This paper proposes a distributed multi-agent formation control framework, ensuring rigorous safety and solely relying on range-only measurements. To address the challenge of rigid formation control without bearing information, we introduce a bearing estimator with respect to a single landmark. This estimator enables integration with conventional distance-based formation controllers while preserving distributed deployments. To ensure collision-free trajectories in cluttered environments, we develop a safety-critical control law that includes the estimated bearings with control barrier functions (CBFs), formally guaranteeing obstacle avoidance. We rigorously prove the convergence of the bearing estimator, the asymptotic stability of the desired formation shape and the continuity of safety control inputs. The effectiveness of the proposed approach is demonstrated through simulations with multiple agents and obstacles, as well as experimental validation using quadcopters in a cluttered lab environment. A video showcasing the study and experimental results is available at https://www.youtube.com/watch?v=iV0pNrtZbxM.

In this paper, we present a novel underactuated gantry crane (GC) systems where an extra degree-of-freedom actuation is introduced to control the trolley position and its sway angle during motion. The proposed gantry crane systems resembles a prismatic-revolute-prismatic (PRP) robotic configuration where the revolute joint corresponds to the sway angle which is not actuated. Firstly, an energy-based modeling of the PRP-GC systems is presented where both Euler-Lagrange and port-controlled Hamiltonian formalisms are used. Secondly, we present the design of a Neural Interconnection and Damping Assignment Passivity-Based Controller (N-IDA-PBC) that allows for an automated learning of an IDA-PBC controller, where the solutions to the corresponding IDA-PBC matching PDE are not trivial for underactuated systems. Finally, the efficacy of the proposed N-IDA-PBC is evaluated through Monte Carlo simulations, where the neural networks training of N-IDA-PBC uses 3,000 randomly generated data points and 20,000 samples of Monte-Carlo simulation are performed, taking into account uncertainties in the systems’ parameters, including initial states, physical damping, and payload masses. The Monte-Carlo simulations show that the trained N-IDA-PBC is able to regulate the trolley position and sway angle to the set-point position and it is robust against parameter uncertainties.

We present a dynamical modeling of integrated (end-to-end) container terminal operations using finite state machine (FSM) framework where each state machine is represented by a discrete-event system (DES) formulation. The hybrid model incorporates the operations of quay cranes (QC), internal trucks (IT), and yard cranes (YC) and also the selection of storage positions in container yard (CY) and vessel bays. The QC and YC are connected by the IT in our models. As opposed to the commonly adapted modeling in container terminal operations, in which the entire information/inputs to the systems are known for a defined planning horizon, in this research we use real-time trucks, crane, and container storage operations information, which are always updated as the time evolves. The dynamical model shows that the predicted state variables closely follow the actual field data from a container terminal in Tanjung Priuk, Jakarta, Indonesia. Subsequently, using the integrated container terminal hybrid model, we proposed a model predictive algorithm (MPA) to obtain the near-optimal solution of the integrated terminal operations problem, namely the simultaneous allocation and scheduling of QC, IT, and YC, as well as selecting the storage location for the inbound and outbound containers in the CY and vessel. The numerical experiment based on the extensive Monte Carlo simulation and real dataset show that the MPA outperforms by 3-6% both of the policies currently implemented by the terminal operator and the state-of-the-art method from the current literature.

In this article, we present the design and experimental validation of source-seeking control algorithms for a unicycle mobile robot that is equipped with novel 3-D-printed flexible graphene-based piezoresistive airflow sensors. Based solely on a local gradient measurement from the airflow sensors, we propose and analyze a projected gradient ascent algorithm to solve the source-seeking problem. In the case of partial sensor failure, we propose a combination of extremum-seeking control with our projected gradient ascent algorithm. For both control laws, we prove the asymptotic convergence of the robot to the source. Numerical simulations were performed to validate the algorithms, and experimental validations are presented to demonstrate the efficacy of the proposed methods.

In this paper, we study the problem of integrated berth and quay crane allocation (I-BCAP) in general seaport container terminals and propose the model predictive allocation (MPA) algorithm and preconditioning methods for solving the I-BCAP. First, we propose a dynamical modeling framework based on discrete-event systems (DESs), which describes the operation of a berthing process with multiple discrete berthing positions and multiple quay cranes. Second, based on the discrete-event model, we propose the MPA algorithm for solving the I-BCAP using the model predictive control (MPC) principle with a rolling event horizon. The validation and performance evaluation of the proposed modeling framework and allocation method are done using: 1) extensive Monte Carlo simulations with realistically generated datasets; 2) real dataset from a container terminal in Tanjung Priuk port, located in Jakarta, Indonesia; and 3) real life field experiment at the aforementioned container terminal. The numerical simulation results show that our proposed MPA algorithm can improve the efficiency of the process where the total handling and waiting cost is reduced by approximately 6%-9% in comparison with the commonly adapted method of first-come first-served (FCFS) (for the berthing process) combined with the density-based quay cranes allocation (DBQA) strategy. Moreover, the proposed method outperforms the state-of-the-art hybrid particle swarm optimization (HPSO)-based and genetic algorithm (GA)-based method proposed in the recent literature. The real life field experiment shows an improvement of about 6% in comparison with the existing allocation method used in the terminal.

In this paper, we present the design and implementation of a robust motion formation distributed control algorithm for a team of mobile robots. The primary task for the team is to form a geometric shape, which can be freely translated and rotated at the same time. This approach makes the robots to behave as a cohesive whole, which can be useful in tasks such as collaborative transportation. The robustness of the algorithm relies on the fact that each robot employs only local measurements from a laser sensor which does not need to be off-line calibrated. Furthermore, robots do not need to exchange any information with each other. Being free of sensor calibration and not requiring a communication channel helps the scaling of the overall system to a large number of robots. In addition, since the robots do not need any off-board localization system, but require only relative positions with respect to their neighbors, it can be aimed to have a full autonomous team that operates in environments where such localization systems are not available. The computational cost of the algorithm is inexpensive and the resources from a standard microcontroller will suffice. This fact makes the usage of our approach appealing as a support for other more demanding algorithms, e.g., processing images from onboard cameras. We validate the performance of the algorithm with a team of four mobile robots equipped with low-cost commercially available laser scanners.

Recently, it has been reported that inconsistent range-measurement or, equivalently, mismatches in prescribed interagent distances, may prevent popular gradient controllers from guiding rigid formations of mobile agents to converge to their desired shape and, even worse, from standing still at any location. In this paper, instead of treating mismatches as the source of poor performance, we take them as design parameters and show that by introducing such a pair of parameters per distance constraint, distributed controller achieving simultaneously both formation and motion control can be designed that not only encompasses the popular gradient control, but more importantly allows us to achieve constant collective translation, rotation, or their combination, while guaranteeing asymptotically that no distortion in the formation shape occurs. Such motion control results are then applied to 1) the alignment of formations' orientations and 2) enclosing and tracking a moving target. Besides rigorous mathematical proof, experiments using mobile robots are demonstrated to show the satisfying performances of the proposed formation-motion distributed controller.

Despite the great success of using gradient-based controllers to stabilize rigid formations of autonomous agents in the past years, surprising yet intriguing undesirable collective motions have been reported recently, when inconsistent measurements are used in the agents' local controllers. To make the existing gradient control robust against such measurement inconsistency, we exploit local estimators following the well-known internal model principle for robust output regulation control. The new estimator-based gradient control is still distributed in nature, and can be constructed systematically even when the number of agents in a rigid formation grows. We prove rigorously that the proposed control is able to guarantee exponential convergence, and then demonstrate through robotic experiments and computer simulations that the reported inconsistency-induced orbits of collective movements are effectively eliminated.

MOTIVATION: Genetic modifications or pharmaceutical interventions can influence multiple sites in metabolic pathways, and often these are 'distant' from the primary effect. In this regard, the ability to identify target and off-target effects of a specific compound or gene therapy is both a major challenge and critical in drug discovery. RESULTS: We applied Markov Chain Monte Carlo (MCMC) for parameter estimation and perturbation identification in the kinetic modeling of metabolic pathways. Variability in the steady-state measurements in cells taken from a population can be caused by differences in initial conditions within the population, by variation of parameters among individuals and by possible measurement noise. MCMC-based parameter estimation is proposed as a method to help in inferring parameter distributions, taking into account uncertainties in the initial conditions and in the measurement data. The inferred parameter distributions are then used to predict changes in the network via a simple classification method. The proposed technique is applied to analyze changes in the pathways of pyruvate metabolism of mutants of Lactococcus lactis, based on previously published experimental data. AVAILABILITY: MATLAB code used in the simulations is available from ftp://[email protected]/pub/Bioinformatics_BJ.zip