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One of the major problems facing our cities is the disposal of the huge amount of waste produced every day. A possible solution is represented by recycling. In this article, we propose a system for automatic recognition and extraction of materials from the unsorted waste, which takes advantage of Computer Vision and Machine Learning techniques. The system can classify the material of incoming objects and grasp, and insert them into proper bins. For the material classification phase, the system analyzes the information captured by a Near-Infrared (NIR) camera and an RGB camera. Experimental tests performed on real-world datasets show encouraging accuracy values.

This paper presents the planning system Pdk (Planning with Domain Knowledge), based on the translation of planning problems into Linear Time Logic theories, in such a way that finding solution plans is reduced to model search. The model search mechanism is based on temporal tableaux. The planning language accepted by the system allows one to specify extra problem dependent information, that can be of help both in reducing the search space and finding plans of better quality.

The “planning as satisfiability” approach for classical planning establishes a correspondence between planning problems and logical theories, and, consequently, between plans and models. This work proposes a similar framework for contingency planning: considering contingent planning problems where the sources of indeterminism are incomplete knowledge about the initial state, non-inertial fluents and non-deterministic actions, it shows how to encode such problems into Linear Time Logic. Exploiting the semantics of the logic, and the notion of conditioned model introduced in this work, a formal characterization is given of the notion of contingent plan (a plan together with the set of conditions that ensure its executability).