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

Given limited terrain adaptability,most existing multirobot cooperative transportation systems(MRCTSs)mainly work on flat pavements,restricting their outdoor applications.The connectors'finite deformation capability and the control strategies'limitations are primarily responsible for this phenomenon.This study proposes a novel MRCTS based on tracked mobile robots(TMRs)to improve terrain adaptability and expand the application scenarios of MRCTSs.In structure design,we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object(the communal payload).In addition,the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control.In the control strategy,we present a virtual leader-physical follower collaborative paradigm.The leader robot is imaginary to describe the movement of the entire system and manage the follower robots.All the TMRs in the system act as follower robots to transport the object cooperatively.Having divided the whole control structure into the leader robot level and the follower robot level,we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework.Furthermore,a control law satisfying saturation constraints is derived to ensure transportation stability.An adaptive control algorithm processes the wheelbase uncertainty of the TMR.Finally,we develop a prototype of the TMR-based MRCTS for experiments.In the trajectory tracking experiment,the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time.In the outdoor experiment,the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR's maximum load limited to 15 kg.The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS.

Modeling and control for cooperative transport of a slung fluid container using quadrotors

In this paper, dynamic modeling and control problem for transfer of a sloshing liquid container suspended through rigid massless links from a team of quadrotors are investigated. By the proposed solution, pose of the slung container and fluid sloshing modes are stabilized appropriately. Dynamics of the container-liquid-quadrotors system is modeled by Euler-Lagrange method.Fluid slosh dynamics is included using multi-mass-spring model. According to derived model, a proper control law is designed for a system with three or more quadrotors. Implementing the proposed control law, quadrotors can control pose of the container, directions of the links and liquid sloshing modes simultaneously. Stability of closed loop system of tracking errors and sloshing modes are demonstrated using a theory of singularly perturbed systems and Lyapunov stability theorem. Also, the capability of the proposed feedback control laws in solving a formerly organized transport problem of a liquid filled container has been demonstrated in simulations. Moreover, priority of the proposed control scheme to an existing slung load controller in the literature is demonstrated.

Cooperative transport strategy for formation control of multiple mobile robots

This paper addresses a box transport problem that requires the cooperation of multiple mobile robots. A geometric-based distributed formation control strategy is proposed for robots to push the box to the target, which might be static or dynamic. Velocity and hardware constraints are considered in the advanced planning of the trajectory. Information sharing is included because the robots used as box pushers cannot acquire the required environmental information from their local sensors. Simulation results show the effectiveness of the proposed distributed cooperation strategy.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

ComplexTrans-Global Rail-Road Transportation System Economical and Clustered Individual and Individualised Public Transport also Prevents the Spread of Covid 19

Land transport can no longer meet the requirements.European transport can be described by these words−crowded motorways and cities,dangerous emissions,ubiquitous traffic accidents,delays,expensive railways.Solutions are being sought to transfer a large part of passengers and especially freight transport to(high-speed)rail,and efforts are moving towards electromobility,car-sharing,5G-connectivity,autonomous driving,MaaS(Mobility as a Service)-coordinated transport or hyperloop-type solutions.However,all these solutions have additional challenges and limitations.Solutions are not being searched where they really exist-in the mutual adaptation of road and rail vehicles and their deep cooperation.The ComplexTrans project shows that simply adapting the dimensions and functions of road and rail vehicles can eliminate(or at least significantly reduce)all the problems of existing land transport.The main features of the ComplexTrans system are sufficient parking spaces,reduction of urban and non-urban congestion,electric vehicles with unlimited range and cheaper than standard cars,cheaper and more accessible battery charging,“autonomous ride”,solving the overlap between passenger and freight rail transport and making it self-financing,transferring intercity freight transport to rail,replacing part of continental air transport and many others.The cost-effective and clustered individual transport and individualised public transport of the ComplexTrans system also bring very significant reductions in the risk of transmission of covid-19 and other contagious diseases during transport.