Fast multipole networks

Two prerequisites for robotic multiagent systems are mobility and communication. Fast multipole networks (FMNs) enable both ends within a unified framework. FMNs can be organized very efficiently in a distributed way from local information and are ideally suited for motion planning using artificial potentials. We compare FMNs to conventional communication topologies, and find that FMNs offer competitive communication performance (including higher network efficiency per edge at marginal energy cost) in addition to advantages for mobility

A fuzzy multi-criteria decision making approach for managing performance and risk in integrated procurement-production planning

Nowadays in Supply Chain (SC) networks, a high level of risk comes from SC partners. An effective risk management process becomes as a consequence mandatory, especially at the tactical planning level. The aim of this article is to present a risk-oriented integrated procurement–production approach for tactical planning in a multi-echelon SC network involving multiple suppliers, multiple parallel manufacturing plants, multiple subcontractors and several customers. An originality of the work is to combine an analytical model allowing to build feasible scenarios and a multi-criteria approach for assessing these scenarios. The literature has mainly addressed the problem through cost or profit-based optimisation and seldom considers more qualitative yet important criteria linked to risk, like trust in the supplier, flexibility or resilience. Unlike the traditional approaches, we present a method evaluating each possible supply scenario through performance-based and risk-based decision criteria, involving both qualitative and quantitative factors, in order to clearly separate the performance of a scenario and the risk taken if it is adopted. Since the decision-maker often cannot provide crisp values for some critical data, fuzzy sets theory is suggested in order to model vague information based on subjective expertise. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution is used to determine both the performance and risk measures correlated to each possible tactical plan. The applicability and tractability of the proposed approach is shown on an illustrative example and a sensitivity analysis is performed to investigate the influence of criteria weights on the selection of the procurement–production plan

Reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees

We present a model-free reinforcement learning algorithm to synthesize control policies that maximize the probability of satisfying high-level control objectives given as Linear Temporal Logic (LTL) formulas. Uncertainty is considered in the workspace properties, the structure of the workspace, and the agent actions, giving rise to a Probabilistically-Labeled Markov Decision Process (PL-MDP) with unknown graph structure and stochastic behaviour, which is even more general than a fully unknown MDP. We first translate the LTL specification into a Limit Deterministic Büchi Automaton (LDBA), which is then used in an on-the-fly product with the PL-MDP. Thereafter, we define a synchronous reward function based on the acceptance condition of the LDBA. Finally, we show that the RL algorithm delivers a policy that maximizes the satisfaction probability asymptotically. We provide experimental results that showcase the efficiency of the proposed method

Reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees

We present a model-free reinforcement learning algorithm to synthesize control policies that maximize the probability of satisfying high-level control objectives given as Linear Temporal Logic (LTL) formulas. Uncertainty is considered in the workspace properties, the structure of the workspace, and the agent actions, giving rise to a Probabilistically-Labeled Markov Decision Process (PL-MDP) with unknown graph structure and stochastic behaviour, which is even more general than a fully unknown MDP. We first translate the LTL specification into a Limit Deterministic Büchi Automaton (LDBA), which is then used in an on-the-fly product with the PL-MDP. Thereafter, we define a synchronous reward function based on the acceptance condition of the LDBA. Finally, we show that the RL algorithm delivers a policy that maximizes the satisfaction probability asymptotically. We provide experimental results that showcase the efficiency of the proposed method

Triangular mesh offset aiming to enhance fused deposition modeling accuracy

Fused Deposition Modeling is a worldwide diffused Additive Manufacturing technology able to fabricate prototypes, tooling and end user parts directly from a virtual model. The layer by layer fabrication allows having no limitation of part shape complexity but some weaknesses exist about the obtainable accuracy. The dimensional deviations observed on a physical prototype are of the order of some tenths of millimeter depending upon the local slope of the surface. In order to enhance the accuracy, the idea is to compensate for the deviations by means of a virtual model offset. For the purpose a novel methodology has been developed to offset the triangular mesh: it operates an offset with a variable radius of the vertices without changing their interconnections. In this way the new virtual model is generated in a stable and robust way without modifying the Fused Deposition Modeling process chain. The experimentation carried out both on simple and complex geometries points out a marked improvement of the dimensional accuracy. The assessment has been performed by dimensional measurements and by a preliminary analysis of the errors introduced by the offset method