Daniele Magazzeni

A universal planning system for hybrid domains

Many real world problems involve hybrid systems, subject to (continuous) physical effects and controlled by (discrete) digital equipments. Indeed, many efforts are being made to extend the current planning systems and modelling languages to support such kind of domains. However, hybrid systems often present also a nonlinear behaviour and planning with continuous nonlinear change that is still a challenging issue.In this paper we present the UPMurphi tool, a universal planner based on the discretise and validate approach that is capable of reasoning with mixed discrete/continuous domains, fully respecting the semantics of PDDL+. Given an initial discretisation, the hybrid system is discretised and given as input to UPMurphi, which performs universal planning on such an approximated model and checks the correctness of the results. If the validation fails, the approach is repeated by appropriately refining the discretisation.To show the effectiveness of our approach, the paper presents two real hybrid domains where universal planning has been successfully performed using the UPMurphi tool.

AUV mission control via temporal planning

Underwater installations require regular inspection and maintenance. We are exploring the idea of performing these tasks using an autonomous underwater vehicle, achieving persistent autonomous behaviour in order to avoid the need for frequent human intervention. In this paper we consider one aspect of this problem, which is the construction of a suitable plan for a single inspection tour. In particular we generate a temporal plan that optimises the time taken to complete the inspection mission. We report on physical trials with the system at the Diver and ROV driver Training Center in Fort William, Scotland, discussing some of the lessons learned.

Plan-based policies for efficient multiple battery load management

Efficient use of multiple batteries is a practical problem with wide and growing application. The problem can be cast as a planning problem under uncertainty. We describe the approach we have adopted to modelling and solving this problem, seen as a Markov Decision Problem, building effective policies for battery switching in the face of stochastic load profiles. Our solution exploits and adapts several existing techniques: planning for deterministic mixed discrete-continuous problems and Monte Carlo sampling for policy learning. The paper describes the development of planning techniques to allow solution of the non-linear continuous dynamic models capturing the battery behaviours. This approach depends on carefully handled discretisation of the temporal dimension. The construction of policies is performed using a classification approach and this idea offers opportunities for wider exploitation in other problems. The approach and its generality are described in the paper. Application of the approach leads to construction of policies that, in simulation, significantly outperform those that are currently in use and the best published solutions to the battery management problem. We achieve solutions that achieve more than 99% efficiency in simulation compared with the theoretical limit and do so with far fewer battery switches than existing policies. Behaviour of physical batteries does not exactly match the simulated models for many reasons, so to confirm that our theoretical results can lead to real measured improvements in performance we also conduct and report experiments using a physical test system. These results demonstrate that we can obtain 5%-15% improvement in lifetimes in the case of a two battery system.

Automated Generation of Optimal Controllers through Model Checking Techniques

We present a methodology for the synthesis of controllers, which exploits (explicit) model checking techniques. That is, we can cope with the systematic exploration of a very large state space. This methodology can be applied to systems where other approaches fail. In particular, we can consider systems with an highly non-linear dynamics and lacking a uniform mathematical description (model). We can also consider situations where the required control action cannot be specified as a local action, and rather a kind of planning is required. Our methodology individuates first a raw optimal controller, then extends it to obtain a more robust one. A case study is presented which considers the well known truck-trailer obstacle avoidance parking problem, in a parking lot with obstacles on it. The complex non-linear dynamics of the truck-trailer system, within the presence of obstacles, makes the parking problem extremely hard. We show how, by our methodology, we can obtain optimal controllers with different degrees of robustness.

Toward persistent autonomous intervention in a subsea panel

Intervention autonomous underwater vehicles (I-AUVs) have the potential to open new avenues for the maintenance and monitoring of offshore subsea facilities in a cost-effective way. However, this requires challenging intervention operations to be carried out persistently, thus minimizing human supervision and ensuring a reliable vehicle behaviour under unexpected perturbances and failures. This paper describes a system to perform autonomous intervention—in particular valve-turning—using the concept of persistent autonomy. To achieve this goal, we build a framework that integrates different disciplines, involving mechatronics, localization, control, machine learning and planning techniques, bearing in mind robustness in the implementation of all of them. We present experiments in a water tank, conducted with Girona 500 I-AUV in the context of a multiple intervention mission. Results show how the vehicle sets several valve panel configurations throughout the experiment while handling different errors, either spontaneous or induced. Finally, we report the insights gained from our experience and we discuss the main aspects that must be matured and refined in order to promote the future development of intervention autonomous vehicles that can operate, persistently, in subsea facilities.

Visual extraction of information from web pages

In this paper we present a graphical software system that provides an automatic support to the extraction of information from web pages. The underlying extraction technique exploits the visual appearance of the information in the document, and is driven by the spatial relations occurring among the elements in the page. However, the usual information extraction modalities based on the web page structure can be used in our framework, too. The technique has been integrated within the Spatial Relation Query (SRQ) tool. The tool is provided with a graphical front-end which allows one to define and manage a library of spatial relations, and to use a SQL-like language for composing queries driven by these relations and by further semantic and graphical attributes.

RESOURCE -OPTIMAL PLANNING FOR AN AUTONOMOUS PLANETARY VEHICLE

Autonomous planetary vehicles, also known as rovers, are small autonomous vehicles equipped with a variety of sensors used to perform exploration and experiments on a planets surface. Rovers work in a partially unknown environment, with narrow energy/time/movement constraints and, typically, small computational resources that limit the complexity of on-line planning and scheduling, thus they represent a great challenge in the field of autonomous vehicles. Indeed, formal models for such vehicles usually involve hybrid systems with nonlinear dynamics, which are difficult to handle by most of the current planning algorithms and tools. Therefore, when offline planning of the vehicle activities is required, for example for rovers that operate without a continuous Earth supervision, such planning is often performed on simplified models that are not completely realistic. In this paper we show how the UPMurphi model checking based planning tool can be used to generate resource-optimal plans to control the engine of an autonomous planetary vehicle, working directly on its hybrid model and taking into account several safety constraints, thus achieving very accurate results.

Automated planning and scheduling

In the last fifteen years, AI planning and scheduling techniques have been characterized by an impressive increase of their performances in terms of size and complexity of the solutions produced. These improvements are related to the definition of new data structures which can efficiently encode and make explicit constraints that are only implicitly defined in problem formulation, and to the definition of heuristics that allow one to visit only the most promising parts of the search space. Quite interesting, an increasing number of systems started to adopt planning and scheduling techniques in order to afford complex application contexts, and obtaining solutions that better fit the problem constraints and the users’ needs. This special issue contains extended versions of selected papers presented at IPS 2013, the 5th Italian Workshop on Planning and Scheduling held in Torino, Italy, December 4th, 2013.1 IPS 2013 was held within the XIII Conference of the Italian Association for Artificial Intelligence (AI*IA 2013), gathered together researchers interested in different aspects of planning and scheduling, and introduced new researchers to the community. For this edition of IPS, the call for papers solicited submissions of two different types: full papers and short papers. Full technical papers reported work in progress or completed work, while short papers reported views or

A general theory of spatial relations to support a graphical tool for visual information extraction

In this paper we present a general spatial composition framework which allows one to model the graphical objects and the spatial relations of a large class of visual languages. The new formalism has been implemented within the SRQ tool, a software system for the Visual Information Extraction, enabling it to work on a wider range of domains. In particular, in the paper we describe the application of SRQ to geospatial data.

Planning for Autonomous Planetary Vehicles

Autonomous vehicles are often complex systems that work in a partially unknown environment, with narrow energy/time/movement constraints. Formal models for such vehicles usually involve hybrid systems with nonlinear dynamics, which are difficult to handle by most of the current planning algorithms and tools. Therefore, when offline planning of the vehicle activities is required, for example for rovers that operate without a continuous Earth supervision, such planning is often performed on simplified models that are not completely realistic. In this paper we show how a model checking based tool, namely UPMurphi, can be used to generate optimal plans to control the engine of an autonomous planetary vehicle, working directly on its hybrid model, and thus achieving very accurate results.