Abstract Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.

Unexpected contingencies in robot execution may induce a cascade of effects, especially when multiple robots are involved. In order to effectively adapt to this, robots need the ability to reason along multiple dimensions at execution time. We propose an approach to closed-loop planning capable of generating configuration plans, i.e., action plans for multirobot systems which specify the causal, temporal, resource and information dependencies between individual sensing, computation, and actuation components. The key feature which enables closed loop performance is that configuration plans are represented as constraint networks, which are shared between the planner and the executor and are continuously updated during execution. We report experiments run both in simulation and on real robots, in which a fault in one robot is compensated through different types of plan modifications at run time.

This paper focuses on the extension of the transferable belief model (TBM) to a multiagent-distributed context where no central aggregation unit is available and the information can be exchanged only locally among agents. In this framework, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge about an event of interest. Two different scenarios are considered: In the first one, agents are supposed to provide observations which do not change over time (static scenario), while in the second one agents are assumed to dynamically gather data over time (dynamic scenario). A protocol for distributed data aggregation, which is proved to converge to the basic belief assignment given by an equivalent centralized aggregation schema based on the TBM, is provided. Since multiagent systems represent an ideal abstraction of actual networks of mobile robots or sensor nodes, which are envisioned to perform the most various kind of tasks, we believe that the proposed protocol paves the way to the application of the TBM in important engineering fields such as multirobot systems or sensor networks, where the distributed collaboration among players is a critical and yet crucial aspect.

In this work the data aggregation problem for a multi-agent system within the framework of Theory of Evidence is investigated. In the proposed scenario, agents are assumed to be independent reliable sources which collect data and collaborate to reach a common knowledge. In particular, each agent is supposed to provide a set of observations which does not change over time. A protocol for distributed data aggregation for graph-like network topologies is designed. Experimental results with a sensor network have been carried out to corroborate the theoretical results and the feasibility of the proposed approach.

Environmental monitoring is a rather new field in robotics. One of the main appealing tasks is gas mapping, i.e., the characterization of the chemical properties (concentration, dispersion, etc.) of the air within an environment. Current approaches rely on a robot using standard localization and mapping techniques to fuse gas measures with spatial features. These approaches require sophisticated sensors and/or high computational resources. We propose a minimalistic approach, in which one or multiple low-cost robots exploit the ability to store information in the environment, or ¿stigmergy¿, to effectively compute an artificial potential leading toward the likely location of the gas source, as indicated by a highest gas concentration or fluctuation. The potential is computed and stored directly on an array of RFID tags buried under the floor. Our approach has been validated in extensive experiments performed on real robots in a domestic environment.

In this paper the problem of multi-robot collaborative topological map-building is addressed. In this framework, a team of robots is supposed to move in an indoor office-like environment. Each robot, after building a local map by using infrared range-finders, achieves a topological representation of the environment by extracting the most significant features via the Hough transform and comparing them with a set of predefined environmental patterns. The local view of each robot which is significantly constrained by its limited sensing capabilities is then strengthened by a collaborative aggregation schema based on the Transferable Belief Model (TBM). In this way, a better representation of the environment is achieved by each robot with a minimal exchange of information. A preliminary experimental validation carried out by exploiting data collected from a self-made team of robots is proposed.