Cooperative Transport Systems Research Articles

Adaptive control scheme for cooperative transport systems navigation under uncertainty

An adaptive robust control scheme for a cooperative transport system is proposed to tackle the challenges arising from parameter uncertainty, external interference, measurement errors, and other factors. The cooperative transport system consists of a leader automated ground vehicle, a baggage carrier, and a follower automated ground vehicle. Firstly, a mechanics-based independent dynamic model without constraints is established for each component within the system, and the coupling relationship between components is analyzed to design the constraint function. Secondly, to address inaccuracies in initial conditions, the trajectory errors in both their zero- and first-order forms are introduced. A closed-form dynamic model that is subject to constraints is then developed for the entire cooperative system, incorporating both structural and performance constraints. Thirdly, an innovative adaptive robust control scheme is introduced for mechanical systems facing uncertainty. An adaptive law is devised to estimate the bounds of uncertainty. The control is deterministic and can be mathematically expressed in a closed-form. Fourthly, a constrained optimization problem is formulated using the fuzzy information of uncertainty to choose an appropriate optimal gain kopt of the adaptive law. This is achieved by minimizing the combination of average fuzzy system performance and control effort. Finally, numerical simulations are conducted to verify the effectiveness and adaptability of the proposed control method. The performance of controlled cooperative transport system is both deterministically guaranteed and fuzzily optimized.

A decoupled solution to heterogeneous multi-formation planning and coordination for object transportation

Multi-robot formations have numerous applications, such as cooperative object transportation in intelligent warehouses. In this context, robots are tasked with delivering objects in formation while avoiding intra- and inter-formation collisions. This necessitates the development of solutions for multi-robot task allocation, formation generation, rigid formation route planning, and formation coordination. In this paper, we present a cooperative formation object transportation system for heterogeneous multi-robot systems which captures robot dynamics and avoids inter-formation collisions. Accounting for heterogeneous formations expands the applicability of the proposed robotic transport system. For formation generation, we propose an approach based on conflict-based search, which integrates high-level path planning with low-level trajectory optimisation. For heterogeneous formation planning, we present a two-stage iterative trajectory optimisation framework which adheres to the kinematic constraints of our heterogeneous multi-robot system while retaining formation rigidity. For multi-formation coordination, we use a loosely-coupled algorithm which can guarantee collision-free and deadlock-free formation navigation under minimal assumptions. We demonstrate the efficacy of our approach in simulation.

Distributed Collaborative Control of Multi-Vehicle Autonomous Cooperative Transportation Systems: A Hierarchical Constraint-Following Approach

Distributed Collaborative Control of Multi-Vehicle Autonomous Cooperative Transportation Systems: A Hierarchical Constraint-Following Approach

Accuracy and Safety: Tracking Control of Heavy-Duty Cooperative Transportation Systems Using Constraint-Following Method

Accuracy and Safety: Tracking Control of Heavy-Duty Cooperative Transportation Systems Using Constraint-Following Method

Decentralized adaptive controller for multi-drone cooperative transport with offset and moving center of gravity

Multi-drone cooperative transport has been extensively studied in literature. However, few issues are still required to be addressed to make such systems technologically sound. One such issue is that of control of such systems when the center of gravity of the object being transported is unknown and/or varies with time. Few examples in which the center of gravity is either offset (does not coincide with the geometric center of the object) or moves continuously with time include package delivery, pesticide spraying, and aerial car. This paper presents a decentralized adaptive control approach for a multi-drone cooperative transport system where the center of gravity of the system is offset and/or moving. This paper proposes a fuzzy logic based force-torque feedback controller along with two novel adaptive control laws to estimate the payload's center of gravity and/or its velocity. Simulation results validate the effectiveness of the proposed control technique as compared other methods in literature.

Robust Cooperative Transport System with Model Error Compensator Using Multiple Robots with Suction Cups

In cooperative transport systems, multiple robots work together to transport objects that are difficult to transport with a single robot. In recent years, multi-robot systems that cooperate to transport objects have been researched. However, during the transfer of objects, misalignment occurs between the ideal and actual grasp positions. In an automatic transport system, a grasping error can cause an error in the trajectory of the object, significantly reducing the transport efficiency. In this paper, a control system that allows robust cooperative transport control using a model error compensator is proposed for a leader–follower system in which the transported object is the virtual leader and the followers are ideally arranged. This system adds robustness to the operation of a conventional cooperative transport system by using the ideal formation of robots. The effectiveness of the proposed method was evaluated through cooperative transport experiments using two ideal formations for passing through a narrow entrance. The cooperative transport system could not pass through the narrow entrance using the conventional method; however, the system using the compensator passed through the narrow entrance smoothly.

Enhancing the terrain adaptability of a multirobot cooperative transportation system via novel connectors and optimized cooperative strategies

Enhancing the terrain adaptability of a multirobot cooperative transportation system via novel connectors and optimized cooperative strategies

Effect of Robotic Pile-Up Mechanism on Cooperative Transportation for Versatile Objects

In recent years, cooperative transportation systems using multiple mobile robots have been studied. The advantage will be to transport objects that cannot be dealt with by a single robot and transport them by using smaller robots. Although cooperative transportation by a group of robots has been studied, the conventional transportation targets are limited to objects whose posture is stable. In this paper, we propose a system in which robots pile up on each other to support an object, aiming at a system for more versatile object transportation, including unstable objects. After deriving the conditions by modeling the support system in object transportation, we verify the transporting performance including the robotic pile-up effect through actual robot experiments.

Decentralized Control and Obstacle Avoidance in Autonomous Cooperative Transport System

Autonomous cooperative transport has been a receptive research hotspot for decades. The main concern is to find the effective coordination among autonomous agents to perform the task in order to achieve overall performance accuracy. In this paper, a multi-robot system performing a payload manipulation task where the participating robots do not have any knowledge regarding other robots in the system or the payload dynamics is considered. A decentralized proportional-derivative control strategy by which the multi-robot system can organize itself and adapt dynamically to changing circumstances is proposed. The decentralized controller is augmented with an orientation control by which the payload in transport attains a desired angular orientation at the target location. An obstacle avoidance strategy using an artificial potential field method is incorporated to avoid any obstacle in its transport path. The transport systemis simulated with uniform and non-uniform distributions of robots around the payload, for transportation as well as obstacle avoidance scenarios.

Path planning for dual UAVs cooperative suspension transport based on artificial potential field-A* algorithm

Unmanned Aerial Vehicles (UAVs) are frequently utilized for transferring and delivering. Dual- or multi-quadrotor cooperative transportation schemes are required for heavy loads. In this work, focusing on the path planning for a dual-quadrotor cooperative transport system, a unique artificial potential field-A*(APF-A*) algorithm is studied to fulfill the safe and effective cooperative suspension transport. APF-A* algorithm proposed the strategy of host-wingman cooperative transportation based on the APF theory, and optimizes the heuristic function by introducing the concept of repulsive force to maintain the quadrotor flying at a safe distance from the obstacle. In addition, APF-A* algorithm promises to increase search efficiency while identifying the optimal path, and it can reduce the swing angle motion of loads to enhance the security of transportation. The coordinated flight control and path planning of quadrotor UAVs are simulated in AirSim environment and tested in flight experiments. The results demonstrate that the algorithm is capable to plan safe and reliable paths, meanwhile changing formation and avoid collision when the wingman is close to obstacles. In comparison to the regular A* algorithm, the APF-A* algorithm proposed in the paper performs better in path planning for dual-quadrotor cooperative transport system, reducing the search time and the number of search grids by nearly 60% and 53.75%, respectively.

Definition of Forensic Methodologies for Autonomous Vehicles

Digitisation is present more and more widely in the vehicle industry, the use of modern and increasingly self-driving vehicles is appearing more and more widely. Their use becomes more appropriate and necessary in industry, transport, military and logistics applications. The spread of these highly automated vehicles will have a direct impact on road accidents, violations and crimes. In the event of accidents, the determination of responsibility and the identification of the perpetrators of crimes will continue to be necessary in the future, for which tools and procedures are currently not available or not fully applicable. By connecting vehicles and infrastructure elements, the range of data related to events has been greatly enhanced. This information must be extracted and analysed during the investigations. The purpose of the investigations is to determine what, when, where and how it happened, who were involved, and to determine responsibility. The basis of the expert investigation of self-driving vehicles and cooperative transport systems is the classical forensic procedure and digital forensics. On the other hand, the field requires specific knowledge, preparation, specific tools and approach. In this study, the existing, already used digital forensics examination methods are described,the applicability of some of them, some steps or tools that require further examination in the case of modern and increasingly autonomous vehicles.

Design and control of a multi-mobile-robot cooperative transport system based on a novel six degree-of-freedom connector

Multi-robot cooperative object transport on uneven roads is challenging. The key barrier is dealing with nonholonomic and rigid-formation motion constraints. In this study, to alleviate the influence of these constraints on a multi-robot cooperative transport system (MRCTS), a six degree-of-freedom connector capable of sensing three-axial displacements, three-axial forces, and three-axial angular displacements is designed and employed. Based on the local displacements derived from each connector, we develop a position calibration method to calculate the relative position of each robot and achieve a centralized control strategy. Based on the forces sensed by each connector, we design a decentralized control strategy to accomplish cooperative transport in which a leader robot guides the follower robots toward a destination by applying forces, instead of centralized information broadcasting. The experimental results show that the MRCTS works well on an uneven surface, and the tracking errors are within the design stroke of the connectors, demonstrating the effectiveness of the design and control methods of the MRCTS.

Passengers’ Satisfaction towards Service Quality with Public Transportation in Kathmandu Valley

The operation of public transportation is a public service. Nepal's only cooperative public transportation system is Sajha Yatayat. Additionally, many public transportation options are operated by private companies. Kathmandu is the capital of Nepal; hence the population and public transportation are under significant strain. Therefore, this location was chosen for the research on passengers' satisfaction with the service quality of public transportation. The survey issued 80 questionnaires to riders of public transportation. 52 surveys were returned. Forty of the samples were valid for analysis. 26 passengers (65%) said that evening and nighttime access to public transportation was extremely limited. In addition, 16 (40%) of respondents felt uneasy riding in public transportation. Similarly, 27 (67.5%) respondents noted poor hygiene, and 22 (55%), dangerous public transportation. However, the majority of passengers are dissatisfied with the accessibility, comfort, cleanliness, and safety of public transportation. Only the transportation fare provides satisfaction to passengers. In order to increase passenger happiness, the relevant authority should be accountable for enhancing the service quality of public transportation.

Motor-Efficiency Estimation and Control of Multirotors Comprising a Cooperative Transportation System

A cooperative transportation system (CTS) composed of two multirotors has been developed to facilitate the cooperative transportation of a payload, even in the presence of defective motors. The reduced thrust generated by the faulty motors may cause greater tracking errors, making it challenging to control and estimate the system. Moreover, the reduced thrust can also act as a disturbance to the other UAV, further exacerbating the problem. To address these issues, the motor thrust is reconfigured using an efficiency estimator based on an unscented Kalman filter (UKF). This estimator increases the thrust of the defective motors, thus improving the system’s performance. The CTS achieves trajectory tracking despite the defective motors by using a geometric controller that incorporates the motor-efficiency estimator. A stability analysis has been conducted to prove the stability of the tracking errors. The estimation and control performances have been verified through simulations.

A Novel Neural Network-Based Robust Adaptive Formation Control for Cooperative Transport of a Payload Using Two Underactuated Quadcopters

Designing a controller for the cooperative transport of a payload using quadcopter-type unmanned aerial vehicles (UAVs) is a very challenging task in control theory because these vehicles are underactuated mechanical systems. This paper presents a novel robust adaptive formation control design for the cooperative transport of a suspended payload by ropes using two underactuated quadcopters in the presence of external disturbances and parametric uncertainties. The structure of the proposed controller is divided into two subsystems: fully actuated and underactuated. An integral sliding mode adaptive control strategy is proposed for the fully actuated subsystem, and for the underactuated subsystem, an adaptive control strategy based on the combination of Backstepping and sliding mode is proposed. Then, the control parameters of the sliding surfaces of both control subsystems are adaptively tuned by a neural network. In addition, to improve the robustness of the proposed controller, a disturbance observer is incorporated to estimate and compensate the lumped disturbances. The asymptotic stability of the cooperative transport system is verified with the Lyapunov theorem. Finally, numerical simulations are performed in MATLAB/Simulink environment, and the results show that the proposed controller successfully transports the payload safely and without oscillations. Moreover, the desired formation pattern is maintained throughout the flight task, even with external disturbances and parametric uncertainties.

Co-TS: Design and Implementation of a 2-UAV Cooperative Transportation System

UAV application research has attracted more and more attention of researchers. However, in the field of UAV transportation, especially in the case of multi-UAV cooperation, there is little research or design on the overall system of multi-UAV cooperative transportation due to the complexity of the task and many factors need to be considered. In this paper, we first introduce a 2-UAV cooperative transportation task scenario and we call it Co-TS. Then according to the scenario, we design a task planning system, which describes the entire process of 2-UAV performing transportation tasks; in order to solve the design complexity of this UAV upper-layer software, we propose a model architecture of 2-UAV transportation application, design the functions of each module and analyze the relationship between modules in the model architecture; in addition, to realize the correct execution of the 2-UAV cooperative transportation task, the task execution is divided into four states which include preparation, rising, forwarding, and landing states, and the corresponding cooperative transportation control flow algorithm is designed for four states. Finally, we develop this system and conduct experiments. The quantitative and qualitative results demonstrate the effectiveness of our method.

Design and Experiment for Inter-Vehicle Communication Based on Dead-Reckoning and Delay Compensation in a Cooperative Harvester and Transport System

To achieve high-frequency and effective inter-vehicle communication between harvesters and transport vehicles during cooperative harvesting, a protocol for wireless communication was designed by analyzing actual communication requirements. Two different wireless communication modes (radio and 4G) were selected for the hardware design; then, a Kalman Filter was designed based on real-time Dead-reckoning and inter-vehicle Communication data after delay Compensation (KFDCC). Finally, the relative longitudinal deviation between two vehicles was obtained and updated steadily at a 10 Hz frequency. By using the relative longitudinal deviation of two vehicles, calculated after aligning the UTC stamp with the local GNSS data from the harvester and transport vehicle as a comparative metric, accuracy evaluation experiments were conducted regarding radio and 4G. The maximum absolute errors of the KFDCC output value were 0.03783 and 0.07381 m, respectively, and the mean square errors were 0.00392 and 0.01317 m, respectively. Compared with systems without the KFDCC method, the mean square errors were reduced by 88.76% and 90.60%, respectively. The KFDCC method can also effectively solve the problems of data delay, packet loss, blockage, error, and so on, in wireless communication, and has short-time breakpoint endurance capabilities. Field experiments showed that the proposed method can provide accurate data support for the dynamic alignment and unloading processes of harvesters and transport vehicles, and it can also provide algorithmic support for real-time communication data fusion between different wireless communication modes. Overall, the inter-vehicle communication mode and data-processing method designed in this paper have good effects and adaptability, and they can guarantee that the whole process of autonomous harvesting operates properly.

Assist control for human‐robot cooperative transportation systems using nonlinear disturbance observer

AbstractIn this paper, we investigate an assist control for a system in which a human and a robot cooperate to transport an object toward a designated position and angle. We consider a situation where the force due to the human to the object cannot be measured directly. In order to tackle this situation, an assist control with a nonlinear disturbance observer is proposed. By using the observer, we estimate the control input due to the human from position and angle of the object. Moreover, we design the control input by the robot to assist the human input based on the estimates by the disturbance observer. The effectiveness of the proposed method is discussed by numerical simulations for the minimum jerk model.

Assist Control for Human-Robot Cooperative Transportation Systems Using Nonlinear Disturbance Observer

Assist Control for Human-Robot Cooperative Transportation Systems Using Nonlinear Disturbance Observer

The Design and the Development of a Biped Robot Cooperation System

The aim of this paper is to design a fuzzy motion control algorithm for a developed monocular vision system based on a cooperative transportation system of two humanoid robots. The control strategies of the cooperation transportation system contain three stages, including object searching, walking toward the transported object, and cooperatively moving the transported object. To have different moving speeds, the gait step size was pre-planned as two different modes, i.e., one of the gaits is selected to let the HR have large variations of motion and another gait is to make the HR with small variations. The fuzzy motion control algorithm is utilized to select the appropriate mode of gait. Both humanoid robots can actively search and move to the front of the target object, then cooperatively lift the target and carry it to the platform. The task of synchronous movement is controlled with fuzzy techniques through the control terminal. From the experimental results, it can be seen that both robots can distinguish the orientation of the target, move to the appropriate position, and then successfully raise the target together.