Tracking Multiple Fast Targets With Swarms: Interplay Between Social Interaction and Agent Memory

The task of searching for and tracking of multiple targets is a challenging one. However, most works in this area do not consider evasive targets that move faster than the agents comprising the multi-robot system. This is due to the assumption that the movement patterns of such targets, combined with their excessive speed, would make the task nearly impossible to accomplish. In this work, we show that this is not the case and we propose a decentralized search and tracking strategy in which the level of exploration and exploitation carried out by the swarm is adjustable. By tuning a swarm's exploration and exploitation dynamics, we demonstrate that there exists an optimal balance between the level of exploration and exploitation performed. This optimum maximizes its tracking performance and changes depending on the number of targets and the targets' movement profiles. We also show that the use of agent-based memory is critical in enabling the tracking of an evasive target. The obtained simulation results are validated through experimental tests with a decentralized swarm of six robots tracking a virtual fast-moving target

Optimal Robotic Path Planning Using Intlligents Search Algorithms

This investigation investigates the application of Adjusted Fuzzy Molecule Swarm Optimization (FPSO) to the versatile robot route issue in arrange to decide the briefest conceivable course with the least time required to travel from a beginning area to a goal area in a deterrent working zone. MPSO is being created in this ponder to progress the capability of customized calculations for a worldwide course. The proposed calculations decipher the environment outline spoken to by the framework show and develop an idea or nearly ideal collision-free way. Reenactment tests appear the viability of the most recent organized calculation for portable robot course arranging. The programs are composed in MATLAB R2019a and run on 2.65 GHz Intel Center i5 and 7 GB Smash computers. Changes proposed in MPSO and cuckoo look calculation fundamentally point to resolve the untimely merging issue related to the beginning PSO. A mistake calculate is demonstrated within the MPSO to guarantee the meeting of the PSO. FPSO points to handle another issue which is the populace may incorporate a few infeasible ways; an updated strategy is tired the FPSO to fathom the issue of the infeasible street. The discoveries illustrate that this calculation has huge potential to fathom the course arranging with satisfactory comes about in terms of decreasing remove and time for execution

협업 로봇을 위한 서비스 기반과 모델 기반의 소프트웨어 개발 방법론

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 하순회.가까운 미래에는 다양한 로봇이 다양한 분야에서 하나의 임무를 협력하여 수행하는 모습은 흔히 볼 수 있게 될 것이다. 그러나 실제로 이러한 모습이 실현되기에는 두 가지의 어려움이 있다. 먼저 로봇을 운용하기 위한 소프트웨어를 명세하는 기존 연구들은 대부분 개발자가 로봇의 하드웨어와 소프트웨어에 대한 지식을 알고 있는 것을 가정하고 있다. 그래서 로봇이나 컴퓨터에 대한 지식이 없는 사용자들이 여러 대의 로봇이 협력하는 시나리오를 작성하기는 쉽지 않다. 또한, 로봇의 소프트웨어를 개발할 때 로봇의 하드웨어의 특성과 관련이 깊어서, 다양한 로봇의 소프트웨어를 개발하는 것도 간단하지 않다. 본 논문에서는 상위 수준의 미션 명세와 로봇의 행위 프로그래밍으로 나누어 새로운 소프트웨어 개발 프레임워크를 제안한다. 또한, 본 프레임워크는 크기가 작은 로봇부터 계산 능력이 충분한 로봇들이 서로 군집을 이루어 미션을 수행할 수 있도록 지원한다. 본 연구에서는 로봇의 하드웨어나 소프트웨어에 대한 지식이 부족한 사용자도 로봇의 동작을 상위 수준에서 명세할 수 있는 스크립트 언어를 제안한다. 제안하는 언어는 기존의 스크립트 언어에서는 지원하지 않는 네 가지의 기능인 팀의 구성, 각 팀의 서비스 기반 프로그래밍, 동적으로 모드 변경, 다중 작업(멀티 태스킹)을 지원한다. 우선 로봇은 팀으로 그룹 지을 수 있고, 로봇이 수행할 수 있는 기능을 서비스 단위로 추상화하여 새로운 복합 서비스를 명세할 수 있다. 또한 로봇의 멀티 태스킹을 위해 '플랜' 이라는 개념을 도입하였고, 복합 서비스 내에서 이벤트를 발생시켜서 동적으로 모드가 변환할 수 있도록 하였다. 나아가 여러 로봇의 협력이 더욱 견고하고, 유연하고, 확장성을 높이기 위해, 군집 로봇을 운용할 때 로봇이 임무를 수행하는 도중에 문제가 생길 수 있으며, 상황에 따라 로봇을 동적으로 다른 행위를 수행할 수 있다고 가정한다. 이를 위해 동적으로도 팀을 구성할 수 있고, 여러 대의 로봇이 하나의 서비스를 수행하는 그룹 서비스를 지원하고, 일대 다 통신과 같은 새로운 기능을 스크립트 언어에 반영하였다. 따라서 확장된 상위 수준의 스크립트 언어는 비전문가도 다양한 유형의 협력 임무를 쉽게 명세할 수 있다. 로봇의 행위를 프로그래밍하기 위해 다양한 소프트웨어 개발 프레임워크가 연구되고 있다. 특히 재사용성과 확장성을 중점으로 둔 연구들이 최근 많이 사용되고 있지만, 대부분의 이들 연구는 리눅스 운영체제와 같이 많은 하드웨어 자원을 필요로 하는 운영체제를 가정하고 있다. 또한, 프로그램의 분석 및 성능 예측 등을 고려하지 않기 때문에, 자원 제약이 심한 크기가 작은 로봇의 소프트웨어를 개발하기에는 어렵다. 그래서 본 연구에서는 임베디드 소프트웨어를 설계할 때 쓰이는 정형적인 모델을 이용한다. 이 모델은 정적 분석과 성능 예측이 가능하지만, 로봇의 행위를 표현하기에는 제약이 있다. 그래서 본 논문에서 외부의 이벤트에 의해 수행 중간에 행위를 변경하는 로봇을 위해 유한 상태 머신 모델과 데이터 플로우 모델이 결합하여 동적 행위를 명세할 수 있는 확장된 모델을 적용한다. 그리고 딥러닝과 같이 계산량을 많이 필요로 하는 응용을 분석하기 위해, 루프 구조를 명시적으로 표현할 수 있는 모델을 제안한다. 마지막으로 여러 로봇의 협업 운용을 위해 로봇 사이에 공유되는 정보를 나타내기 위해 두 가지 모델을 사용한다. 먼저 중앙에서 공유 정보를 관리하기 위해 라이브러리 태스크라는 특별한 태스크를 통해 공유 정보를 나타낸다. 또한, 로봇이 자신의 정보를 가까운 로봇들과 공유하기 위해 멀티캐스팅을 위한 새로운 포트를 추가한다. 이렇게 확장된 정형적인 모델은 실제 로봇 코드로 자동 생성되어, 소프트웨어 설계 생산성 및 개발 효율성에 이점을 가진다. 비전문가가 명세한 스크립트 언어는 정형적인 태스크 모델로 변환하기 위해 중간 단계인 전략 단계를 추가하였다. 제안하는 방법론의 타당성을 검증하기 위해, 시뮬레이션과 여러 대의 실제 로봇을 이용한 협업하는 시나리오에 대해 실험을 진행하였다.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid. For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly. By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Contribution 7 1.3 Dissertation Organization 9 Chapter 2. Background and Existing Research 11 2.1 Terminologies 11 2.2 Robot Software Development Frameworks 25 2.3 Parallel Embedded Software Development Framework 31 Chapter 3. Overview of the SeMo Framework 41 3.1 Motivational Examples 45 Chapter 4. Robot Behavior Programming 47 4.1 Related works 48 4.2 Model-based Task Graph Specification for Individual Robots 56 4.3 Model-based Task Graph Specification for Cooperating Robots 70 4.4 Automatic Code Generation 74 4.5 Experiments 78 Chapter 5. High-level Mission Specification 81 5.1 Service-oriented Mission Specification 82 5.2 Strategy Description 93 5.3 Automatic Task Graph Generation 96 5.4 Related works 99 5.5 Experiments 104 Chapter 6. Conclusion 114 6.1 Future Research 116 Bibliography 118 Appendices 133 요약 158Docto

Multi-AUV Cooperative Target Hunting based on Improved Potential Field in a Surface-Water Environment

In this paper, target hunting aims to detect target and surround the detected target in a surface-water using Multiple Autonomous Underwater Vehicles (multi-AUV) in a given area. The main challenge in multi-AUV target hunting is the design of AUV\u27s motion path and coordination mechanism. To conduct the cooperative target hunting by multi-AUV in a surface-water environment, an integrated algorithm based on improved potential field (IPF) is proposed. First, a potential field function is established according to the information of the surface-water environment. Then, the dispersion degree, the homodromous degree, and district-difference degree are introduced to increase the cooperation of the multi-AUV system. Finally, the target hunting is solved by embedding the three kinds of degree into the potential field function. The simulation results show that the proposed approach is applicable and feasible for multi-AUV cooperative target hunting

Potential field based approach for multi-threat containment with cooperative robots

Target or threat containment is a process where a team of robots forms a particular formation around a target so that it gets engulfed in it. The robots under consideration run decentralized Swarm-Algorithms that make them cooperate with each other using their local sensing capabilities. Previous work in this area has dealt with robot formations and single threat containment where the target is either fixed or dynamic. In either case the same targets exist throughout the containment process. The challenge is to use such collaborative robots to contain multiple targets that appear at random times, and expire after some time. In order to accommodate multiple threats, an algorithm named MUltiple Threat Containment Algorithm (MUTCA), was proposed and analyzed in this work. Upon sensing threat occurrences, the robots split up and attempt to surround all threats with a circular formation. MUTCA is based on Quadratic Artificial Potential Functions (QAPF). Multiple QAPFs are carefully designed to allow robots to surround the threats evenly and to avoid collisions. Using MUTCA, each robot periodically and independently calculates the sum of forces acting at their location in order to decide its movements. This work investigates the capabilities and limitations of autonomous robots utilizing MUTCA via simulation using MAHESHDAS - a simulator developed to simulate autonomous robots. Application specific success rates were examined for robot sensor capabilities and parameters used by QAPF. Though working well for the tested scenarios, MUTCA has been found to be sensitive to application specifics and sensor errors. As no prior work has been found to solve such a multi-threat containment problem, this work reveals the issues involved with this challenging problem and analyzes the proposed novel and robust solution approach

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

Swarm Robotics

This study analyzes and designs the Swarm intelligence (SI) that Self-organizing migrating algorithm (SOMA) represents to solve industrial practice as well as academic optimization problems, and applies them to swarm robotics. Specifically, the characteristics of SOMA are clarified, shaping the basis for the analysis of SOMA's strengths and weaknesses for the release of SOMA T3A, SOMA Pareto, and iSOMA, with outstanding performance, confirmed by well-known test suites from IEEE CEC 2013, 2015, 2017, and 2019. Besides, the dynamic path planning problem for swarm robotics is handled by the proposed algorithms considered as a prime instance. The computational and simulation results on Matlab have proven the performance of the novel algorithms as well as the correctness of the obstacle avoidance method for mobile robots and drones. Furthermore, two out of the three proposed versions achieved the tie for 3rd (the same ranking with HyDE-DF) and 5th place in the 100-Digit Challenge at CEC 2019, GECCO 2019, and SEMCCO 2019 competition, something that any other version of SOMA has yet to do. They show promising possibilities that SOMA and SI algorithms offer.Tato práce se zabývá analýzou a vylepšením hejnové inteligence, kterou představuje samoorganizující se migrační algoritmus s možností využití v průmyslové praxi a se zaměřením na hejnovou robotiku. Je analyzován algoritmus SOMA, identifikovány silné a slabé stránky a navrženy nové verze SOMA jako SOMA T3A, SOMA Pareto, iSOMA s vynikajícím výkonem, potvrzeným známými testovacími sadami IEEE CEC 2013, 2015, 2017 a 2019. Tyto verze jsou pak aplikovány na problém s dynamickým plánováním dráhy pro hejnovou robotiku. Výsledky výpočtů a simulace v Matlabu prokázaly výkonnost nových algoritmů a správnost metody umožňující vyhýbání se překážkám u mobilních robotů a dronů. Kromě toho dvě ze tří navržených verzí dosáhly na 3. a 5. místo v soutěži 100-Digit Challenge na CEC 2019, GECCO 2019 a SEMCCO 2019, což je potvrzení navržených inovací. Práce tak demonstruje nejen vylepšení SOMA, ale i slibné možnosti hejnové inteligence.460 - Katedra informatikyvyhově