Francesco Martinelli

A Passive UHF-RFID System for the Localization of an Indoor Autonomous Vehicle

A global localization system combining odometry data with radio frequency identification (RFID) readings is proposed. RFID tags are placed at the ceiling of the environment and can be detected by a mobile robot unit traveling below them. The detection of the tags is the only information used in the proposed approach (no distance or bearing to the tag is considered available), but differently from similar localization setups reported in the literature, only a small number (about one each square meter or less) of tags are used. This is possible using a suitable tags antenna in ultrahigh frequency band, expressly designed to obtain regular and stable RFID detection regions, which allows us to consider an efficient Kalman filtering approach to fuse RFID readings with the vehicle odometry data. A satisfactory performance is achieved, with an average position error of about 0.1 m. The hardware/software localization setup described in this paper is cheap and easy to use and may provide a satisfactory approach in several industrial and domestic scenarios.

Mobile Robot Localization Using the Phase of Passive UHF RFID Signals

This paper presents a global localization system for an indoor autonomous vehicle equipped with odometry sensors and a radio-frequency identification (RFID) reader to interrogate tags located on the ceiling of the environment. The RFID reader can measure the phase of the signals coming from responding tags. This phase has non-univocal dependence on the distance robot tag, but in the considered frequency, it is really sensitive to a change in the position of the robot. For this reason, a multihypothesis Kalman filtering approach provides a really satisfactory performance even in the case that a very small density of tags is used: In the experimental tests, an average position estimation error of about 4 cm is achieved using only two tags for an area of about 5 m2.

Supply Chain Management by H-Infinity Control

In this paper, a H infin control technique is proposed for the management of a supply chain (SC) model linearized about nominal operating conditions. The performance considered is a weighted H infin norm comprising the inventory and the orders placed by each site, hence controlling the bullwhip effect together with local management costs. It is shown how the optimization of local costs at each site is related to the performance of the whole chain and a decentralized control methodology is proposed based on this relation.

Optimality of a Two-Threshold Feedback Control for a Manufacturing System With a Production Dependent Failure Rate

We consider a Markovian, failure prone, single machine, single part-type, fluid flow, manufacturing system. The failure rate depends on the production rate through a non decreasing, piecewise constant function which may assume two values. The problem is to minimize a long term average expected cost which penalizes both the presence of waiting customers and the inventory surplus. We derive and prove the optimality of a policy which depends on two thresholds. The expression of these thresholds and of the optimal cost is also included in this note together with some numerical examples.

A distributed algorithm for the multi-robot task allocation problem

In this work we address the Multi-Robot Task Allocation Problem (MRTA). We assume that the decision making environment is decentralized with as many decision makers (agents) as the robots in the system. To solve this problem, we developed a distributed version of the Hungarian Method for the assignment problem. The robots autonomously perform different substeps of the Hungarian algorithm on the base of the individual and the information received through the messages from the other robots in the system. It is assumed that each robot agent has an information regarding its distance from the targets in the environment. The inter-robot communication is performed over a connected dynamic communication network and the solution to the assignment problem is reached without any common coordinator or a shared memory of the system. The algorithm comes up with a global optimum solution in O(n3) cumulative time (O(n2) for each robot), with O(n3) number of messages exchanged among the n robots.

Constrained and quantized Kalman filtering for an RFID robot localization problem

In this paper a global localization problem of a robot moving in a known environment is considered. The environment is equipped with a relatively sparse set of passive RFID (Radio Frequency IDentification) tags. The robot can detect the presence of the tags when traveling in their proximity and combines this information with the one given by other sensors (e.g. odometry). The RFID measurements are characterized by a highly non Gaussian noise: for this reason in the literature Particle Filter (PF) methods have often been used to fuse these data with the measurements coming from other sensors. In this paper a different approach is pursued, based on the observation that RFID readings can be considered as noisy quantized measurements of the pose of the robot or as noisy dynamic constraints on the pose itself. This allows to exploit the rich literature on Kalman quantized filtering or Kalman constrained estimation, to realize reliable methods with a satisfactory performance which require a computational time significantly lower with respect to the one needed by a PF. Simulative and experimental results will be reported to illustrate the proposed methods.

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.

Hedging point policies remain optimal under limited backlog and inventory space

In this note, we consider a fluid flow, single part-type, single unreliable machine production system with a bounded backlog/inventory space. We prove that, as in the unbounded case, the problem of minimizing an infinite horizon average demand loss/backlog/surplus cost is solved by a hedging point policy. An implicit equation is given, whose structure easily allows to numerically evaluate the optimal safety stock. The effect of system parameters on the optimal safety stock is analyzed and some numerical examples illustrate the presented results.

H-infinity control of a Supply Chain model

A Hinfin control approach has been developed in this paper for a simple supply chain model, linearized about typical operative conditions, and fed with an exogenous and unknown demand. The considered approach has been compared with other emerging control techniques in terms of a performance index defined on the inventory levels in the supply chain, showing a good performance when the exogenous demand presents significant low frequency components. Robustness issues, regarding the knowledge of the production and transport delays in the chain, are considered in the paper, and a properly modified Hinfin technique is proposed to deal with uncertain and/or time varying delays

Robot group formations: a dynamic programming approach for a shortest path computation

Rigid formations of mobile robots are to be used for special missions in which the task-execution requires a tight cooperation of all units in the group so as to constrain them to keep preassigned mutual distances. In the paper an algorithm for the optimal path-planning of rigid formations of mobile robots is considered for a case in which the path cost index is given by the sum of all distances covered by the robots in the group. The proposed solution method provides an approximate solution to the problem based on a discretization of the configuration space of the formation. A dynamic programming algorithm is used then to find the optimal path in configuration space. Several examples are introduced to show the effectiveness of the proposed dynamic programming method as compared to three heuristic strategies which are also devised in the paper.