Vittorio Amos Ziparo

A mechanism for dynamic ride sharing based on parallel auctions

Car pollution is one of the major causes of greenhouse emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Existing efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that minimize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important feature when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.

Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from RoboCup rescue

There are disclosed benzothiadiazinyl and quinazolinyl substituted carboxylalkyl dipeptides, wherein the benzothiodiazinyl or quinazolinyl portions are joined to the dipeptide portions by an aminocarbonyl group. Compounds of this invention are useful as antihypertensive agents, in the treatment of congestive heart failure and in the treatment of glaucoma. In addition, compounds of this invention have diuretic activity.

RFID-Based Exploration for Large Robot Teams

To coordinate a team of robots for exploration is a challenging problem, particularly in large areas as for example the devastated area after a disaster. This problem can generally be decomposed into task assignment and multi-robot path planning. In this paper, we address both problems jointly. This is possible because we reduce significantly the size of the search space by utilizing RFID tags as coordination points. The exploration approach consists of two parts: a stand-alone distributed local search and a global monitoring process which can be used to restart the local search in more convenient locations. Our results show that the local exploration works for large robot teams, particularly if there are limited computational resources. Experiments with the global approach showed that the number of conflicts can be reduced, and that the global coordination mechanism increases significantly the explored area.

Assignment of Dynamically Perceived Tasks by Token Passing in Multirobot Systems

The problem of assigning tasks to a group of robots acting in a dynamic environment is a fundamental issue for a multirobot system (MRS) and several techniques have been studied to address this problem. Such techniques usually rely on the assumption that tasks to be assigned are inserted into the system in a coherent fashion. In this work we consider a scenario where tasks to be accomplished are perceived by the robots during mission execution. This issue has a significative impact on the task allocation process and, at the same time, makes it strictly dependent on perception capabilities of robots. More specifically, we present an asynchronous distributed mechanism based on Token Passing for allocating tasks in a team of robots. We tested and evaluated our approach by means of experiments both in a simulated environment and with real robots; our scenario comprises a set of robots that must cooperatively collect a set of objects scattered in the working environment. Each object collection task requires the cooperation of two robots. The experiments in the simulation environment allowed us to extract quantitative data from several missions and in different operative conditions and to characterize in a statistical way the results of our approach, especially when the team size increases

Task Assignment with Dynamic Perception and Constrained Tasks in a Multi-Robot System

In this paper we present an asynchronous distributed mechanism for allocating tasks in a team of robots. Tasks to be allocated are dynamically perceived from the environment and can be tied by execution constraints. Conflicts among team mates arise when an uncontrolled number of robots execute the same task, resulting in waste of effort and spatial conflicts. The critical aspect of task allocation in Multi Robot Systems is related to conflicts generated by limited and noisy perception capabilities of real robots. This requires significant extensions to the task allocation techniques developed for software agents. The proposed approach is able to successfully allocate roles to robots avoiding conflicts among team mates and maintaining low communication overhead. We implemented our method on AIBO robots and performed quantitative analysis in a simulated environment.

Teamwork Design Based on Petri Net Plans

This paper presents a design of cooperative behaviors through Petri Net Plans, based on the principles provided by Cohen and Levesques Joint Commitments Theory. Petri Net Plans are a formal tool that has proved very effective for the representation of multi-robot plans, providing all the means necessary for the design of cooperation. The Joint Commitment theory is used as a guideline to present a general multi-robot Petri Net Plan for teamwork, that can be used to model a wide range of cooperative behaviors. As an example we describe the implementation of a robotic-soccer passing task, performed by Sony AIBO robots.

Context-based design of robotic systems

The need for improving the robustness, as well as the ability to adapt to different operational conditions, is a key requirement for a wider deployment of robots in many application domains. In this paper, we present an approach to the design of robotic systems, that is based on the explicit representation of knowledge about context. The goal of the approach is to improve the systems performance, by dynamically tailoring the functionalities of the robot to the specific features of the situation at hand. While the idea of using contextual knowledge is not new, the proposed approach generalizes previous work, and its advantages are discussed through a case study including several experiments. In particular, we identify many attempts to use contextual knowledge in several basic functionalities of a mobile robot such as: behavior, navigation, exploration, localization, mapping and perception. We then show how re-designing our mobile platform with a common representation of contextual knowledge, leads to interesting improvements in many of the above mentioned components, thus achieving greater flexibility and robustness in the face of different situations. Moreover, a clear separation of contextual knowledge leads to a design methodology, which supports the design of small specialized system components instead of complex self-contained subsystems.

Multi-objective multi-robot surveillance

In many surveillance applications, there are different properties of the environment to check. For example, in the case of robots surveilling an industrial depot, one could be interested in verifying for fire alarms, intrusion alarms or bio-hazards. It is very hard to characterize the solution of this problem in terms of a unique utility function. Indeed, this would require to define a measure of the tradeoff among objectives, which are, by definition, incommensurable quantities. These tradeoffs should be tuned according to contingencies which is, in general, a very difficult task. We present in this paper, an approach to address such issues. We define a multi-robot multi-objective surveillance problem and show how this can be solved by a particular type of heuristic graph search used in the field of multi-objective optimization. The approach has been experimented based on an off-line planner including simulated and real robot plan execution results.

Improving Search and Rescue Using Contextual Information

Search and rescue (SAR) is a challenging application for autonomous robotics research. The requirements of this kind of application are very demanding and are still far from being met. One of the most compelling requirements is the capability of robots to adapt their functionalities to harsh and heterogeneous environments. In order to meet this requirement, it is common to embed contextual knowledge into robotic modules. We have previously developed a context-based architecture that decouples contextual knowledge, and its use, from typical robotic functionalities. In this paper, we show how it is possible to use this approach to enhance the performance of a robotic system involved in SAR missions. In particular, we provide a case study on exploration and victim detection tasks, specifically tailored to a given SAR mission. Moreover, we extend our contextual knowledge formalism in order to manage complex rules that deal with spatial and temporal aspects that are needed to model mission requirements. The approach has been validated through several experiments that show the effectiveness of the presented methodology for SAR.

Cooperative Exploration for USAR Robots with Indirect Communication

To coordinate a team of robots for exploration is a challenging problem, particularly in unstructured areas, as for example post-disaster scenarios where direct communication is severely constrained. Furthermore, conventional methods of SLAM, e.g. those performing data association based on visual features, are doomed to fail due to bad visibility caused by smoke and fire. We use indirect communication (based on RFIDs), to share knowledge and use a gradient-like local search to direct robots towards interesting areas. To share a common frame of reference among robots we use a feature based SLAM approach (where features are RFIDs). The approach has been evaluated on a 3D simulation based on USARSim.