Autonomous Multi-robot Systems Research Articles

Distributed Coverage Control with Mobile Robots: A Potential Game Approach

The use of mobile robots in industrial applications has led to a demand for autonomous multi-robot systems with robust and distributed algorithms. A critical objective in such systems is coverage control, where a team of mobile robots need to respond to spatiotemporal events in a bounded region. Here, we address a specific coverage problem, where a group of mobile robots are tasked with responding to events by covering specific locations on two sides of a linear workstation. We formulate the problem as a game played by the mobile robots with well-designed player strategies, and we demonstrate that the resulting framework is a potential game based on equally shared utilities among the robots. The proposed framework is distributed and decentralized, allowing for anonymous identities and constrained sensing capabilities in the robots. A set of simulation studies verify our approach.

Towards Safe and Resilient Autonomy in Multi-Robot Systems

In the near future, autonomous systems such as multi-robot systems are envisioned to increasingly co-exist with hu- mans in our daily lives, from household service to large- scale warehouse logistics, agriculture environment sampling, and smart city. In these applications, robots and humans as networked heterogeneous components will frequently inter- act with each other in a variety of scenarios under uncer- tain, rapidly-changing, and possibly hostile environment. On one hand, harmonious interactions among robots, as well as between robots and humans, would require safe integration (e.g. collision-free close-proximity interactions) of heteroge- neous robots, human, and human-robot autonomy. On the other hand, reliable interactions among autonomous multi- robot systems often call for resilient system integrity (e.g. communication capability with potential robot failures) to re- tain its capability of accomplishing complex tasks through coordinated behaviors. In the proposed talk, I will discuss our recent works towards safe autonomy and resilient autonomy that aim to facilitate correct-by-design robotic behaviors in a variety of applications.

Passivity based nonlinear model predictive control (PNMPC) of multi-robot systems for space applications.

In the past 2 decades, there has been increasing interest in autonomous multi-robot systems for space use. They can assemble space structures and provide services for other space assets. The utmost significance lies in the performance, stability, and robustness of these space operations. By considering system dynamics and constraints, the Model Predictive Control (MPC) framework optimizes performance. Unlike other methods, standard MPC can offer greater robustness due to its receding horizon nature. However, current literature on MPC application to space robotics primarily focuses on linear models, which is not suitable for highly non-linear multi-robot systems. Although Nonlinear MPC (NMPC) shows promise for free-floating space manipulators, current NMPC applications are limited to unconstrained non-linear systems and do not guarantee closed-loop stability. This paper introduces a novel approach to NMPC using the concept of passivity to multi-robot systems for space applications. By utilizing a passivity-based state constraint and a terminal storage function, the proposed PNMPC scheme ensures closed-loop stability and a superior performance. Therefore, this approach offers an alternative method to the control Lyapunov function for control of non-linear multi-robot space systems and applications, as stability and passivity exhibit a close relationship. Finally, this paper demonstrates that the benefits of passivity-based concepts and NMPC can be combined into a single NMPC scheme that maintains the advantages of each, including closed-loop stability through passivity and good performance through one-line optimization in NMPC.

An integrated strategy-based game-theoretic model and decentralized queueing system for mobile multi-robot task coordination

Autonomous multi-robot systems are restricted by their coordination ability with one another. Coordination entails the simultaneous division of tasks, the assignment of tasks, the formation of teams, the scheduling of tasks, and the routing of tasks. These activities are called task planning. Task planning is essential for the execution of task allocation problems in multi-robot systems. This study investigates the application of a decentralized queuing mechanism for task coordination in the context of mobile robots. Heuristic and strategic coordination strategies are used in developing the proposed system. The completion time and the distance traveled to process a demand are the two metrics used to evaluate the performance of robots in their respective operation field. The robots that used the stochastic queueing strategy achieved competitive results with those of the other robots.

Resilient and Consistent Multirobot Cooperative Localization With Covariance Intersection

Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple inter-robot communication with observation, making these algorithms susceptible to failures in both communication and observation steps. To enhance the resilience of multirobot cooperative localization algorithms in a distributed system, we use covariance intersection to formalize a localization algorithm with an explicit communication update and ensure estimation consistency at the same time. We investigate the covariance boundedness criterion of our algorithm with respect to communication and observation graphs, demonstrating provable localization performance under even sparse communications topologies. We substantiate the resilience of our algorithm as well as the boundedness analysis through experiments on simulated and benchmark physical data against varying communications connectivity and failure metrics. Especially when inter-robot communication is entirely blocked or partially unavailable, we demonstrate that our method is less affected and maintains desired performance compared to existing cooperative localization algorithms.

Energy efficient routing and task scheduling for autonomous transfer vehicles in intra logistics

Autonomous Transfer Vehicles (ATVs) are becoming increasingly prevalent in intra logistics. Industry 4.0 is bringing us closer to the efficient routing and scheduling of autonomous multi robot systems which perform transportation tasks. In this study, an energy efficient routing and scheduling system is proposed to minimize the total energy that the vehicles spend. Not only travelled distance but also the load of the vehicle is considered between two points. The routes of vehicles are obtained by using the proposed Hybrid Simulated Annealing Algorithm. An algorithm for the initial solution is also proposed for determining of the minimum number of vehicles for pickup and delivery requests. The performance of the algorithm is compared with the best solutions of the test problems in the literature. Besides, the proposed energy efficient routing and task scheduling model is compared with the classical distance model for routing and scheduling with backhauls. An analysis of trade-offs between energy and distance is proposed for intra logistics.

Secure fusion approach for the Internet of Things in smart autonomous multi-robot systems

The application of smart autonomous multi-robot systems has received increasing attention and dramatically developed, and this situation greatly promotes the development of the Internet of Things (IoT) and brings the IoT into a new stage. However, the IoT has some emerging issues, such as security and privacy; among many concerns, trusted identity authentication and consensus protocols are the main ones. To address these issues, this study proposes a novel scheme for the identity authentication and consensus algorithm. In the proposed scheme, an identity-based authentication model is designed, and all communication nodes utilize this model to establish connection and conduct data exchange. Meanwhile, a hash pool-based joint consensus algorithm is constructed. All transmission data are strictly protected by permuting hash functions from hash pool and utilizing the generated random number. This feature greatly enhances the security for the IoT. Analysis and experimental results demonstrate that the proposed scheme provides enhanced security and reliability for the IoT. Furthermore, the time efficiency is encouraging, and the communication and storage cost is promising.

SolGuard: Preventing external call issues in smart contract-based multi-agent robotic systems

In the new era of blockchain-based multi-agent robotic systems, smart contract programs perform an influential role in implementing decentralized applications with required task allocations. Smart contract programs are developed using script-type of programming languages, and they have already deployed several vulnerable patterns without proper testing and audit. We studied Solidity smart contracts running on the Ethereum platform and identified that they had been exploited because of several programming issues, especially using low-level external calls to malicious sources. Since smart contracts are immutable after their deployment to autonomous multi-robot systems, they should be tested to fix possible development phase issues. We implemented a prototype plugin called SolGuard by extending the solhint linter to prevent three critical issues related to Solidity smart contract programs’ usage of external calls. The SolGuard plugin checks state variable order in the smart contracts, participation of delegatecall invocations, address type parameters in the smart contract’s constructor, and denial of service patterns. We empirically evaluate the SolGuard plugin with existing popular static analysis tools. Our results indicate that SolGuard outperformed the baseline tools in terms of efficiency and accuracy.

Secure and smart autonomous multi-robot systems for opinion spammer detection

Reviews of social media have become very important reference indicators for people shopping. However, in order to attract more consumers, some bad merchants organize many fraudulent reviewers to conduct malicious reviews aim at misleading consumers, which is called opinion spammer group. Previous studies have shown that there is an implicit community between reviewers In this paper, we try to use community discovery approach in secure and smart autonomous multi-robot systems for opinion spammer detection, and improve the prediction precision. We propose a novel approach, named SPClique, which is based on the Clique Percolation Method (CPM) to detect opinion spammer groups by modeling the review dataset as reviewer projection graph. During the process, we introduce approximate computing to get a valuable smaller reviewer-projection graph and expand the computing power. It first executes the CPM algorithm in the reviewer-projection graph to find all k-clique clusters, and then innovatively describes an opinion spammer group based on k-clique clusters. Second, the group-based spam and individual-based spams indicators are used to measure the suspiciousness of each opinion spammer group. Finally, it outputs the suspect ranking of opinion spammer groups. The prediction precision of our proposed method is better than four advanced comparison methods, and our approach can detect more real opinion spammers in large-scale review datasets.

Task allocation and coordinated motion planning for autonomous multi-robot optical inspection systems

Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due to fast computation requirements of inline optimization, unique characteristics on robotic end-effector orientations, and complex large-scale free-form product surfaces. This paper proposes a novel task allocation methodology for coordinated motion planning of multi-robot inspection. Specifically, (1) a local robust inspection task allocation is proposed to achieve efficient and well-balanced measurement assignment among robots; (2) collision-free path planning and coordinated motion planning are developed via dynamic searching in robotic coordinate space and perturbation of probe poses or local paths in the conflicting robots. A case study shows that the proposed approach can mitigate the risk of collisions between robots and environments, resolve conflicts among robots, and reduce the inspection cycle time significantly and consistently.

Image style transfer for autonomous multi-robot systems

In deep learning, multi-robot systems intelligently cooperate to perform a complex task, and improve the performance of a single robot through communication, collaboration and sharing of information. Intelligent collaborative work makes the research and application of multi-robot systems develop rapidly. The traditional image style transfer algorithm mainly uses mathematical modeling to characterize the texture of the picture, and combines the content image with the style image to achieve the effect of style transfer. This style transfer algorithm ignores the edge distribution of the image, which makes the contour of the generated image blurred, and it takes a long time in the iterative process of style transfer, and the effect is poor. Therefore, this paper proposes a ceramic decoration pattern style migration algorithm based on the ESPCN model. The algorithm uses the Laplace operator to sharpen the image to highlight the edge distribution, and then uses downsampling to generate low-resolution images to reduce the iteration time of image style transfer. The final generated image uses ESPCN super-resolution reconstruction to reconstruct the low-resolution image. Converted to high-resolution images. The experimental results show that multiple image evaluation indicators show that the edge distribution of the generated image is clear, the iteration time is shortened, and the image quality and definition are improved.

An improved DPoS consensus mechanism in blockchain based on PLTS for the smart autonomous multi-robot system

Due to the development of robot technology, smart autonomous multi-robot systems face different security problems such as data loss and vulnerabilities. However, the robot information stored in blockchain can be more transparent and effective at ensuring the security of the robot system due to the decentralization, tamper-proof and anonymity of blockchain. In the architectural composition of blockchain, Delegated Proof of Stake (DPoS) consensus mechanism is playing a critical role with more decentralization, lower energy consumption and faster confirmation speed. Similar to the board voting, the holders cast a certain number of delegates to perform verification and block generating on their behalf in DPoS. In order to improve the efficiency and flexibility of DPoS consensus mechanism, we propose an improved DPoS consensus mechanism based on the Probabilistic Linguistic Term Set (PLTS) for the smart autonomous multi-robot system. By adding voting options for nodes, the Voting Algorithm with Probabilistic Linguistic Information (VAPLI) calculates the score and deviation degree of each node after tabulating the voting results. The selection of a delegate is based on the comparison of the score and deviation degree. Finally, we explore the model implementation of the improved DPoS consensus mechanism, and verify its feasibility and effectiveness using examples.

An efficient transmission algorithm for power grid data suitable for autonomous multi-robot systems

Under the power environment of Internet of things used in autonomous multi-robot systems, due to the influence of various actual environmental factors, the security protection method based on security partition and physical isolation of nodes is difficult to fully meet the information security protection requirements of the power system, and the network threats such as impersonation, eavesdropping, and dexterity on communication links are still difficult to avoid. In order to improve the security of node data transmission in autonomous multi-robot power systems, reducing network energy consumption has become the bottleneck of network application and development. In this paper, we propose an Improved Effective Clustering Algorithm (IECA) and transmission algorithm of power grid data based on autonomous multi-robot systems. The distance between nodes sending data to the next-hop node is equivalent to the energy model. The remaining energy in the cluster head node sending data in the next hop is recorded, and the remaining energy is subtracted from the equivalent energy to find a maximum value. The corresponding node of the maximum value is taken as the number of nodes According to the received node. Compared with the LEACH algorithm and GAF algorithm, this algorithm can not only extend the lifetime of nodes but also has more efficient performance.

Multi-Robot Information Gathering for Precision Agriculture: Current State, Scope, and Challenges

Precision agriculture is the collection of hardware and software technologies that allow a farmer to make informed, differentiated decisions regarding agricultural operations such as planting, fertilizing, pest control, and harvesting. In recent years, advances in agricultural machinery and the emergence of agricultural robots continuously increased the resolution at which differentiated treatment is possible. This creates a corresponding need for information at a fine spatial and temporal resolution. Autonomous multi-robot systems (e.g., unmanned ground and aerial vehicles) are some of the most promising approaches for such information collection in open-air farms. In this paper, we survey the current state and challenges of multi-robot information gathering for precision agriculture, with a special focus on maximizing information and ensuring the security of the collected data while simultaneously keeping energy consumption in check.

AGV routing and motion planning in a flexible manufacturing system using a fuzzy-based genetic algorithm

Autonomous-guided vehicles (AGVs) are becoming increasingly prevalent and already helping in a variety of activities, ranging from space exploration to domestic housework. Recent advances in the design of sensors, motors and microelectromechanical systems are bringing us closer to the realization of autonomous multi robot systems which perform complex tasks. In this work, we explored the problem of vehicle routing and motion planning for a fleet of AGVs in a flexible manufacturing system (FMS). Considering concurrently fuzzy demands associated with the workstations and fuzzy travel distances while moving between workstations, the problem is addressed in the context of uncertainty both in demands and travel times. The proposed motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. Furthermore, in order to take into account the fuzziness which may arise in the FMS, the proposed planner is integrated with fuzzy theory on fuzzy sets and fuzzy numbers. The efficiency of the solution procedure is demonstrated through numerical examples.

Model-based testing of autonomous robots using TestIt

This paper presents a testing toolkit named TestIt. Its main goal is to provide tools for automated model-based testing of autonomous multi-robot systems to verify long-term autonomy in various applications including those of smart city environments and smart buildings. The main novelty of the presented toolkit is the open and scalable multi-pipeline architecture that enables incorporation of test development and execution tools from various vendors. The second main contribution is adaptive test optimization technique that takes advantage of the proposed multi-pipeline architecture where testing threads can communicate and coordinate the test runs based on their cooperatively collected test performance data. The usability of TestIt for test generation, testing process performance improvement and optimization in multi-robot autonomous navigation context is demonstrated using a smart building robotic security system case study.

Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor

Collective behavior based on self-organization has been observed in populations of animals from insects to vertebrates. These findings have motivated engineers to investigate approaches to control autonomous multi-robot systems able to reproduce collective animal behaviors, and even to collectively interact with groups of animals. In this article, we show collective decision making by a group of autonomous robots and a group of zebrafish, leading to a shared decision about swimming direction. The robots can also modulate the collective decision-making process in biased and non-biased experimental setups. These results demonstrate the possibility of creating mixed societies of vertebrates and robots in order to study or control animal behavior.

Decentralized Cooperative Localization Approach for Autonomous Multirobot Systems

This study proposes the use of a split covariance intersection algorithm (Split-CI) for decentralized multirobot cooperative localization. In the proposed method, each robot maintains a local cubature Kalman filter to estimate its own pose in a predefined coordinate frame. When a robot receives pose information from neighbouring robots, it employs a Split-CI based approach to fuse this received measurement with its local belief. The computational and communicative complexities of the proposed algorithm increase linearly with the number of robots in the multirobot systems (MRS). The proposed method does not require fully connected synchronous communication channels between robots; in fact, it is applicable for MRS with asynchronous and partially connected communication networks. The pose estimation error of the proposed method is bounded. As the proposed method is capable of handling independent and interdependent information of the estimations separately, it does not generate overconfidence state estimations. The performance of the proposed method is compared with several multirobot localization approaches. The simulation and experiment results demonstrate that the proposed algorithm outperforms the single-robot localization algorithms and achieves approximately the same estimation accuracy as the centralized cooperative localization approach, but with reduced computational and communicative cost.

A Spatial Queuing-Based Algorithm for Multi-Robot Task Allocation

Multi-robot task allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to allocate a set of tasks to a set of robots so that the tasks can be completed by the robots while ensuring that a certain metric, such as the time required to complete all tasks, or the distance traveled, or the energy expended by the robots is reduced. We consider a scenario where tasks can appear dynamically and a task needs to be performed by multiple robots to be completed. We propose a new algorithm called SQ-MRTA (Spatial Queueing-MRTA) that uses a spatial queue-based model to allocate tasks between robots in a distributed manner. We have implemented the SQ-MRTA algorithm on accurately simulated models of Corobot robots within the Webots simulator for different numbers of robots and tasks and compared its performance with other state-of-the-art MRTA algorithms. Our results show that the SQ-MRTA algorithm is able to scale up with the number of tasks and robots in the environment, and it either outperforms or performs comparably with respect to other distributed MRTA algorithms.

Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots

In autonomous multirobot systems one of the concerns is how to prevent collisions between the individual robots. One approach to this problem involves finding coordinated trajectories from start to destination for all the robots and then letting the robots follow the preplanned coordinated trajectories. A widely used practical method for finding such coordinated trajectories is “classical” prioritized planning, where robots plan sequentially one after another. This method has been shown to be effective in practice, but it is incomplete (i.e., there are solvable problem instances that the algorithm fails to solve) and it has not yet been formally analyzed under what circumstances is the method guaranteed to succeed. Further, prioritized planning is a centralized algorithm, which makes the method unsuitable for decentralized multirobot systems.