Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Multi-UAV network control through dynamic task allocation: Ensuring data-rate and bit-error-rate support

A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. The distributed algorithm uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes the team is able to optimize the use of agents to address the needs of dynamic complex missions. The framework is designed to consider realistic network communication dynamics including path loss, stochastic fading, and information routing. The planning strategy is shown to ensure that agents support both datarate and interconnectivity bit-error-rate requirements during task execution. System performance is characterized through experiments both in simulation and in outdoor flight testing with a team of three UAVs.Aurora Flight Sciences Corp. (Fellowship Program

BEHAVIORAL COMPOSITION FOR HETEROGENEOUS SWARMS

Research into swarm robotics has produced a robust library of swarm behaviors that excel at defined tasks such as flocking and area search, many of which have potential for application to a wide range of military problems. However, to be successfully applied to an operational environment, swarms must be flexible enough to achieve a wide array of specific objectives and usable enough to be configured and employed by lay operators. This research explored the use of the Mission-based Architecture for Swarm Composability (MASC) to develop mission-specific tactics as compositions of more general, reusable plays for use with the Advanced Robotic Systems Engineering Laboratory (ARSENL) swarm system. Three tactics were developed to conduct autonomous search of a geographic area and investigation of generated contacts of interest. The tactics were tested in live-flight and virtual environment experiments and compared to a preexisting monolithic behavior implementation completing the same task. Measures of performance were defined and observed that verified the effectiveness of solutions and confirmed the advantages that composition provides with respect to reusability and rapid development of increasingly complex behaviors.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

Auction-based Task Allocation for Safe and Energy Efficient UAS Parcel Transportation

In this paper, two greedy auction-based algorithms are proposed for the allocation of heterogeneous tasks to a heterogeneous fleet of UAVs. The tasks set is composed of parcel delivery tasks and charge tasks, the latter to guarantee service persistency. An optimization problem is solved by each agent to determine its bid for each task. When considering delivery tasks, the bidder aims at minimizing the energy consumption, while the minimization of the flight time is adopted for charge tasks bids. The algorithms include a path planner that computes the minimum risk path for each task-UAV bid exploiting a 2D risk map of the operational area, defined in an urban environment. Each solution approach is implemented by means of two auction strategies: single-item and multiple-item. Considerations about complexity and efficiency of the algorithms are drawn from Monte Carlo simulations

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

무인기 공격편대군 운용을 위한 CBBA 기반 분산형 임무할당

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 항공우주공학과, 2021. 2. 김유단.In this thesis, a distributed task assignment algorithm is proposed for the air strike package mission of heterogeneous unmanned areal vehicles(UAVs) based on the consensus-based bundle algorithm(CBBA). Air Strike Package mission can be modeled as a task assignment problem that multiple UAVs perform various actions by their respective roles. The UAVs participating in the operation consist of strike force for the destruction of enemys ground targets, SEAD(Suppression of Enemy Air Defense) for the neutralization of enemy's air defense system, and counter-air for the protection of UAV from the enemys air-to-air threat. In this study, a distributed task assignment algorithm considering path-planning in presence of SAM(Surface-to-Air Missile) and terrain obstacle is proposed, which is applied to complex air strike package mission based on the characteristics of the ground target and various operational functions of the UAVs. Numerical simulations are performed to demonstrate the effectiveness and applicability of the proposed method.국내ㆍ외에서 다양한 임무할당 연구가 진행 중이나 실제 공중작전에 핵심전력인 공격편대군의 작전환경을 고려한 연구는 매우 제한적이다. 무인기 기술이 고도화되어 군사적 운용이 현실화된 오늘날, 세계 최고의 군사 강국인 미국은 2025 ~ 30년을 목표로 무인기 공격편대군을 개발하 려는 노력을 경주하고 있다. 본 논문에서는 CBBA(Consensus Based Bundle Algorithm)을 기반 으로 무인기 공격편대군의 임무수행을 위한 분산형 임무할당 알고리듬을 제시한다. 공격편대군 임무는 이종의 무인기에 각자의 역할에 부합하도 록 임무를 부여하는 임무할당 문제로 정의할 수 있다. 작전에 참가하는 무인기는 적의 지상표적을 타격하는 Strike와 대공 방어망인 SAM (Surface-to-Air Missile)을 파괴하는 SEAD(Suppression of Enemy Air Defense), 그리고 적기의 공대공 위협으로부터 아군을 보호하는 Counter-Air 전력으로 구성된다. 본 논문에서는 최단경로생성 알고리듬 과 CBBA 알고리듬을 이용하여 SAM 위협과 지형장애물이 존재하는 상 황에서 공격해야 할 지상표적의 특성과 아군 무인기의 다양한 작전성능 을 고려한 표적군 기반의 분산형 임무할당 기법을 제안한다. 또한, 다양 한 위협이 동시에 적용된 복합 상황에서의 공격편대군 임무에 제안한 알 고리듬을 적용하고, 수치 시뮬레이션을 통하여 적용된 기법의 성능과 운 용 가능성에 대해 검토한다. 본 연구에서는 무인기로 구성된 공격편대군을 가정하여 다양한 복합 환경에서의 임무수행 가능성을 알고리듬을 통해 검증하는데 중점을 두었 다. 향후 무인기를 활용한 군사작전과 실제적인 전장환경을 고려한 연구 가 더욱 확대될 것이다. 이러한 연구들을 통해, 오늘날 국외 기술에 기반한 전투기 · 정밀무장 중심의 공중작전이 대한민국의 과학기술과 무인기에 기반한 새로운 무기체계 중심으로 전환하는 계기가 될 것으로 기대된다.Table of Contents Abstract ············································································································· i Table of Contents ························································································ iii List of Tables ································································································· v List of Figures ······························································································ vi List of Abbreviations ················································································· vii CHAPTER 1. INTRODUCTION 1 1.1 Background ···································································································· 1 1.2 Related Research ························································································· 4 1.3 Contributions ································································································· 6 1.4 Thesis Organization ···················································································· 7 CHAPTER 2. Problem Statement 8 2.1 Employment of the Air Strike Package ·············································· 8 2.1.1 ATO cycle ··························································································· 9 2.1.2 Limitations of the preplanned air strike package ············ 10 2.1.3 Real-time applicable UAV air strike package ····················· 11 2.2 Mission Environment of the Air Strike Package ·························· 13 2.3 Task Assignment Problem ····································································· 15 2.4 CBBA Method ······························································································ 17 2.5 Distributed Task Assignment Algorithm Considering Selective Strike ·························································································· 20 CHAPTER 3. Three Staged UAV Air Strike Package Task Assignment Model 3.1 Model of Target Group-Based Air Strike Package in Offline Environments (Stage 1) ···························································· 24 3.1.1 Assumptions ··················································································· 24 3.1.2 Target group-based CBBA algorithm ··································· 26 3.2 Model of Air Strike Package with Additional SEAD and Counter-Air in an Online Environment (Stage 2) ························ 27 3.2.1 Assumptions ····················································································· 27 3.2.2 Online algorithm for a complete-form of air strike package combination ··············································· 28 3.3 Model of Comprehensive Air Strike Package mission in Complex Threat Situations (Stage 3) ················································· 31 CHAPTER 4. Numerical Simulation 32 4.1 Battlefield Environment ··········································································· 32 4.2 Simulation results ····················································································· 35 4.2.1 Stage 1 simulation result ························································· 35 4.2.2 Stage 2 simulation result ························································· 38 4.2.3 Stage 3 simulation result ························································· 41 CHAPTER 5. Conclusions 45 5.1 Concluding Remarks ················································································ 45 5.2 Future Work ································································································ 46 Reference 48 국문초록 52 감사의 글 54Maste

Solar-powered aquaponics prototype as sustainable approach for food production

This paper presents the establishment of a solar-powered aquaponics prototype as a sustainable, cost effective and environmentally sound approach for food production. In this study, a prototype bench top aquaponics rig with an integrated 20 W solar panel were fabricated for the cultivation of red Hybrid Tilapia (Oreochromis spp.) and leaf mustard (Brassica juncea). The size of the fish tank is about 29.5L and serves as the base for the setup. Additionally, the hydroponic grower compartment (0.45 m (L) � 0.32 m (W) � 0.13 m (H)) was stacked on top of the fish tank and was filled with LECA media bed for the plant growth. Two important operating parameters were studied. First, the amount of energy produced by the solar panel and the energy consumption by the water pump used in the setup. Secondly, the resultant effects from fish cultivation and plants growth on the water qualities and nitrification effi�ciency of the aquaponics unit. The aquaponics unit was operated for a month and the values of pH, tem�perature, and ammonia level were measured to be within the range of 6.4–7.2, 27.1–31.7 �C, and 1 mg�L�1 , respectively. Survival rate for fish was about 75% with specific growth rate (SGR) of 3.75% per day and food conversion ratio of about 1.15. A slight nutrient deficiency was evident and plants showed a healthy growth with height gain as high as 5 cm was achieved. Despite raining season, our data shows that the energy produced via 20 W solar panel enabled the unit to run at night without depending on local electricity for nearly two hours. Clearly, a larger solar panel is needed for longer operation. Nevertheless, the study has proven the potential of operating a low cost aquaponics setup using renew�able energy for a sustainable food production method