Cooperative Task Execution Research Articles

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.

Enzymatically-induced dynamic assemblies from surface functional stomatocyte nanoreactors.

Collective behavior has become a recent topic of investigation in systems chemistry. In pursuing this phenomenon, we present polymersome stomatocytes loaded with the enzyme urease, which show basic stigmergy-based communication and are capable of signal production, reception, and response by clustering with surface complementary binding partners. The collective behavior is transient and based on the widely known pH-sensitive non-covalent interactions between nitrilotriacetic acid (NTA) and histidine (His) moieties attached to the surface of urease-loaded and empty stomacytes, respectively. Upon the addition of the substrate urea, the urease stomatocytes are able to increase the environmental pH, allowing the NTA units to interact with the surface histidines on the complementary species, triggering the formation of transient clusters. The stomatocytes display a maximum clustering interaction at a pH between 6.3 and 7.3, and interparticle repulsive behavior outside this range. This leads to oscillating behavior, as the aggregates disassemble when the pH increases due to high local urease activity. After bulk pH conditions are restored, clustering can take place again. Within the detectable region of dynamic light scattering, individual stomatocytes can aggregate to agglomerates with 10 times their volume. Understanding and designing population behavior of active colloids can facilitate the execution of cooperative tasks, which are not feasible for individual colloids.

Cooperative Task Execution for Object Detection in Edge Computing: An Internet of Things Application

The development of computer hardware and communications has brought with it many exciting applications in the Internet of Things. More and more Single Board Computers (SBC) with high performance and low power consumption are used to infer deep learning models at the edge of the network. In this article, we investigate a cooperative task execution system in an edge computing architecture. In our topology, the edge server offloads different workloads to end devices, which collaboratively execute object detection on the transmitted sets of images. Our proposed system attempts to provide optimization in terms of execution accuracy and execution time for inferencing deep learning models. Furthermore, we focus on implementing new policies to optimize the E2E execution time and the execution accuracy of the system by highlighting the key role of effective image compression and the batch sizes (splitting decisions) received by the end devices from a server at the network edge. In our testbed, we used the You Only Look Once (YOLO) version 5, which is one of the most popular object detectors. In our heterogeneous testbed, an edge server and three different end devices were used with different characteristics like CPU/TPU, different sizes of RAM, and different neural network input sizes to identify sharp trade-offs. Firstly, we implemented the YOLOv5 on our end devices to evaluate the performance of the model using metrics like Precision, Recall, and mAP on the COCO dataset. Finally, we explore optimal trade-offs for different task-splitting strategies and compression decisions to optimize total performance. We demonstrate that offloading workloads on multiple end devices based on different splitting decisions and compression values improves the system’s performance to respond in real-time conditions without needing a server or cloud resources.

Bearings only passive location for UAV in formation flight

With the increasing development of UAV technology, UAV formation flight has also changed from single target task execution to multi-target cooperative task execution. When UAV is developing rapidly in the direction of clustering, there are too many external interference factors. Therefore, electromagnetic silence should be maintained for a long time in the whole formation flight, and the number of times when transmitting electromagnetic wave signals to the outside should be reduced as much as possible. Therefore, this paper uses the sine cosine theorem to adjust the position of UAV to the optimal position by using bearings only passive location method to maintain the formation of UAV multi-target flight. In this paper, the cosine theorem and sine theorem are used to locate and optimize the other UAVs that receive signals passively; By enumerating, the corresponding linear equations are listed by using the triangle formed by all UAVs; The position of each UAV shall be adjusted step by step according to the above conclusions, and the remaining UAVs receiving signals passively shall be adjusted to the optimal position to ensure the uniform distribution of nine UAVs on the circumference with a radius of 100m.

Cooperative Robotics and Machine Learning for Smart Manufacturing: Platform Design and Trends Within the Context of Industrial Internet of Things

Internet of Things (IoT) in industrial settings now leads to the development of a new generation of systems designed to improve the operational efficiency of the new paradigm of smart manufacturing plants. Thereby, the current article introduces in detail the definitions, concepts, standards, and other important aspects related to smart manufacturing, cooperative robotics, and machine learning techniques. The paper highlights the opportunities presented by the new paradigm and the challenges faced in effectively implementing it in the industrial context. Especially, the focus is on the challenges associated with the architectures, communications technology, and protocols that enable the integration and deployment of machine learning algorithms to improve the execution of cooperative tasks performed daily by human operators, machines, and robots. The article also provides a systematic review of state-of-the-art research efforts for the fields aforementioned. Finally, an architecture for integrating collaborative robotics and machine learning based on six layers and four hierarchies of the RAMI 4.0 (Reference Architectural Model Industry 4.0) is presented.

Communication, Computation, and Caching Resource Sharing for the Internet of Things

The Internet of Things connects a large number of smart mobile devices with the Internet, where these devices are embedded with often limited communication, computation, and caching resources. To address the heterogeneity of these devices and achieve efficient overall system resource utilization, researchers have proposed various device-to-device resource sharing models, enabling mobile devices to form device-todevice connections and to share their resources for cooperative task execution. Most of these existing works, however, considered scenarios where mobile devices can share one or two types of resources, and hence inadequately explore the potential of resource sharing among mobile devices. In this article, we introduce a general framework where mobile devices can share any combination of the three types of resources, and it can generalize many existing deviceto- device resource sharing models. In addition, it can achieve more efficient resource allocation by offering mobile devices more flexibility in terms of resource sharing. Based on the proposed framework, we focus on discussing two issues: the optimization issue, regarding how to schedule resources among devices; and the economic issue, regarding how to motivate the device owners to share their resources. We introduce the challenges and potential solutions to these two issues. We further outline several open issues and future directions for the proposed general resource sharing framework.

Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks.

Wireless sensor networks have been attracting research attention for the past decade and will continue to be a hot topic due to the emerging trend of Internet-of-Things (IoT). Edge computing for IoT (Edge-IoT) is a promising framework that can help low-powered sensor networks to conduct complex computational tasks. Different from the existing works that focus on cooperative task execution for edge and sensor networks, in this paper, we investigate the problem of reliable data transmission in edge-aided sensor networks. Firstly, we discuss how edge servers can help to improve the data transmission of sensor networks. Secondly, we propose a forwarding scheme for edge nodes to forward packets according to coverage and corresponding interference. Thirdly, we propose an edge-based error recovery approach. By employing edge servers for data transmission and error recovery, the efficiency and reliability of data transmissions can be largely improved.

Distributed Path Planning for Executing Cooperative Tasks with Time Windows

We investigate the distributed planning of robot trajectories for optimal execution of cooperative tasks with time windows. In this setting, each task has a value and is completed if sufficiently many robots are simultaneously present at the necessary location within the specified time window. Tasks keep arriving periodically over cycles. The task specifications (required number of robots, location, time window, and value) are unknown a priori and the robots try to maximize the value of completed tasks by planning their own trajectories for the upcoming cycle based on their past observations in a distributed manner. Considering the recharging and maintenance needs, robots are required to start and end each cycle at their assigned stations located in the environment. We map this problem to a game theoretic formulation and maximize the collective performance through distributed learning. Some simulation results are also provided to demonstrate the performance of the proposed approach.

Development of dynamic braking of omni-wheel rollers for dual robot cooperative task execution

Development of dynamic braking of omni-wheel rollers for dual robot cooperative task execution

Kinesthetic teaching and attentional supervision of structured tasks in human–robot interaction

We present a framework that allows a robot manipulator to learn how to execute structured tasks from human demonstrations. The proposed system combines physical human–robot interaction with attentional supervision in order to support kinesthetic teaching, incremental learning, and cooperative execution of hierarchically structured tasks. In the proposed framework, the human demonstration is automatically segmented into basic movements, which are related to a task structure by an attentional system that supervises the overall interaction. The attentional system permits to track the human demonstration at different levels of abstraction and supports implicit non-verbal communication both during the teaching and the execution phase. Attention manipulation mechanisms (e.g. object and verbal cueing) can be exploited by the teacher to facilitate the learning process. On the other hand, the attentional system permits flexible and cooperative task execution. The paper describes the overall system architecture and details how cooperative tasks are learned and executed. The proposed approach is evaluated in a human–robot co-working scenario, showing that the robot is effectively able to rapidly learn and flexibly execute structured tasks.

Dynamic braking of omni-wheel rollers for dual robot cooperative task execution

This paper presents a novel wheel that features dynamic braking of the rollers of an omni-wheel. This results in interesting motions that would not be attained by ordinary wheels or the typical omni-wheel especially for dual differential drive robots. The design and prototype of the wheel is presented together with basic modeling.

Design and Implementation of Distributed Autonomous Coordinators for Cooperative Multi-Robot Systems

The paper presents a systematic method of the design of cooperative task planning and execution for complex robotic systems using multiple robots. Because individual robots can autonomously execute their dedicated tasks, in cooperative multi-robot systems, robotic activities should be designed as discrete event driven asynchronous, concurrent processes. Further, since robotic activities are hierarchically defined, control requirements should be specified in a proper and consistent manner on different levels of control abstraction. In this paper, Petri nets are adopted as a specification tool for task planning and execution by multiple robots. Based on place/transition Petri nets, control conditions for inter-robot cooperation with synchronized interaction are represented, and control rules to achieve distributed autonomous coordinated activities with synchronous and asynchronous communication are proposed. An implementation of net based control software on hierarchical and distributed architecture is presented for an example multi-robot cell, where the higher-level controller executes a global net model of task plan representing cooperative behaviors performed by the robots, and the parallel activities of the individual robots are synchronized through the transmission of requests and the reception of status between the associated lower-level local controllers.

Modular robot systems towards the execution of cooperative tasks in large facilities

Large facilities present with wide range of tasks and modular robots present as a flexible robot solution. Some of the tasks to be performed in large facilities can vary from, achieving locomotion with different modular robot (M-Robot) configurations or the execution of cooperative tasks such as moving objects or manipulating objects with multiple modular robot configurations (M-Robot colony) and existing robot deployments. The coordination mechanisms enable the M-Robots to perform cooperative tasks as efficiently as specialised or standard robots. The approach is based on the combination of two communication types i.e., Inter Robot and Intra Robot communications. Through this communication architecture, tight and loose cooperation strategies are implemented to synchronise modules within an M-Robot configuration and to coordinate M-Robots belonging to the colony. These cooperation strategies are based on a closed-loop discrete time method, a remote clock reading method and a negotiation protocol. The coordination mechanisms and cooperation strategies are implemented into a real modular robotic system, SMART. The need for using such a mechanism in hazardous section of large scientific facilities is presented along with constraints and tasks. Locomotion execution of the mobile M-Robots colony in a bar-pushing task is used as an example for cooperative task execution of the coordination mechanisms and results are presented.

Decentralized control of collaborative redundant manipulators with partial command coverage via locally connected recurrent neural networks

This paper studies the decentralized control of multiple redundant manipulators for the cooperative task execution problem. Different from existing work with assumptions that all manipulators are accessible to the command signal, we propose in this paper a novel strategy capable of solving the problem even though there exists some manipulators unable to access the command signal directly. The cooperative task execution problem can be formulated as a constrained quadratic programming problem. We start analysis by re-designing the control law proposed in (Li et al. Neurocomputing, 2012), which solves the optimization problem recursively. By replacing the command signal with estimations with neighbor information, the control law becomes to work in the partial command coverage situation. However, the stability and optimality of the new system are not necessarily the same as the original system. We then prove in theory that the system indeed also globally stabilizes to the optimal solution of the constrained quadratic optimization problem. Simulations demonstrate the effectiveness of the proposed method.

Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks

This paper studies the decentralized kinematic control of multiple redundant manipulators for the cooperative task execution problem. The problem is formulated as a constrained quadratic programming problem and then a recurrent neural network with independent modules is proposed to solve the problem in a distributed manner. Each module in the neural network controls a single manipulator in real time without explicit communication with others and all the modules together collectively solve the common task. The global stability of the proposed neural network and the optimality of the neural solution are proven in theory. Application orientated simulations demonstrate the effectiveness of the proposed method.

Optimal Sensor Placement for Underwater Target Positioning with Noisy Range Measurements

Abstract Autonomous underwater vehicles (AUVs) are becoming ubiquitous due in part to the flexibility and versatility that a number of them display in the execution of individual and cooperative tasks. These characteristics, coupled with the fact that their use avoids placing human lives at risk, makes them quite attractive in a number of missions. Central to the operation of some classes of AUVs is the availability of a good underwater positioning system to localize one or more vehicles simultaneously based on information received on-board a support ship or an autonomous surface system. Speaking in loose terms, we are interested in determining the optimal geometric configuration of a surface sensor network that will, in a well defined sense, maximize the range-related information available for underwater target positioning. To this effect, we assume that the range measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. The Fisher Information Matrix and the maximization of its determinant are used to determine the sensor configuration that yields the most accurate positioning of the target. An explicit analytical result is first obtained for a three sensor network and then extended to a generic “n„ sensor network. It is shown that the optimal configuration lends itself to an interesting geometrical interpretation and that the “spreading„ of the configuration depends directly on the intensity of the range measurement noise and the target depth. Simulation examples illustrate the key results derived.

ARCHITECTURE OF COOPERATION FOR MULTI-ROBOT SYSTEMS

This paper presents a new architecture for coordinating multiple autonomous robots in the execution of cooperative tasks. The architecture is based on the definition of a strategy that uses different tactics, setplays and roles. Roles enable to configure individual behaviour by performing specific tasks using a given set of actions. The proposed architecture allows flexible and efficient multi-robot operation in dynamic environments. The paper also presents an application of the proposed architecture to a complex domain such as middle-size robotic soccer. Our architecture is also generally enough to be applied in different robotic soccer leagues and similar multi-robot problems.

METHODOLOGY FOR PARAMETERS OPTIMIZATION OF AN HYBRID ARCHITECTURE OF CONTROL

This paper presents a methodology for parameters optimization applied to an hybrid behavioral control architecture. The coordination between behaviors in this architecture is insured using both hierarchical and fusion action mechanisms. This global mechanism of coordination is characterized by a multitude of parameters which must be finely tuned to enhance the efficiency of the execution of cooperative tasks. The proposed parameters optimization is obtained using genetic algorithms. Appropriate genetic operators are used to manipulate real chromosomes with a normalization relationship between its genes. The validation of the results is established using a large number of simulations.

A Paradigm for Dynamic Coordination of Multiple Robots

In this paper, we present a paradigm for coordinating multiple robots in the execution of cooperative tasks. The basic idea in the paper is to assign to each robot in the team, a role that determines its actions during the cooperation. The robots dynamically assume and exchange roles in a synchronized manner in order to perform the task successfully, adapting to unexpected events in the environment. We model this mechanism using a hybrid systems framework and apply it in different cooperative tasks: cooperative manipulation and cooperative search and transportation. Simulations and real experiments demonstrating the effectiveness of the proposed paradigm are presented.

Development of an autonomous underwater robot ?Twin-Burger? for testing intelligent behaviors in realistic environments

An autonomous underwater robot named “Twin-Burger” was developed as a versatile test bed to establish the techniques which realize intelligent robot behaviors. The robot was designed to have necessary functions for complex tasks including cooperative task execution with other robots and divers. The first robot “Twin-Burger I” was completed and launched in November 1992. This paper describes hardware and software systems of the robot. Motion of the robot is controlled by sliding controllers based on simplified equations of motion which are derived from system identification experiments. Tank tests proved that the robot was able to cruise along a commanded path as a sequence of control actions generated by the sliding controllers. The Distributed Vehicle Management Architecture (DVMA) is applied to the robot as an architecture for the control software. Mission execution experiments shows that the Twin-Burger behaves appropriately according to the mission and environmental conditions.