Cooperative Object Transportation Research Articles

Cooperative Object Transport by Two Robots Connected With a Ball-String-Ball Structure

Cooperative Object Transport by Two Robots Connected With a Ball-String-Ball Structure

Cooperative object transportation with differential-drive mobile robots: Control and experimentation

Non-prehensile cooperative object transportation is a challenging model problem for distributed control and organization methods but also has practical applications. Therefore, it is widely studied in distributed robotics research. This paper describes and evaluates a novel transportation scheme for differential-drive mobile robots that is, to the authors’ best knowledge, the most versatile scheme of its kind successfully evaluated with real-world hardware. The proposed scheme can conceptually deal with any number of robots and arbitrary polygonal objects, including non-convex ones, without having to retune, retrain, or reconfigure any of the control parameters between different scenarios. This is achieved by splitting the task into a formation control and a formation finding task, both of which are tackled with model-based approaches using distributed optimization. Formation control and formation finding are complicated by the robots’ non-holonomic kinematic constraints. Therefore, a tailored distributed model predictive controller is used for formation control. Finding formations relies on a multibody-dynamics representation of the robots-object system to properly account for contact and non-holonomic constraints. Due to these measures, the transportation scheme achieves a very satisfactory performance and dexterity in real-world hardware experiments utilizing network communication and distributed computation.

Force-based organization and control scheme for the non-prehensile cooperative transportation of objects

AbstractOver decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.

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.

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination, task allocation methods, experimental platforms, and simulators. However, most of recent multi-robot system studies focus on coordination and task allocation problems, with little focus on communications among multiple robots. In this review, we focus on the communication, validation platform, and simulator of multi-robot systems, and discuss one of the important applications, cooperative object transport. First, we study the multi-robot system fundamentals and comprehensively review the multi-robot system communication technologies. Then, the multi-robot system validating platform, testbed, simulator, and middleware used in academia and industry are investigated. Finally, we discuss recent advances in cooperative object transport, and challenges and possible future research directions for multi-robot systems.

Mutli-Robot Cooperative Object Transportation with Guaranteed Safety and Convergence in Planar Obstacle Cluttered Workspaces via Configuration Space Decomposition

In this work, we consider the autonomous object transportation problem employing a team of mobile manipulators within a compact planar workspace with obstacles. As the object is allowed to translate and rotate and each robot is equipped with a manipulator consisting of one or more moving links, the overall system (object and mobile manipulators) should adapt its shape in a flexible way so that it fulfills the transportation task with safety. To this end, we built a sequence of configuration space cells, each of which defines an allowable set of configurations of the object, as well as explicit intervals for each manipulator’s states. Furthermore, appropriately designed under- and over-approximations of the free configuration space are used in an innovative way to guide the configuration space’s exploration without loss of completeness. In addition, we coupled methodologies based on Reference Governors and Prescribed Performance Control with harmonic maps, in order to design a distributed control law for implementing the transitions specified by the high-level planner, which possesses guaranteed invariance and global convergence properties, thus avoiding the requirement for synchronized motion as inherently dictated by the majority of the related works. Furthermore, the proposed low-level control law does not require continuous information exchange between the robots, which rely only on measurements of the object’s configuration and their own states. Finally, a transportation scenario within a complex warehouse workspace demonstrates the proposed approach and verifies its efficiency.

A Predictive Control Approach for Cooperative Transportation by Multiple Underwater Vehicle Manipulator Systems

This article addresses the problem of cooperative object transportation for multiple underwater vehicle manipulator systems (UVMSs) in a constrained workspace involving static obstacles. We propose a nonlinear model predictive control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations, such as kinematic and representation singularities, obstacles within the workspace, joint limits, and control input saturation. More precisely, by exploiting the coupled dynamics between the robots and the object and using certain load sharing coefficients, we design a predictive controller for each UVMS in order to cooperatively transport the object within the workspace’s feasible region. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. In addition, the feedback relies on each UVMS’s onboard measurements and no explicit data are exchanged online among the robots, thus reducing the required communication bandwidth. Finally, realistic simulation results conducted in the UwSim dynamic simulator running in robot operating system (ROS) environment as well as real-time experiments employing two small UVMSs and demonstrated the effectiveness of the proposed control strategy.

Two-Layers Workspace: A New Approach to Cooperative Object Transportation With Obstacle Avoidance for Multi-Robot System

In this paper we present a new approach to cooperative overhead object transportation with obstacle avoidance for multi-robot systems. This approach considers situations in which there are obstacles that only pose a risk to the robots involved in the transport and not necessarily to the group of robots with the load. Thus, we propose to separate the workspace into two layers in a way that the obstacles which only pose risk of collision to the robots can be transposed by a local planner. This new approach to the load transportation problem allows the system to find shorter paths or even find a solution in situations which would not be possible with the classic approach. Furthermore, the developed method has a configuration space independent of the number of robots and it is robust to environmental variation, not reducing its performance in more complex environments as in narrow passages or long corridors. The proposed approach was tested by simulation and the results are compared to the classic one.

Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning.

This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques.

Cooperative Impedance Control for Multiple Underwater Vehicle Manipulator Systems Under Lean Communication

This article addresses the problem of cooperative object transportation for multiple underwater vehicle manipulator systems (UVMSs) in a constrained workspace with static obstacles, where the coordination relies solely on implicit communication arising from the physical interaction of the robots with the commonly grasped object. In this article, we propose a novel distributed leader-follower architecture, where the leading UVMS, which has knowledge of the object's desired trajectory, tries to achieve the desired tracking behavior via an impedance control law, navigating in this way, the overall formation toward the goal configuration while avoiding collisions with the obstacles. On the other hand, the following UVMSs estimate locally the object's desired trajectory via a novel prescribed performance estimation law and implement a similar impedance control law that achieves tracking of the desired trajectory despite the uncertainty and external disturbance in the object and the UVMS dynamics, respectively. The feedback relies on each UVMS's force/torque measurements and no explicit data is exchanged online among the robots, thus reducing the required communication bandwidth and increasing robustness. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. Finally, various simulation studies clarify the proposed method and verify its efficiency.

Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

Object transportation could be a challenging problem for a single robot due to the oversize and/or overweight issues. A multi-robot system can take the advantage of increased driving power and more flexible configuration to solve such a problem. However, an increased number of individuals also changed the dynamics of the system which makes control of a multi-robot system more complicated. Even worse, if the whole system is sitting on a centralized decision making unit, the data flow could be easily overloaded due to the upscaling of the system. In this research, we propose a decentralized control scheme on a multi-robot system with each individual equipped with a deep Q-network (DQN) controller to perform an oversized object transportation task. DQN is a deep reinforcement learning algorithm, thus does not require the knowledge of system dynamics, instead, it enables the robots to learn appropriate control strategies through trial-and-error style interactions within the task environment. Since analogous controllers are distributed on the individuals, the computational bottleneck is avoided systematically. We demonstrate such a system in a scenario of carrying an oversized rod through a doorway by a two-robot team. The presented multi-robot system learns abstract features of the task and cooperative behaviors are observed. The decentralized DQN-style controller is showing strong robustness against uncertainties. In addition, We propose a universal metric to assess the cooperation quantitatively.

Distributed Model Predictive Control for cooperative floating object transport with multi-vessel systems

Compared to an individual Autonomous Surface Vessel (ASV), greater efficiency and operational capability can be realized by a team of cooperative ASVs for certain operations, such as search and rescue, hydrographic survey and navigation assistance. This paper focuses on cooperative floating object transport, i.e., a group of ASVs coordinate their actions to transport floating objects. We propose a multi-layer distributed control structure for the object transport system. The object transport problem is formulated as the combination of several sub-problems: trajectory tracking of the object, control allocation, and formation tracking of the ASVs. The sub-problems are integrated by a nonlinear towline model that describe the transformation of forces considering the mass and elasticity of the towline. A controller based on Model Predictive Control (MPC) is designed to control the motion of each ASV. A negotiation framework based on the Alternating Direction of Multipliers Method (ADMM) is then proposed to achieve consensus among the ASVs. Numerical simulations of utilizing the proposed cooperative system to move a large vessel sailing inbound the Port of Rotterdam are carried out to show the effectiveness of our method. Besides transporting barges and off-shore platforms, the proposed cooperative object transport system could also be a solution to coordinate non-autonomous vessels and ASVs in future autonomous ports where both human-operated and autonomous vessels exist.

Control framework for cooperative object transportation by two humanoid robots

This paper aims at proposing a comprehensive control framework designed for cooperative transportation of a heavy load by two humanoid robots. First, a simplified dynamic model of the cooperative task is developed and the system stability is rigorously analyzed. Next, a centralized controller is formulated, this formulation provides an optimal control of the system by considering the robots dynamical stability while satisfying the robot–object–robot constraints. Finally, the controller is integrated with an arm controller and a local planner module forming a complete framework for cooperative transportation tasks. The approach is thoroughly analyzed and validated in simulation, and experiments are carried out on a team of two Nao humanoid robots transporting a range of objects placed on a small table. The experimental results pointed out the robustness of the approach as the robots successfully accomplished the transportation tasks in a stable way, moreover the transported objects masses were up to half the mass of one of the robot. Besides increasing the robot payload, some of the transported objects are relatively large and it is simply impossible for a single robot to transport them.

Collaborative Multi-Robot Transportation in Obstacle-Cluttered Environments via Implicit Communication.

This paper addresses the problem of cooperative object transportation in a constrained workspace involving static obstacles, with the coordination relying on implicit communication established via the commonly grasped object. In particular, we consider a decentralized leader-follower architecture for multiple mobile manipulators, where the leading robot, which has exclusive knowledge of both the object's desired configuration and the position of the obstacles in the workspace, tries to navigate the overall formation to the desired configuration while at the same time it avoids collisions with the obstacles. On the other hand, the followers estimate the object's desired trajectory profile via novel prescribed performance estimation laws that drive the estimation errors to an arbitrarily small predefined residual set. Moreover, a navigation function-based scheme is innovatively combined with adaptive control to deal with parametric uncertainty. Hence, the current state of the art in robust motion planning and collision avoidance is extended by studying second order non-linear dynamics with parametric uncertainty. Furthermore, the feedback relies exclusively on each robot's force/torque, position as well as velocity measurements and no explicit information is exchanged online among the robots, thus reducing the required communication bandwidth and increasing robustness. Finally, two simulation studies clarify the proposed methodology and verify its efficiency.

Cooperative Object Transport in Multi-Robot Systems: A Review of the State-of-the-Art.

In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increasethe visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport.

Cooperative object transportation using parallel line formation with a circular shift

SUMMARYThis paper presents a new cooperative object transportation technique using parallel line formation with a circular shift. Typical areas of research in relation to object transportation are grasping, pushing, and caging techniques, but these require precise grasping behaviors, iterative motion correction according to the object pose, and the real-time acquisition of the object shape, respectively. In this paper, the proposed technique does not need to consider the shape or the pose of objects, and equipped tools are not necessary for object transportation because objects are transported by pushing behavior only. Multiple robots create parallel line formation using a virtual electric dipole field and then push multiple objects into the formation. This parallel line is extended to the goal using cyclic motion by the robots and the objects are transported to the goal by pushing behavior. The above processes are decentralized and activated based on the finite state machine of each robot. Simulations and practical experiments are presented to verify the proposed technique.

Symmetry position/force hybrid control for cooperative object transportation using multiple humanoid robots

A symmetry position/force hybrid control framework for cooperative object transportation tasks with multiple humanoid robots is proposed in this paper. In a leader-follower type cooperation, follower robots plan their biped gaits based on the forces generated at their hands after a leader robot moves. Therefore, if the leader robot moves fast (rapidly pulls or pushes the carried object), some of the follower humanoid robots may lose their balance and fall down. The symmetry type cooperation discussed in this paper solves this problem because it enables all humanoid robots to move synchronously. The proposed framework is verified by dynamic simulations.

Cooperative Object Transportation Using Connected Robots

This paper presents cooperative object transportation using connected robots. Previous studies of object transportation mainly used pushing or grasping methods to manipulate objects. A pushing method, however, cannot control the object motion precisely and a grasping method requires complicated gripping action before the transportation. Therefore, we suggest a new object transportation method which uses a rope. The method consists of three phases according to the behavior: the approaching, enclosing and transportation phases. Robots and an object have different formation controllers in accordance with their phases. Real experiments are presented to prove the validity of the proposed method.

2A2-R03 ポテンシャルフィールド法による全方向移動ロボットの協調搬送制御(車輪型/クローラ型移動ロボット(2))

In cooperative object transportation by mobile robots, complex motions of robots are required. Therefore omnidirectional mobile robots are suitable for the cooperative transportation. And they need high positioning accuracy. The same can be said of potential method for cooperative object transportation. But, most of existing omnidirectional wheels have complex mechanism, and are not practical. Generally, they have low self localization accuracy using internal sensors. Because it is hard to measure a movement of free rollers. On the other hand, an active-caster wheel feature simple mechanism and high positioning accuracy, and I enable the robots to improve self localization accuracy. In this paper, I propose the cooperative system using self localization with active-caster wheeled robots.