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Human Interfaces for Cooperative Control of Multiple Vehicle SystemsSun, Jisang 20 March 2006 (has links) (PDF)
This thesis presents a human interface which helps users efficiently allocate multiple unmanned ground vehicles (UGVs) cooperating to accomplish timing-sensitive missions in an urban environment. The urban environment consists of obstacles and a hazardous region. The obstacles represent a "no-go zone" while the hazardous region represents a high-risk area. The main object of this problem is to minimize the team operational cost while satisfying timing constraints. Operational costs for individual vehicles are based on risk and power consumption, and are calculated using path length and vehicle velocity. In this thesis, three types of timing constraints are considered: simultaneous arrival, tight sequential arrival, and loose sequential arrival. Coordination variables and functions are the strategy by which both temporal and spatial information is used to achieve cooperative timing at a minimum cost. Specifically, coordination variables and functions are used to plan trajectories for a team of UGVs that satisfy timing constraints. The importance of properly representing information to users, allowing them to make efficient decisions, is also discussed. Four different control interfaces (temporal, spatial, cost, and coordination variable/function control) were tested. A full factorial design of experiments was performed with response time, workload, and quality of decision as metrics used to evaluate the quality and effectiveness of each interface. Based on the results of this experiment, a final graphical user interface (GUI) was designed and is described. It incorporates a combination of coordination variable/function control and cost control. This GUI is capable of planning paths for vehicles based on cooperative timing constraints and enables users to make high quality decisions in deploying a group of vehicles.
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Three Enabling Technologies for Vision-Based, Forest-Fire Perimeter Surveillance Using Multiple Unmanned Aerial SystemsHolt, Ryan S. 21 June 2007 (has links) (PDF)
The ability to gather and process information regarding the condition of forest fires is essential to cost-effective, safe, and efficient fire fighting. Advances in sensory and autopilot technology have made miniature unmanned aerial systems (UASs) an important tool in the acquisition of information. This thesis addresses some of the challenges faced when employing UASs for forest-fire perimeter surveillance; namely, perimeter tracking, cooperative perimeter surveillance, and path planning. Solutions to the first two issues are presented and a method for understanding path planning within the context of a forest-fire environment is demonstrated. Both simulation and hardware results are provided for each solution.
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Multi-robot coordination and planning with human-in-the-loop under STL specifications : Centralized and distributed frameworks / Multi-robotkoordination och planering med mänsklig interaktion under STL-specifikationer : Centraliserade och distribuerade ramverkZhang, Yixiao January 2023 (has links)
Recent urbanization and industrialization have brought tremendous pressure and challenges to modern autonomous systems. When considering multiple complex tasks, cooperation and coordination between multiple agents can improve efficiency in a system. In real-world applications, multi-agent systems (MAS) are widely used in various fields, such as robotics, unmanned aerial systems, autonomous vehicles, distributed sensor networks, etc. Unlike traditional MAS systems based on pre-defined algorithms and rules, a special human-in-loop (HIL) based MAS involves human interactions to enhance the system’s adaptability for special scenarios, as well as apply human preferences for robot control. However, existing HIL strategies are primarily based on human involvement at a low level, such as mixed-initiative control and mixed-agent scenarios with both human-driven and intelligent robots. There are fewer investigations on applying HIL in high-level coordination. In particular, designing a coordination strategy for multi-task multi-agent scenarios, which can also deal with real-time human commands, will be one of the key topics of this Master’s thesis project. In this thesis work, different kinds of tasks described by signal temporal logic (STL) are created for agents, which can be enforced by control barrier function (CBF) constraints. Both centralized and distributed frameworks are designed for agent coordination. In detail, the centralized strategy is developed for machine-to-infrastructure (M2I) communication, by using the nonlinear model predictive control (NMPC) method to obtain collision-free trajectories. The distributed strategy utilizing graph theory is proposed for machine-to-machine (M2M), in order to reduce computation time by offloading. Most importantly, a HIL model is generated for both frameworks to apply online human commands to the coordination, with a novel task allocation protocol. Simulations and experiments are carried out on both Matlab and Python-based ROS simulators, to show that proposed frameworks can achieve obvious performance advantages in safety, smoothness, and stability for task completion. Numerical results are provided to validate the feasibility and applicability of our algorithms. / Den senaste urbaniseringen och industrialiseringen har medfört enormt tryck och utmaningar för moderna autonoma system. Vid beaktande av flera komplexa uppgifter kan samarbete och samordning mellan flera agenter förbättra effektiviteten i ett system. I verkliga tillämpningar används multiagent-system (MAS) i stor utsträckning inom olika områden, såsom robotik, obemannade luftfarkoster, autonoma fordon, distribuerade sensorsystem etc. Till skillnad från traditionella MAS-system baserade på fördefinierade algoritmer och regler, innebär ett särskilt människa-i-loop (HIL)-baserat MAS mänsklig interaktion för att förbättra systemets anpassningsförmåga till speciella scenarier samt anpassa mänskliga preferenser för robotstyrning. Emellertid är befintliga HIL-strategier främst baserade på mänsklig inblandning på en låg nivå, såsom mixad-initiativkontroll och mixade agentscenarier med både människa-drivna och intelligenta robotar. Det finns färre undersökningar om att tillämpa HIL på högnivåkoordination. Särskilt att utforma en koordineringsstrategi för fleruppgiftsfleragent-scenarier, som också kan hantera mänskliga kommandon i realtid, kommer att vara ett av huvudämnena för detta masterprojekt. I detta examensarbete skapas olika typer av uppgifter beskrivna av signaltemporallogik (STL) för agenter, som kan upprätthållas genom styrbarriärfunktions (CBF) -begränsningar. Både centraliserade och distribuerade ramverk utformas för agentkoordination. Mer specifikt utvecklas den centraliserade strategin för maskin-till-infrastruktur (M2I)-kommunikation genom att använda icke-linjär modellprediktiv reglering (NMPC) för att erhålla kollisionsfria trajektorier. Den distribuerade strategin med användning av grafteori föreslås för maskin-till-maskin (M2M) för att minska beräkningstiden genom avlastning. Viktigast av allt genereras en HIL-modell för båda ramverken för att tillämpa online-mänskliga kommandon på koordinationen med en ny protokoll för uppgiftstilldelning. Simuleringar och experiment utförs på både Matlab och Python-baserade ROS-simulatorer för att visa att de föreslagna ramverken kan uppnå tydliga prestandafördelar när det gäller säkerhet, smidighet och stabilitet för uppgiftsslutförande. Numeriska resultat presenteras för att validera genomförbarheten och tillämpligheten hos våra algoritmer.
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Autonomous Landing of a UAV ona Moving UGV Platform using Cooperative MPCGaregnani, Luca January 2021 (has links)
Cooperative control of autonomous multi-agent systems is a research areawhich is getting significant attention in recent years. Multi-agent systemsallow for a broad spectrum of applications and cooperation can increasetheir flexibility, efficiency and robustness to changes in external constraintsand disturbances. Focusing on autonomous vehicles, examples of possibleapplications of cooperative multi-agent systems include search and rescuemissions, autonomous delivery and performing of emergency landings.The purpose of the thesis is to develop and implement a cooperativerendezvous algorithm based on model predictive control and apply it to theproblem of autonomous landing in an indoor setting. The agents involved in themaneuver are a quadcopter and a ground carrier. The two agents cooperativelynegotiate on the optimal location for the touchdown taking also into accountrelevant spatial constraints and, if necessary, update that location, also referredto as rendezvous point, in real-time throughout the maneuver.The algorithm is first tested and validated in a simulated environment andfinally on the physical system during real-time operations.Additional scenarios are tested in the simulated environment in order tofurther inspect the potential capabilities of the developed algorithm. Thoseadditional simulations analyse how the algorithm behaves when a constantlateral wind influences the quadcopter; when the controllers operate at areduced frequency; and when the quadcopter is affected by an external Gaussiandisturbance.The developed algorithm proved to be suitable for the purpose, allowingthe agents to perform the desired maneuver in a relatively short time and witha high degree of precision. / Kooperativ reglering av autonoma fleragentsystem är ett forskningsområdesom har fått stor uppmärksamhet de senaste åren. Fleragentsystem möjliggörett brett spektrum av applikationer samtidigt som kooperation kan öka derasflexibilitet, effektivitet och robusthet mot förändringar i yttre begränsningar ochstörningar. Med fokus på autonoma fordon, exempel på möjliga tillämpningarav kooperativa fleragentsystem inkluderar sök- och räddningsuppdrag, autonomleverans och utförande av nödlandningar.Syftet med rapporten är att utveckla och implementera en kooperativrendezvous -algoritm baserad på modellprediktiv reglerteknik samt att tillämpaden för att utföra en inomhus autonom landning. I vår uppställning beståragenterna i manövern av en quadcopter och en markbärare. De två agenternaförhandlar samarbetsvilligt om den optimala platsen för landning samtidigtsom de beaktar relevanta rumsliga begränsningar och uppdaterar vid behovden platsen i realtid under hela manövern.Algoritmen testas och valideras först i en simulerad miljö och slutligen pådet fysiska systemet under en realtidsmiljö.Ytterligare scenarier testas i den simulerade miljön för att bortre inspekterapotentialen hos den utvecklade algoritmen. Dessa extra simuleringar illustrerarhur algoritmen beter sig när en konstant sidovind påverkar quadcoptern; närstyrenheterna arbetar med reducerad frekvens; och när quadcoptern påverkasav en yttre Gaussisk störning.Den utvecklade algoritmen visade sig vara lämplig för ändamålet, vilketgjorde att agenterna kunde utföra önskad manöver på relativt kort tid och medhög precision.
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