Incremental task selection and allocation for the real-world multiple travelling robot problem

Bu çalışmada, Çoklu-Robot Çoklu-Hedef Ataması problemi için bir gerçek zamanlı görev seçme ve atama yöntemi önerilmektedir. Çoklu-Robot Çoklu-Hedef Ataması problemi, literatürde iyi bilinen ve tüm veri kümeleri için en iyi çözümlerin polinomsal algoritmalarla bulunamadığı MTSP (Multiple Traveling Salesman Problem) probleminin her bir hedefin en az bir robot tarafından ziyaret edilmesini gerektiren değişik bir uyarlamasıdır. Bu problem için farklı eniyileme amaç fonksiyonları tanımlanabilir (Örn. yürütme zamanını iyileme, robotların toplam yollarını iyileme, vb.). Bu makalede, bu problemin gerçek dünya versiyonu ele alınıp, yürütme zamanında ortaya çıkabilecek problemler irdelenmiştir. Çoklu-robot sistemlerinin ortaklaşa çalışmalarında karşılaşılan en büyük güçlükler, görevlerin yürütme zamanının önceden kesin olarak tahmin edilememesi, yürütme süresince değişebilen maliyet değerleri ile belirsizlik ve tutarsızlıklardan kaynaklanır. Bu çalışmada, yürütme zamanı kısıtlamalarını da aşmak üzere etkin bir dinamik görev seçim ve atama yöntemi önerilmekte ve önerilen yöntemin, hem benzetim ortamlarında hem de gerçek robotlar üzerinde yapılan testlerle başarım analizi yapılmaktadır. Önerilen yöntem, yürütme zamanında oluşabilecek birçok hataya karşı dayanıklı olarak sistemin güncel durumuna göre artımlı ve dağıtılmış bir görev ataması gerçekler. Gerçek zamanlı yürütmeye uygun şekilde iletişim gereksinimleri minimumda tutulmaya çalışılmıştır. Gerçeklenen testler yöntemin bilgi-işlemsel açıdan etkin ve düşük maliyetli, sistemin hatalara karşı dayanıklı olmasını sağlayacak şekilde çalıştığını gösterir niteliktedir. Anahtar Kelimeler: Çoklu-robot sistemleri, gerçek zamanlı çoklu-robot çoklu-hedef atama, artımlı görev seçimi, dinamik görev atama, hataya dayanıklılık.In this study, the real-world Multiple Travelling Robot Problem (MTRP) is analyzed and an integrated approach is proposed to solve this problem in real time. MTRP is a generalization of the well-known Multiple Traveling Salesman Problems and is solved by a multi-robot team. In MTRP, a different version of the well known NP-hard MTSP (Multiple Traveling Salesman Problem), each target must be visited by at least one robot in its open tour. Depending on the selected application domain, various objectives may be defined for this problem (e.g. minimization of total path length, time, makespan etc.). Although this problem has emerged from Operations Research, it has become one of the main problems in multi-robot research for different missions (e.g., search and rescue, space, or surveillance operations, etc.). In this article, besides the target allocation issue, the real challenges of this problem are presented. Unpredictability of the exact processing times of tasks, unstable cost values during runtime and inconsistencies due to uncertain information form the main difficulties of the task allocation problem for robot systems. Since the real world is beyond the control of robots, in most cases, the Operations Research solutions are not directly applicable due to either robot hardware/software limitations or environmental dynamics. These approaches may become impractical when the size of the mission is even moderate or the cost values change frequently because of the uncertain knowledge, changes in the environment (including failures) or the changing structure of the mission (e.g. online tasks). Furthermore, robots have continuous path planning burdens for target sets in dynamic environments. Expensive computational efforts for initial allocations may become redundant. We propose a solution as a generalized framework " DEMiR-CF (Distributed and Efficient Multi-Robot Cooperation Framework) " which includes dynamic task selection, distributed task allocation and contingency handling mechanisms. These mechanisms and lower level robot controllers, the motor and sensory modules are integrated together to solve the real-world MTRP. DEMiR-CF, while capable of handling diverse contingencies, performs an incremental task allocation method based on the current information about the environment. Globally efficient solutions are obtained by the proposed mechanisms that form priority based rough schedules and select the most suitable tasks from these schedules. Rough schedules are formed by using information regarding the beliefs about the other robots. Since DEMiR-CF is for real-world task execution, communication requirements are kept at minimum as much as possible. The approach is distributed and computationally efficient. Target allocation and route construction is integrated into each other by an incremental assignment approach. Real time conditions and contingencies that change the problem instance are handled at the same time by the designed contingency handling mechanisms. Robots keep system models to correct models of their own or warn other robots to maintain system consistency. Empirical evaluations of the system performed on the Webots simulator and on Khepera II robots reveal the efficiency of the integrated components of the approach. Experiments are designed in three sets. In the first set of the experiments, evaluations of the proposed cost functions to be used with DEMiR-CF are performed. Comparisons are made both with the optimal results taken from the IP solver CPLEX and that of the Prim Allocation approach, one of the efficient methods to solve this problem. As the results illustrate, allocating all targets from scratch and generating routes of robots may result in suboptimal solutions. Therefore the target allocation and the route construction should be integrated for efficient heuristic approaches. This integration and incremental allocation is also useful for eliminating redundant calculations especially in highly dynamic or unknown environments. In the second set of experiments, scalability of the framework and the response efficiency of the contingency handling mechanism integrated into the framework are evaluated. The scalability of the approach is validated and the efficiency of using the contingency handling mechanisms is observed in the results. The third set of experiments is performed on real robots. As results illustrate, both incremental task selection and allocation approaches produce efficient results and the contingency handling mechanisms make the system robust and allow the handling of real-time online situations. Keywords: Multi-robot systems, multi-robot multi-target assignment in real-time, incremental task selection, dynamic task allocation, robustness.

Topology-aware optimal task allocation framework for mission critical environment: Centralized and decentralized approaches

A Mission Critical Environment (MCE) consists of error-prone, highly variable, and highly rate limited communication channels. Paradoxically, this environment substantially increases the need to perform Optimal Task Allocation (OTA), while at the same time making it much harder to perform OTA efficiently. To perform OTA in MCE, in this thesis, I have proposed two novel automated algorithms. The first algorithm is called Centralized Optimal Task Allocation Algorithm (COTAA), where I consider OTA for publish/subscribe-based MCE since it has unique characteristics such as high level publish/subscribe node and task differentiation and high scalability. I also propose an architectural framework and communication protocols emphasizing the unique challenges of MCE. I adopt well known Hungarian Algorithm and Rectangular Assignment Algorithm to solve the OTA problem in polynomial time. The second algorithm is called Decentralized Optimal Task Allocation Algorithm (DOTAA) which exploits the concept of application-layer Distributed Hash Table (DHT) to perform OTA in MCE. Through simulations, I evaluate the performance of both COTAA and DOTAA for multiple mission critical scenarios. The results indicate that both COTAA and DOTAA achieve the goal of OTA in highly dynamic MCEs, with low processing time and communication overhead

Multi-Robot Auction Based Coordination

This dissertation studied the coordination problem for a Task Initiator (TI) with multiple ground stations (GSs). Each GS has a team of unmanned aerial vehicles (UAVs) that frequently collected data from a set of unattended ground sensors (UGSs) and delivered it to the source ground station (GS)

Learning executable models of physical social agent behavior

Issued as final reportNational Science Foundation (U.S.

Sequential Single-Cluster Auctions for Multi-Robot Task Allocation

This thesis studies task allocation in multi-robot teams operating in dynamic environments. The multi-robot task allocation problem is a complex NP-Complete optimisation problem with globally optimal solutions often difficult to find. Because of this, the rapid generation of near optimal solutions to the problem that minimise task execution time and/or energy used by robots is highly desired. Our approach seeks to cluster together closely related tasks and then builds on existing distributed market-based auction architectures for distributing these sets of tasks among several autonomous robots. Dynamic environments introduce many challenges that are not found in closed systems. For instance, it is common for additional tasks to be inserted into a system after an initial solution to the task allocation problem is determined. Additionally, it is highly likely in long-term autonomous systems that individual robots may suffer some form of failure. The ability to alter plans to react to these types of challenges in a dynamic environment is required for the completion of all tasks. In our approach we allow the repeated formation and auctioning of task clusters with varying tasks. This allows us to react to and change the task allocation among robots during execution. Throughout this thesis we use empirical evaluation to study different approaches for forming clusters of tasks and the application of task clustering to distributed auctions for multi-robot task allocation problems. Our results show that allocating clusters of tasks to robots in solving these types of problems is a fast and effective method and produces near optimal solutions

Cyber defensive capacity and capability::A perspective from the financial sector of a small state

This thesis explores ways in which the financial sectors of small states are able todefend themselves against ever-growing cyber threats, as well as ways these states can improve their cyber defense capability in order to withstand current andfuture attacks. To date, the context of small states in general is understudied. This study presents the challenges faced by financial sectors in small states with regard to withstanding cyberattacks. This study applies a mixed method approach through the use of various surveys, brainstorming sessions with financial sector focus groups, interviews with critical infrastructure stakeholders, a literature review, a comparative analysis of secondary data and a theoretical narrative review. The findings suggest that, for the Aruban financial sector, compliance is important, as with minimal drivers, precautionary behavior is significant. Countermeasures of formal, informal, and technical controls need to be in place. This study indicates the view that defending a small state such as Aruba is challenging, yet enough economic indicators indicate it not being outside the realm of possibility. On a theoretical level, this thesis proposes a conceptual “whole-of-cyber” model inspired by military science and the VSM (Viable Systems Model). The concept of fighting power components and governance S4 function form cyber defensive capacity’s shield and capability. The “whole-of-cyber” approach may be a good way to compensate for the lack of resources of small states. Collaboration may be an only out, as the fastest-growing need will be for advanced IT skillsets