Cooperation Of Multiple Robots Research Articles

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.

A Multirobot System for 3-D Surface Reconstruction With Centralized and Distributed Architectures

In this article, we propose an original solution to the problem of surface reconstruction of large-scale unknown environments, with multiple cooperative robots. As they progress through the 3-D environment, the robots rely on volumetric maps obtained via a TSDF representation to extract discrete incomplete surface elements (ISEs), and a list of candidate viewpoints is generated to cover them. A next-best-view planning approach, which approximately solves a traveling salesman problem (TSP) via greedy allocation, is then used to iteratively assign these viewpoints to the robots. Two multiagent architectures, a centralized one (TSP-Greedy Allocation or TSGA) and a distributed one (dist-TSGA), in which the robots locally compute their maps and share them, are developed and compared. Extensive numerical and real-world experiments with multiple aerial and ground robots in challenging 3-D environments show the flexibility and effectiveness of our surface representation of a volumetric map. The experiments also shed light on the nexus between reconstruction accuracy and surface completeness, and between total distance traveled and execution time.

Neural adaptive with impedance learning control for uncertain cooperative multiple robot manipulators

In this paper, an adaptive radial basis function neural network (RBFNN) control scheme is studied for the desired tracking of multiple robot manipulators with carrying a common object in joint-space. First, a non-zero time-varying parameter is introduced into the RBFNN, as well as a novel universal approximation of RBFNN with this non-zero parameter is introduced. Second, a switching control scheme based on this approximation property is designed. When the states of uncertainties terms leave the universal approximation domains, the suggested adaptive laws established by a sliding surface would pull them back, then the uncertain nonlinear functions are approximated by the RBFNNs in the domains. With this new structure of control technique, the limitation of a finite universal approximation can be broken. Third, the cooperative multiple manipulators can track the desired trajectory determined through impedance learning, which is used to modulate the control input in order to optimize the environment robot interaction. Finally, simulation results are shown to demonstrate the effectiveness in terms of expected tracking performance and position tracking errors constraints.

An Online Multi-Robot SLAM System Based on Lidar/UWB Fusion

Cooperation of multiple robots in simultaneous localization and mapping (SLAM) systems has more advantages compared to single robot configurations such as a faster exploration speed of the environment and the ability to perform tasks with high complexity. In this paper, we present an online multi-robot SLAM system which merges range measurements provided by UWB sensors and Lidar data provided by different mobile robots to build a globally-consistent map that contains individual point cloud maps and the trajectory estimations of all the robots. The proposed system does not require overlaps between robot trajectories. However, when the maps of different robots overlap, the system can further refine the relative robot transformations. The performance of the proposed system is evaluated through two experiments. The results of the experiments show that the proposed system can achieve accurate environment mapping and self-positioning by the cooperation of multiple robots in real-time.

Reinforcement-Learning-Based Route Generation for Heavy-Traffic Autonomous Mobile Robot Systems

Autonomous mobile robots (AMRs) are increasingly used in modern intralogistics systems as complexity and performance requirements become more stringent. One way to increase performance is to improve the operation and cooperation of multiple robots in their shared environment. The paper addresses these problems with a method for off-line route planning and on-line route execution. In the proposed approach, pre-computation of routes for frequent pick-up and drop-off locations limits the movements of AMRs to avoid conflict situations between them. The paper proposes a reinforcement learning approach where an agent builds the routes on a given layout while being rewarded according to different criteria based on the desired characteristics of the system. The results show that the proposed approach performs better in terms of throughput and reliability than the commonly used shortest-path-based approach for a large number of AMRs operating in the system. The use of the proposed approach is recommended when the need for high throughput requires the operation of a relatively large number of AMRs in relation to the size of the space in which the robots operate.

Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface.

Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. These indoor environments with integrated sloped areas are divided into different levels. The multi-level areas represent a challenge for mobile robot navigation due to the sudden change in reference sensors as visual, inertial, or laser scan instruments. Using multiple cooperative robots is advantageous for mapping and localization since they permit rapid exploration of the environment and provide higher redundancy than using a single robot. This study proposes a multi-robot localization using two robots (leader and follower) to perform a fast and robust environment exploration on multi-level areas. The leader robot is equipped with a 3D LIDAR for 2.5D mapping and a Kinect camera for RGB image acquisition. Using 3D LIDAR, the leader robot obtains information for particle localization, with particles sampled from the walls and obstacle tangents. We employ a convolutional neural network on the RGB images for multi-level area detection. Once the leader robot detects a multi-level area, it generates a path and sends a notification to the follower robot to go into the detected location. The follower robot utilizes a 2D LIDAR to explore the boundaries of the even areas and generate a 2D map using an extension of the iterative closest point. The 2D map is utilized as a re-localization resource in case of failure of the leader robot.

Design and Experimental Validation of a Distributed Cooperative Transportation Scheme

Leveraging explicit communication and cooperation of multiple robots brings about multiple advantages in the solution of tasks with autonomous robotic agents. For this reason, to the end of transporting polygonal objects with a group of mobile robots, the aim of this article is to develop a fully distributed decision-making and control scheme that lets the robots cooperate as equals, without any kind of central control instance. Apart from coordinating the motions of the robots in a distributed manner, challenges include the self-reliant determination of configurations of the robots around the object, as well as the interrobot negotiation on which robot takes which role or position in the transportation process. Based on some assumptions on the properties of the robots and the considered types of objects, all major aspects of the control scheme are explained in detail, with the most crucial parts making use of optimization-based schemes. This enables unprecedented flexibility by letting the robots automatically adapt to a wide range of scenarios, with different numbers of robots cooperating, and accommodating intricate object shapes. A simulation-based analysis reveals some of the key properties of the proposed scheme, whereas hardware-in-the-loop experiments show the practical applicability with inexpensive hardware. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the potential to further the state of the art of what is possible to achieve with autonomous robots when, instead of using one single robot, a group of robots cooperates self-reliantly to solve a task. The scenario studied in this article is a transportation task in which the robots shall cooperatively push an object to transport it along the desired path. The scheme proposed in this article deals with all major aspects of solving this transportation task, with simulations and hardware-in-the-loop experiments showing the practical applicability of the scheme. While the experiments have been conducted in a laboratory environment, one may readily imagine a more productive application, e.g., in logistics, when employing suitably designed hardware. On another note, maybe more importantly, the general architecture devised to solve the present task may serve as an inspiration or blueprint for the development of decision-making and control schemes that deal with alternative cooperative robotic tasks, especially with regard to the usage of optimization-based schemes on multiple levels allowing a flexible, yet still intuitive problem formulation.

Time Optimal Motion Planning and Admittance Control for Cooperative Grasping

Cooperative grasping refers to the situation when an object is manipulated by multiple robots and the grasp is achieved by the unilateral contact between the robots and the object. This is different from the cooperation of multiple robots where each robot rigidly grasps the object. Motion planning of cooperative grasping tasks involves active force control of the interaction wrench in order to ensure stable grasp. This becomes particularly challenging when aiming at time optimal motions. It is crucial that the trajectories are continuous up to third-order, in order to satisfy velocity, acceleration, and jerk as well as torque limits of the robots. A solution approach is presented for the time optimal path following of two robots performing cooperative grasping tasks. The time optimal path is determined with a dynamic programing method. An admittance control scheme in task space is proposed and used to generate the contact wrench. The method is applicable to grasping of general objects that are in surface contact with the robot.

Three-Dimensional Cooperative Mapping for Connected and Automated Vehicles

To improve the efficiency of three-dimensional (3-D) LIDAR mapping, multiple robots cooperation has been considered in mapping of large areas. In order to merge the local maps acquired by independent robots, it is crucial to identify common areas among the local maps. In this article, we propose a novel 3-D cooperative mapping approach for connected and automated vehicles that use only 3-D LIDAR data to cooperatively create globally consistent maps among multiple robots. Assuming each individual vehicle is able to create its own local map, the cooperative mapping process is divided into two main tasks: The common area detection task and the map merging task. We first significantly reduce the time cost of common area detection task by utilizing ground information as features. Further, the accuracy of map merging is guaranteed by whole frame matching of the common area frame pairs generated in the previous step. The proposed method is tested and validated by experiments in real-world environment for various scenarios; results show that the efficiency and accuracy of mapping are both improved compared to the individual robot mapping approach.

Research of grasping and transporting of objects by cooperation of multiple mobile robots

Research of grasping and transporting of objects by cooperation of multiple mobile robots

Cooperation of multiple autonomous robots and analysis of their swarm behavior

In this paper, we extended previous studies of cooperating autonomous robots to include situations when environmental changes and changes in the number of robots in the swarm can affect the efficiency to execute tasks assigned to the swarm of robots. We have presented a novel approach based on partition of the robot behaviour. The sub-diagrams describing sub-routs allowed us to model advanced interactions between autonomous robots using limited number of state combinations avoiding combinatorial explosion of reachability. We identified the systems for which we can ensure the correctness of robots interactions. New techniques were presented to verify and analyze combined robots’ behaviour. The partitioned diagrams allowed us to model advanced interactions between autonomous robots and detect irregularities such as deadlocks, lack of termination etc. The techniques were presented to verify and analyze combined robots’ behaviour using model checking approach. The described system, Dedan verifier, is still under development. In the near future, timed and probabilistic verification are planned..

Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment

The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots’ current local sub-map, in this occasion, a particle represents each of moving multi-robots, and simultaneously, also represents the pose of a motion robot. The trajectory of the robot’s movement generated a local sub-map; the sub-maps can be looked on as the particles. Each robot efficiently forms a local sub-map; the global map integrates over these local sub-maps; identifying SAR objects of interest, in which, each of multi-robots acts as local-level features collector. Once the object of interest (OOI) is detected, the location in the global map could be determined by the SLAM. The designed multi-robot SLAM architecture consists of PC remote control center, a master robot, and multi-followed robots. Through mobileRobot platform, the master robot controls multi-robots team, the multiple robots exchange information with each other, and then performs SLAM tasks; the PC remote control center can monitor multi-robot SLAM process and provide directly control for multi-robots, which guarantee robots conducting safety in harsh SAR environments. This SLAM method has significantly improved the objects identification, area coverage rate and loop-closure, and the corresponding simulations and experiments validate the significant effects.

Automatic large-scale three dimensional modeling using cooperative multiple robots

3D modeling of real objects by a 3D laser scanner has become popular in many applications, such as reverse engineering of petrochemical plants, civil engineering and construction, and digital preservation of cultural properties. Despite the development of lightweight and high-speed laser scanners, the complicated measurement procedure and long measurement time are still heavy burdens for widespread use of laser scanning. To solve these problems, a robotic 3D scanning system using multiple robots has been proposed. This system, named CPS-SLAM, consists of a parent robot with a 3D laser scanner and child robots with target markers. A large-scale 3D model is acquired by an on-board 3D laser scanner on the parent robot from several positions determined precisely by a localization technique, named the Cooperative Positioning System (CPS), that uses multiple robots. Therefore, this system can build a 3D model without complicated post-processing procedures such as ICP. In addition, this system is an open-loop SLAM system and a very precise 3D model can be obtained without closed loops. This paper proposes an automatic planning technique for a laser measurement by using CPS-SLAM. Planning a proper scanning strategy depending on a target structure makes it possible to perform laser scanning efficiently and accurately even for a large-scale and complex environment. The proposed technique plans an efficient scanning strategy automatically by taking account of several criteria, such as visibility between robots, error accumulation, and efficient traveling. We conducted computer simulations and outdoor experiments to verify the performance of the proposed technique.

Active Persistent Localization of a Three-Dimensional Moving Target Under Set-Membership Uncertainty Description Through Cooperation of Multiple Mobile Robots

The persistent localization of moving targets is one of the most important applications of mobile robot systems. However, the sensors on each individual robot are often not sufficiently accurate for this task, presenting a severe limitation on the application of robots to many situations. To overcome this problem, systems of multiple robots have been established. These systems improve the localization accuracy of mobile targets by fusing together multiple sensing data. Numerous studies have demonstrated that the relative motion (or specifically, the relative pose) between the robot and the target, as well as that among different robots, strongly influences the final localization accuracy of the data fusion algorithm. In other words, the regulation of motion in a multiple-robot system will lead to better localization results. Thus, in this paper, a new active persistent localization (APL) scheme is proposed. The underlying concept of this scheme is based on set-membership descriptions of uncertainties. The basic problem is formulated in the framework of an enhanced set-membership filter, and a new data fusion algorithm with improved accuracy and convergence properties is designed. A motion-planning algorithm is then incorporated under the so-called optimal localization condition to complete the APL scheme. Simulations using multiple 3-D mobile robot systems and experiments on a multiple rotor-flying-robots test bed show that the proposed algorithm successfully enhances the accuracy of the persistent localization of the moving target.

A multi-robot cooperation strategy for dexterous task oriented teleoperation

The use of multiple robots working cooperatively in a redundant way offers new possibilities in the execution of complex tasks in dynamic workspaces. The aim of this work is to increase the range of applicability of teleoperated systems by means of the automatic cooperation of multiple slave robots which, controlled by a human operator, act as if they were a unique robot: a Multi-Robot Cooperation Platform for Task-Oriented Teleoperation, MRCP. From the human operator commands, this robotic platform, the MRCP, dynamically selects the most suitable slave robot and manages, when necessary, a task transfer from one robot to another in order to achieve a smooth execution of teleoperated tasks.The result of the proposed methodology is an improved teleoperated system in terms of reachable workspace (volume, manoeuvrability and accessibility) and dexterity, thus widening its range of applicability. This approach allows human operators to focus their attention on the ongoing task more than on the teleoperated robots.

An efficient registration algorithm based on spin image for LiDAR 3D point cloud models

Spin image is a good point feature descriptor of the 3D surface and has been used in model registration for many applications from medical image processing to cooperation of multiple robots. However, researches show that current Spin-Image based Registration (SIR) algorithms present disadvantages in computational efficiency and robustness. Thus in this paper, aiming at 3D model acquired from LiDAR sensor, a new SIR algorithm is proposed to solve these problems. The new algorithm is on the basis of a new-constructed three-dimensional feature space, which, composed of the curvature, the Tsallis entropy of spin image, and the reflection intensity of laser sensor, is combined with the concept of KD-tree to firstly realize the primary key point matching, i.e., to find the Corresponding Point Candidate Set (CPCS). After that, spin-image based corresponding point searching is conducted with respect to each CPCS to precisely obtain the final corresponding points. The most absorbing advantages of the proposed method are as the following two aspects: on one hand, due to the introduction of the extra features, the fault corresponding relation introduced by spin image based method can be effectively reduced and thus the registration precision and robustness can be improved greatly; on the other hand, the CPCS obtained using low-dimensional feature space and KD-tree reduces extraordinarily the computational burden due to spin-image based correspondence searching. This greatly improves the computational efficiency of the proposed algorithm. Finally, in order to verify the feasibility and validity of the proposed algorithm, experiments are conducted and the results are analyzed.

A dynamic priority based path planning for cooperation of multiple mobile robots in formation forming

Robots that work in a proper formation show several advantages compared to a single complex robot, such as a reduced cost, robustness, efficiency and improved performance. Existing researches focused on the method of keeping the formation shape during the motion, but usually neglect collision constraints or assume a simplified model of obstacles. This paper investigates the path planning of forming a target robot formation in a clutter environment containing unknown obstacles. The contribution lies in proposing an efficient path planner for the multiple mobile robots to achieve their goals through the clutter environment and developing a dynamic priority strategy for cooperation of robots in forming the target formation. A multirobot system is set up to verify the proposed method of robot path planning. Simulations and experiments results demonstrate that the proposed method can successfully address the collision avoidance problem as well as the formation forming problem.

A Motion Planning Framework with Connectivity Management for Multiple Cooperative Robots

This paper proposes a decentralized motion planning algorithm for multiple cooperative robots subject to constraints imposed by sensors and the communication network. It consists of decentralized receding horizon planners that reside on each vehicle to navigate to individual target positions. A routing algorithm which modify the network topology based on the position of the robots and the limited range of transmitters and receivers, enables to reduce the communication link failures. A comparative study between the proposed algorithm and other existing algorithms is provided in order to show the advantages especially in terms of traveling time and communication link failure.

1A2-H04 群ロボットによる3次元環境地図自動構築システムの開発 : 位置同定誤差の蓄積を低減する観測戦略(移動ロボットの自己位置推定と地図構築(1))

1A2-H04 群ロボットによる3次元環境地図自動構築システムの開発 : 位置同定誤差の蓄積を低減する観測戦略(移動ロボットの自己位置推定と地図構築(1))

Off-Line Programming Techniques for Multirobot Cooperation System

An off-line programming software designed for multiple robots cooperation system is presented in this paper. At the beginning of the paper, current technologies of robot off-line programming are reviewed and summarized. Then, present status of cooperative functions and cooperative motion instructions defined by predominant robot manufacturers are introduced. Thereafter, principles of robot path planning for cooperative motions are presented. Based on such analysis, software architecture of the off-line programming software designed for general multirobot cooperation system is presented. At last, an off-line programming software with high detailed 3 dimension visualization is achieved. Simulation graphs and snapshots of typical cooperative motions are presented at the end of the paper, which proved the effectiveness of our proposed method.