Cooperative Transportation Task Research Articles

Evolutionary Design of Cooperative Transport Behavior for a Heterogeneous Robotic Swarm

Swarm robotics system (SRS) is a type of artifact that employs multiple robots to work together in a coordinated way, inspired by the self-organizing behavior of social insects such as ants and bees. SRSs are known for their robustness, flexibility, and scalability. This study focuses on evolutionary robotics (ER) which uses artificial neural networks (ANNs) as controllers to operate autonomous robots. In traditional ER research, SRSs were often composed of teams of homogeneous robots, each of which is controlled by a single ANN. In contrast, this study focuses on the implementation of ER in a heterogeneous SRS. To evaluate our approach, we present the concept of employing multiple controllers for sub-teams in a swarm. Heterogeneity was achieved using different controllers for the same physical bodies. We simulated a cooperative transport task, in which the performance of heterogeneity was superior because the two ANN controllers were able to express a variety of behaviors as an entire swarm. Additionally, this study investigated how well the three types of parental selection methods of the heterogeneous approach, can help to optimize the performance of the swarm.

Intelligent Path Planning for AGV-UAV Transportation in 6G Smart Warehouse

Recently, deep reinforcement learning (DRL) has attracted increasing interest in the field of intelligent navigation and path planning in smart warehousing. The latest imitation augmented DRL (IADRL) model has achieved good performance for the cooperative transportation tasks of automatic guided vehicles (AGVs) and unmanned aerial vehicles (UAVs). However, this model cannot always transport target cargoes with the optimized policy due to premature convergence. Therefore, we propose an intelligent path planning model for AGV-UAV transportation in this paper. The proposed model utilizes the proximal policy optimization with covariance matrix adaptation (PPO-CMA) in the imitation learning and DRL networks, which enables the AGV-UAV coalition to plan the optimal transportation route at a lower cost. Experiments conducted in simulation warehousing scenarios demonstrated the proposed model and improved the accumulated training reward by more than 10%, outperforming the existing state-of-the-art models in terms of effectiveness and efficiency.

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.

Cooperative Transportation With Mobile Manipulator: A Capability Map-Based Framework for Physical Human–Robot Collaboration

In the cooperative transportation task with a mobile manipulator (MM), the mobile robot and the manipulator must move simultaneously to adapt to the human motion. In addition, the motion of the MM is underconstrained due to redundancy, which makes MM real-time motion planning challenging. In this article, a capability map-based framework was proposed to enable real-time motion planning of the MM. In the motion planner, the dexterity of the MM, the formation of the human–robot system, and obstacle avoidance of the mobile robot are considered to get safe and human-like robot motion. Moreover, to make optimization faster and avoid local minimum, capability map, which represents the manipulability distribution of the MM in its workspace, is queried to determine the seed of the optimization algorithm. The proposed motion planner is self-contained and can be extended to the transportation task with multiple MMs. The effectiveness of this proposed framework is validated both in simulations and real-world experiments.

Decentralised aerial swarm for adaptive and energy efficient transport of unknown loads

• We present a behaviour based subsumption architecture that enables a swarm’s topology to adapt to a load's mass distribution. • The decentralized self-organized swarm can perform a cooperative load transportation task without any explicit communication between members. • Our architecture has the potential to be scalable to a large number of agents. • This is significant in aerial swarm cooperative transport systems because it leads to even energy distribution among swarm members when compared to a uniform swarm topology. • It also fosters robustness of the load-suspended aerial robot system. Cooperative transport by a swarm of Quadcopters offers more flexibility and performance when carrying loads that are complex in structural profile and mass. Ensuring that team members of the swarm are optimally placed on these loads as well as able to resist disturbances from the environment during transport are current research challenges. In this paper, we present a decentralized behaviour based subsumption architecture for enabling a swarm of Quadcopters to explore an unfamiliar area, find a load and transport it to a target location cooperatively. In the architecture, three behaviours were used: an obstacle avoidance behaviour to avoid collisions with objects in the environment, a flocking behaviour to ensure swarm structure and a bacterium behaviour for exploration of the environment and to adapt to the mass profile of various detected loads. By adapting to the mass profile of a detected load, we show that our architecture ensures even energy distribution among Quadcopters while achieving robustness to disturbances from the environment. Our results show that a mass adapting swarm is able to conserve energy during payload transportation when compared to a swarm that does not adapt to a load’s profile. Furthermore, we do not use explicit communication between team members but instead rely on data from visual sensors attached to the Quadcopters. We experiment with simulations in a physics informed robot simulator called CoppeliaSim and demonstrate the effectiveness of our architecture when utilized for cooperative transport of irregular loads.

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.

Optimization-Driven Control and Organization of a Robot Swarm for Cooperative Transportation

The cooperation of autonomous robotic systems, forming a robotic network by communicating with each other, brings about the potential to improve both performance and robustness in robotics applications, but introduces additional complexity in control design. In this context, this paper examines a cooperative transportation task that can serve as a test bed for distributed control algorithms, but may also inspire applications in logistics. In the proposed approach, the task solution is split into an organization scheme, determining formations useful for the current situation, as well as a distributed controller maintaining the formation. Both of these rely on the solution of optimization problems, with the latter drawing from distributed model predictive control theory. The optimization problems are formulated in a way that they can be derived automatically from the intrinsic properties of the considered scenario, e.g. the number of participating robots and the shape of the transported object. Therefore, no human intervention or scenario-specific design steps are necessary while still being able to cater to a wide range of scenarios. Challenging simulation scenarios illustrate the performance achievable with the proposed scheme.

Cooperative transport tasks with robots using adaptive non-conventional sliding mode control

This work presents a hybrid position/force control of robots aimed at handling applications using multi-task and sliding mode ideas. The proposed robot control is based on a novel adaptive non-conventional sliding mode control used to robustly satisfy a set of inequality constraints defined to accomplish the cooperative transport task. In particular, these constraints are used to guarantee the reference parameters imposed by the task (e.g., keeping the load at a desired orientation) and to guide the robot using the human operator’s forces detected by a force sensor located at the robot tool. Another feature of the proposal is the multi-layered nature of the strategy, where a set of four tasks are defined with different priorities. The effectiveness of the proposed adaptive non-conventional sliding mode control is illustrated by simulation results. Furthermore, the applicability and feasibility of the proposed robot control for transport tasks are substantiated by experimental results using a redundant 7R manipulator.

Efficient Planar Caging Test Using Space Mapping

This paper presents an efficient algorithm to test whether a planar object can be caged by a formation of point agents (point fingertips or point mobile robots). The algorithm is based on a space mapping between the 2-D work space ( $\mathcal {W}$ space) and the 3-D configuration space ( $\mathcal {C}$ space) of the given agent formation. When performing caging test on a planar object, the algorithm looks up the space mapping to recover the $\mathcal {C}$ space of the given agent formation, labels the recovered $\mathcal {C}$ space, and counts the number of labeled surfaces to judge the success of caging. The algorithm is able to work with various planar shapes, including objects with convex boundaries, concave boundaries, or holes. It can also respond quickly to varying agent formations and different object shapes. Experiments and analysis on different objects and fingertip formations demonstrate the completeness, robustness, and efficiency of our proposal. Note to Practitioners —This paper proposes an algorithm to solve a geometric problem—find whether a given formation of planar points can constrain (or cage) a planar shape. Users can use the proposed algorithm to actuate a formation of robotic fingertips to perform caging-based grasping tasks or use the proposed algorithm to actuate a formation of mobile robots to perform cooperative transportation tasks. The algorithm inherits the merits of caging and helps users to avoid explicit force analysis. It offers robustness to avoid uncertainty in the tasks. The code of our work is in the supplementary material.

Effect of mechanical interaction on brain activations during human-human cooperative transportation task

Effect of mechanical interaction on brain activations during human-human cooperative transportation task

Manipulation and Transportation With Cooperative Underwater Vehicle Manipulator Systems

Autonomous underwater manipulation has been a topic of interest since the early 1990s. In the past few years, several milestone projects such as SAUVIM and TRIDENT have demonstrated autonomy capabilities for a single underwater vehicle manipulator system (UVMS) in performing simple manipulation tasks, e.g., the recovery of an object from the seafloor. The Italian funded MARIS project aims to extend some of these results to multiple UVMSs performing a cooperative transportation task of a long object such as a pipe. This paper presents the results achieved in developing a unifying architecture for the control of both individually and cooperatively operating UVMSs which explicitly makes use of a limited amount of information exchange between the agents, which is needed due to the severe bandwidth limitations of the underwater acoustic communications. A complete execution of the reference transportation mission is presented to support the proposed distributed algorithm. Furthermore, hydrodynamic simulations of the cooperative transportation phase are presented and an analysis of the achievable performances as different communication schemes are employed is given.

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.

2P1-M08 人どうしの協調運搬作業においてリーダ・フォロワ間の相互作用が高次脳機能へ及ぼす影響

2P1-M08 人どうしの協調運搬作業においてリーダ・フォロワ間の相互作用が高次脳機能へ及ぼす影響

INT-12 COOPERATIVE TRANSPORTATION Task by Multi-ROBOTS WITH LEADER SELECTION(Intelligent Machines IV,Technical Program of Oral Presentations)

INT-12 COOPERATIVE TRANSPORTATION Task by Multi-ROBOTS WITH LEADER SELECTION(Intelligent Machines IV,Technical Program of Oral Presentations)

平面を移動する三次元ヘビ型ロボットの協調制御

This paper proposes a controller for the cooperative task of 3-dimensional (3D) snake robots which have wheeled links with no side slip. We assume that the snake robot moves on a flat surface. First, we consider features of the kinematic redundancy caused by introducing wheelless links and derive a dynamic model of a 3D snake robot with introducing the shape controllable points for parameterizing its kinematic redundancy. Next, we extend it to the model for the cooperative transportation task of the robots and propose a controller which accomplishes this task by controlling internal forces and utilizing the redundancy. Simulation results show the validity of the proposed controller.

Development of an artificial fish-like robot and its application in cooperative transportation

The design of an artificial fish-like robot and its application in cooperative transportation is described in this paper. Although there have been plenty of results obtained in multi-robot cooperation, they are seldom concerned with underwater applications. In this paper, a fish-like robot is designed from the perspective of biomimetics and then, using multiple fish robots, a cooperative transportation task is designed and implemented based on a situated action selection approach. The experimental trials are presented to verify the effectiveness of the proposed method.

A machine-learning approach to multi-robot coordination

This paper presents a machine-learning approach to the multi-robot coordination problem in an unknown dynamic environment. A multi-robot object transportation task is employed as the platform to assess and validate this approach. Specifically, a flexible two-layer multi-agent architecture is developed to implement multi-robot coordination. In this architecture, four software agents form a high-level coordination subsystem while two heterogeneous robots constitute the low-level control subsystem. Two types of machine learning—reinforcement learning (RL) and genetic algorithms (GAs)—are integrated to make decisions when the robots cooperatively transport an object to a goal location while avoiding obstacles. A probabilistic arbitrator is used to determine the winning output between the RL and GA algorithms. In particular, a modified RL algorithm called the sequential Q-learning algorithm is developed to deal with the issues of behavior conflict that arise in multi-robot cooperative transportation tasks. The learning-based high-level coordination subsystem sends commands to the low-level control subsystem, which is implemented with a hybrid force/position control scheme. Simulation and experimental results are presented to demonstrate the effectiveness and adaptivity of the developed approach.

Control of A Cooperative Transportation System with Two Car-Like Mobile Robots Based on Chained Form (Experimental Verification of Its Open Loop Control)

We have developed a cooperative transportation system with two tricycle mobile robots which are kinematically equivalent to car-like mobile robots, and we have verified the validity of an open loop control method based on chained form for the cooperative transportation system using the developed experimental apparatus. A mechanical feature of the developed tricycle mobile robot is that it is capable of controlling its single-steering wheel angle at high precision since it steers the single-steering wheel by a zero backlash spur geared motor. Another mechanical feature of the tricycle mobile robot is that it has a motor with a differential capable of driving its two rear wheels without slipping them, i.e., without spinning or dragging one of the rear wheels, when cornering. These features mean that the motion of the tricycle mobile robot is described by its kinematical equation precisely, and the controlling accuracy of the motion of the tricycle mobile robot is high enough to examine the validity of the open loop control method experimentally. We have performed a cooperative transportation task in which the two tricycle mobile robots trnsport a carrier cooperatively without changing the carrier orientation. Such task cannot be performed by a traditional single-trailer system. The validity of the open loop control method is verified by the experimental examination.

Cooperation through self-assembly in multi-robot systems

This article illustrates the methods and results of two sets of experiments in which a group of mobile robots, called s-bots , are required to physically connect to each other, that is, to self-assemble, to cope with environmental conditions that prevent them from carrying out their task individually. The first set of experiments is a pioneering study on the utility of self-assembling robots to address relatively complex scenarios, such as cooperative object transport. The results of our work suggest that the s-bots possess hardware characteristics which facilitate the design of control mechanisms for autonomous self-assembly. The control architecture we developed proved particularly successful in guiding the robots engaged in the cooperative transport task. However, the results also showed that some features of the robots' controllers had a disruptive effect on their performances. The second set of experiments is an attempt to enhance the adaptiveness of our multi-robot system. In particular, we aim to synthesise an integrated (i.e., not-modular) decision-making mechanism which allows the s-bot to autonomously decide whether or not environmental contingencies require self-assembly. The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly.

Cooperative transportation system for humanoid robots using simulation-based learning

In this paper, an approach to the behavior acquisition required for humanoid robots to carry out a cooperative transportation task is proposed. In the case of object transportation involving two humanoid robots, mutual position shifts may occur due to the body swinging of the robots. Therefore, it is necessary to correct the position in real-time. Developing the position shift correction system requires a great deal of effort. Solution to the problem of learning the required behaviors is obtained by using the Classifier System and Q-Learning. The successful cooperation of two HOAP-1 humanoid robots in the transportation task has been confirmed by several experimental results.