CRIKEY! ― It's co-ordination in temporal planning

Temporal planning contains aspects of both planning and scheduling. Many temporal planners assume a loose coupling between these two sub-problems in the form of "blackbox" durative actions, where the state of the world is not known during the action's execution. This reduces the size of the search space and so simplifies the temporal planning problem, restricting what can be modelled. In particular, the simplification makes it impossible to model co-ordination, where actions must be executed concurrently to achieve a desired effect. Coordination results from logical and temporal constraints that must both be met, and for this reason, the planner and scheduler must communicate in order to find a valid temporal plan. This communication effectively increases the size of the search space, so must be done intelligently and as little as possible to limit this increase. This thesis contributes a comprehensive analysis of where temporal constraints appear in temporal planning problems. It introduces the notions of minimum and maximum temporal constraints, and with these isolates where the planning and scheduling are coupled together tightly, in the form of co-ordination, it characterises this with the new concepts of envelopes and contents. A new temporal planner written, called СRIKЕҮ, uses this theory to solve temporal problems involving co-ordination that other planners are unable to solve. However, it does this intelligently, using this theory to minimise the communication between the sub-solvers, and so does not expand the search space unnecessarily. The novel search space that CRIKEY uses docs not specify the timings of future events and this allows for the handling of duration inequalities, which again, few other temporal planners are able to solve. Results presented show СRIKЕҮ to be a competitive planner, whilst not making the same simplifying assumptions that other temporal planners make as to the nature of temporal planning problems

On Provably Safe and Live Multirobot Coordination With Online Goal Posting

A standing challenge in multirobot systems is to realize safe and efficient motion planning and coordination methods that are capable of accounting for uncertainties and contingencies. The challenge is rendered harder by the fact that robots may be heterogeneous and that their plans may be posted asynchronously. Most existing approaches require constraints on the infrastructure or unrealistic assumptions on robot models. In this article, we propose a centralized, loosely-coupled supervisory controller that overcomes these limitations. The approach responds to newly posed constraints and uncertainties during trajectory execution, ensuring at all times that planned robot trajectories remain kinodynamically feasible, that the fleet is in a safe state, and that there are no deadlocks or livelocks. This is achieved without the need for hand-coded rules, fixed robot priorities, or environment modification. We formally state all relevant properties of robot behavior in the most general terms possible, without assuming particular robot models or environments, and provide both formal and empirical proof that the proposed fleet control algorithms guarantee safety and liveness

Inexactness of the Hydro-Thermal Coordination Semidefinite Relaxation

Hydro-thermal coordination is the problem of determining the optimal economic dispatch of hydro and thermal power plants over time. The physics of hydroelectricity generation is commonly simplified in the literature to account for its fundamentally nonlinear nature. Advances in convex relaxation theory have allowed the advent of Shor's semidefinite programming (SDP) relaxations of quadratic models of the problem. This paper shows how a recently published SDP relaxation is only exact if a very strict condition regarding turbine efficiency is observed, failing otherwise. It further proposes the use of a set of convex envelopes as a strategy to successfully obtain a stricter lower bound of the optimal solution. This strategy is combined with a standard iterative convex-concave procedure to recover a stationary point of the original non-convex problem.Comment: Submitted to IEEE PES General Meeting 201

Optimising Flexibility of Temporal Problems with Uncertainty

Temporal networks have been applied in many autonomous systems. In real situations, we cannot ignore the uncertain factors when using those autonomous systems. Achieving robust schedules and temporal plans by optimising flexibility to tackle the uncertainty is the motivation of the thesis. This thesis focuses on the optimisation problems of temporal networks with uncertainty and controllable options in the field of Artificial Intelligence Planning and Scheduling. The goal of this thesis is to construct flexibility and robustness metrics for temporal networks under the constraints of different levels of controllability. Furthermore, optimising flexibility for temporal plans and schedules to achieve robust solutions with flexible executions. When solving temporal problems with uncertainty, postponing decisions according to the observations of uncertain events enables flexible strategies as the solutions instead of fixed schedules or plans. Among the three levels of controllability of the Simple Temporal Problem with Uncertainty (STPU), a problem is dynamically controllable if there is a successful dynamic strategy such that every decision in it is made according to the observations of past events. In the thesis, we make the following contributions. (1) We introduce an optimisation model for STPU based on the existing dynamic controllability checking algorithms. Some flexibility and robustness measures are introduced based on the model. (2) We extend the definition and verification algorithm of dynamic controllability to temporal problems with controllable discrete variables and uncertainty, which is called Controllable Conditional Temporal Problems with Uncertainty (CCTPU). An entirely dynamically controllable strategy of CCTPU consists of both temporal scheduling and variable assignments being dynamically decided, which maximize the flexibility of the execution. (3) We introduce optimisation models of CCTPU under fully dynamic controllability. The optimisation models aim to answer the questions how flexible, robust or controllable a schedule or temporal plan is. The experiments show that making decisions dynamically can achieve better objective values than doing statically. The thesis also contributes to the field of AI planning and scheduling by introducing robustness metrics of temporal networks, proposing an envelope-based algorithm that can check dynamic controllability of temporal networks with uncertainty and controllable discrete decisions, evaluating improvements from making decisions strongly controllable to temporally dynamically controllable and fully dynamically controllable and comparing the runtime of different implementations to present the scalability of dynamically controllable strategies

A loosely-coupled approach for multi-robot coordination, motion planning and control

Deploying fleets of autonomous robots in real-world applications requires addressing three problems: motion planning, coordination, and control. Application-specific features of the environment and robots often narrow down the possible motion planning and control methods that can be used. This paper proposes a lightweight coordination method that implements a high-level controller for a fleet of potentially heterogeneous robots. Very few assumptions are made on robot controllers, which are required only to be able to accept set point updates and to report their current state. The approach can be used with any motion planning method for computing kinematically-feasible paths. Coordination uses heuristics to update priorities while robots are in motion, and a simple model of robot dynamics to guarantee dynamic feasibility. The approach avoids a priori discretization of the environment or of robot paths, allowing robots to "follow each other" through critical sections. We validate the method formally and experimentally with different motion planners and robot controllers, in simulation and with real robots

Hävittäjälentokonelaivueen puolustuskyvyn analysointi simuloinnin avulla

This thesis studies the air defense capability of a fleet of military aircraft with respect to the attributes of force fulfillment and engagement frontier. The force fulfillment measures the ability to deploy a number of assets consecutively to an operation area for a prolonged period of time. The analyzing of the force fulfillment is essentially an allocation and scheduling problem. Each asset has a limited amount of fuel, and the asset needs to return to base before fuel runs out. A replacing asset is needed at the operation area when the previous asset needs to return to base. After being refueled the asset is allocated again to some operation area. The replacement is done just in time whenever possible, and this sets up a problem where the flight schedules of assets are dictated by earlier allocations bit by bit. The engagement frontier measures the capability to counter the first enemy attack using assets on alert and stationed at the bases. The airspace may be restricted by zones which must be circumvented. The assets carry missiles which reach far ahead of the asset and thus form a missile envelope. It might be wanted that the assets are gathered such that the engagement would happen with two assets present. The engagement frontier is formed where the opposing assets engage at the earliest. This thesis presents two simulation models; one for computing measures of the force fulfillment, and one for computing the engagement frontier. The first model includes an allocation algorithm that uses a heuristic for controlling the use of ground resources and for allocating the aircraft to the operation areas. The allocation algorithm produces a flight and maintenance schedule from which the measures of the force fulfillment can be deduced. The simulation model for computing the engagement frontier is built upon network optimization where the earliest possible arrival times to each node in a grid are calculated. The simulation models enable versatile analyses, e.g., the force fulfillment over a spatial area. With the simulation model for the engagement frontier, the use of assets and their standby times can be determined such that the desired engagement frontier is achieved without excessive high alert.Tässä diplomityössä tarkastellaan lentokoneosastojen tuottaman ilmapuolustuskyvyn mittaamista kahdella eri attribuutilla; voiman riittävyydellä ja kohtaustasalla. Voiman riittävyys mittaa kykyä ylläpitää tietty määrä lentokoneosastoja peräjälkeen toiminta-alueella pitkällä aikavälillä. Voiman riittävyyden tarkastelu on pohjimmiltaan allokaatio ja aikataulutustehtävä. Kullakin lentokoneosastolla on rajallinen määrä polttoainetta, ja osaston täytyy palata tukikohtaan polttoaineen loppuessa. Korvaava osasto tarvitaan toiminta-alueelle, kun edellisen osaston täytyy kääntyä toiminta-alueelta pois. Tankkauksen jälkeen osasto allokoidaan jälleen jollekin toiminta-alueelle. Vaihto tapahtuu milloin mahdollista, ja tämä johtaa tehtävään, jossa osaston lennätysaikataulu määräytyy aiempien allokaatioiden perusteella pala palalta. Kohtaustasa mittaa kykyä vastata ensihyökkäykseen lentokoneosastoilla, jotka ovat tukikohdissa määrätyssä valmiudessa. Ilmatilaa saattaa rajoittaa lentokieltoalueet, jotka täytyy kiertää. Lentokoneosastoilla on käytössään ilmatorjuntaohjuksia, jotka muodostavat ohjuskuoren lentokoneen edelle. Voimaa voidaan keskittää siten, että torjuntaan halutaan käyttää kahta lentokoneosastoa. Kohtaustasa muodostuu siihen kohtaan, jossa vastakkaisten osapuolien osastot kohtaavat aikaisimmillaan. Tässä työssä esitellään kaksi erillistä simulaatiomallia; yksi voiman riittävyyden laskentaan, ja toinen kohtaustasan laskentaan. Voiman riittävyyden simulaatiomalli sisältää allokaatioalgoritmin, joka käyttää heuristiikkaa tukikohtaresurssien hallintaan ja lentokoneiden allokointiin toiminta-alueille. Allokaatioalgoritmi tuottaa lennätys- ja huoltoaikataulun, jonka perusteella voidaan mitata voiman riittävyyttä. Simulaatiomalli kohtaustasan laskentaan perustuu verkko-optimointitehtävän ratkaisemiseen, jossa lasketaan aikaisin mahdollinen saapumisaika kuhunkin hilapisteen solmuun. Simulointimallit mahdollistavat erilaisia analyysejä liittyen esimerkiksi voiman riittävyyteen maantieteellisellä alueella. Kohtaustasan simulaatiomallin avulla pystytään määräämään lentokoneosastojen valmiuksia siten, että haluttu kohtaustasa saavutetaan, mutta osastot eivät joudu turhaan olemaan korkeassa valmiudessa

Computational and Near-Optimal Trade-Offs in Renewable Electricity System Modelling

In the decades to come, the European electricity system must undergo an unprecedented transformation to avert the devastating impacts of climate change. To devise various possibilities for achieving a sustainable yet cost-efficient system, in the thesis at hand, we solve large optimisation problems that coordinate the siting of generation, storage and transmission capacities. Thereby, it is critical to capture the weather-dependent variability of wind and solar power as well as transmission bottlenecks. In addition to modelling at high spatial and temporal resolution, this requires a detailed representation of the electricity grid. However, since the resulting computational challenges limit what can be investigated, compromises on model accuracy must be made, and methods from informatics become increasingly relevant to formulate models efficiently and to compute many scenarios. The first part of the thesis is concerned with justifying such trade-offs between model detail and solving times. The main research question is how to circumvent some of the challenging non-convexities introduced by transmission network representations in joint capacity expansion models while still capturing the core grid physics. We first examine tractable linear approximations of power flow and transmission losses. Subsequently, we develop an efficient reformulation of the discrete transmission expansion planning (TEP) problem based on a cycle decomposition of the network graph, which conveniently also accommodates grid synchronisation options. Because discrete investment decisions aggravate the problem\u27s complexity, we also cover simplifying heuristics that make use of sequential linear programming (SLP) and retrospective discretisation techniques. In the second half, we investigate other trade-offs, namely between least-cost and near-optimal solutions. We systematically explore broad ranges of technologically diverse system configurations that are viable without compromising the system\u27s overall cost-effectiveness. For example, we present solutions that avoid installing onshore wind turbines, bypass new overhead transmission lines, or feature a more regionally balanced distribution of generation capacities. Such alternative designs may be more widely socially accepted, and, thus, knowing about these degrees of freedom is highly policy-relevant. The method we employ to span the space of near-optimal solutions is related to modelling-to-generate-alternatives, a variant of multi-objective optimisation. The robustness of our results is further strengthened by considering technology cost uncertainties. To efficiently sweep the cost parameter space, we leverage multi-fidelity surrogate modelling techniques using sparse polynomial chaos expansion in combination with low-discrepancy sampling and extensive parallelisation on high-performance computing infrastructure