduling. Moreover, we compare the cooperative situation with the proposed CMVSs with a baseline situation. In the baseline situation, vessels avoid collisions using the generalized velocity obstacle (GVO) method and cross the intersection with a first in, first out rule. The CMVSs show better path following performance, while the GVO method needs fewer velocity changes. From the perspective of efficiency, the CMVSs help to reduce the total time to pass through the intersection.

This paper proposes a new modeling and control methodology for allocating materials in a dry bulk terminal with a finite storage capacity. The dynamical process of material storage allocation in the terminal is modeled using a hybrid system perspective that combines both discrete-event and continuous time dynamics. The stockyard space is partitioned into a number of slots for exchanging incoming and outgoing material flows in the terminal, leading to a so-called mixed logical dynamical (MLD) model with the maximal storage capacity. Based on the MLD model, a model predictive controller is then proposed for maximizing the economic profit in a rolling horizon manner. A number of Monte Carlo simulations have been performed involving a real case study for analyzing the effects of different slot volumes on the economic performance and the computational performance of the controller. Simulations also demonstrate the potential of the proposed methodology.

Waterborne autonomous guided vessels (waterborne AGVs) moving over open waters experience environmental uncertainties. This paper proposes a novel cost-effective robust distributed control approach for waterborne AGVs. The overall system is uncertain and has independent subsystem dynamics but coupling objectives and state constraints. Waterborne AGVs determine their actions in a parallel way, while still minimizing an overall cost function and respecting coupling constraints robustly by communicating within a neighborhood. Our first contribution is the proposal of the system robustness level for the cost-effective robust distributed model predictive control (RDMPC) for waterborne AGVs. Cost-effective RDMPC models the price of robustness by explicitly considering uncertainty and system characteristics in a tube-based robust control framework. The second contribution is an efficient integrated branch & bound (B&B) and the alternating direction method of multipliers (ADMMs) algorithm for solving the cost-effective RDMPC problem. The algorithm exploits special ordered variable sets and combining branching criteria with intermediate ADMM results conducting smart search in B&B. Simulation results demonstrate the effectiveness of the proposed approach for cooperative distributed waterborne AGVs with cost-effective robustness.

Transport over water plays an important role in the transport of goods in Europe. More than 37,000 kilometers of waterways connect hundreds of cities and industrial regions. Nowadays, the European Commission aims to promote and strengthen the competitive position of transport over water in the transport system and to facilitate its integration into the intermodal logistic chain due to its reliability, low environmental impact and ample capacity available for increased exploitation. The objective of this paper is to propose a new integrated framework for improving the efficiency of transport over water at both tactical and operational levels. Research directions addressing main problems at these levels are discussed in this paper: vessel rotation planning and path following for waterborne AGVs. Moreover, this paper presents perspectives regarding future research directions to develop this integrated framework.

With the increasing application of distributed energy resources and novel information technologies in the electricity infrastructure, innovative possibilities to incorporate the demand side more actively in power system operation are enabled. A promising, controllable, residential distributed generation technology is a microcombined heat and power system (micro-CHP). Micro-CHP is an energy-efficient technology that simultaneously provides heat and electricity to households. In this paper, we investigate to what extent domestic energy costs could be reduced with intelligent, price-based control concepts (demand response). Hereby, first the performance of a standard, so-called heat-led micro-CHP system is analyzed. Then, a model-predictive control (MPC) strategy aimed at demand response is proposed for more intelligent control of micro-CHP systems. Simulation studies illustrate the added value of the proposed intelligent control approach over the standard approach in terms of reduced variable energy costs. Demand response with micro-CHP lowers variable costs for households by about 1%-14%. The cost reductions are highest with the most strongly fluctuating real-time pricing scheme.

Irrigation canals transport water from water sources (such as large rivers and lakes) to water users (such as farmers). Irrigation canals are typically very large in nature, covering vast geographical areas, and involving a significant number of control actuators, such as pumps, gates, and locks. The control of such canals is aimed at guaranteeing the adequate delivery of water with minimal water spillage and with minimal control structure usage. To take into account forecasts on, e.g., water consumption and weather, model predictive control (MPC) can be used to determine which actions to take. For large-scale systems, in which different parts of the canal are owned by different parties, distributed MPC control could then be employed. Although iterative distributed MPC approaches proposed earlier in the literature may yield overall optimal performance, the amount of iterations required before achieving this performance may be large, and thus require a significant amount of time. In this paper, the structure of systems consisting of serially interconnected subsystems is exploited to obtain an efficient non-iterative, cascaded MPC scheme. Simulation studies on a 7-reach irrigation canal illustrate the performance of this non-iterative scheme in comparison with an iterative scheme.