Stefano Carpin

USARSim: a robot simulator for research and education

This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.

Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores

Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards the TrueNorth cognitive computing system inspired by the brains function and efficiency. The non-von Neumann nature of the TrueNorth architecture necessitates a novel approach to efficient system design. To this end, we have developed a set of abstractions, algorithms, and applications that are natively efficient for TrueNorth. First, we developed repeatedly-used abstractions that span neural codes (such as binary, rate, population, and time-to-spike), long-range connectivity, and short-range connectivity. Second, we implemented ten algorithms that include convolution networks, spectral content estimators, liquid state machines, restricted Boltzmann machines, hidden Markov models, looming detection, temporal pattern matching, and various classifiers. Third, we demonstrate seven applications that include speaker recognition, music composer recognition, digit recognition, sequence prediction, collision avoidance, optical flow, and eye detection. Our results showcase the parallelism, versatility, rich connectivity, spatio-temporality, and multi-modality of the TrueNorth architecture as well as compositionality of the corelet programming paradigm and the flexibility of the underlying neuron model.

Fast and accurate map merging for multi-robot systems

We present a new algorithm for merging occupancy grid maps produced by multiple robots exploring the same environment. The algorithm produces a set of possible transformations needed to merge two maps, i.e translations and rotations. Each transformation is weighted, thus allowing to distinguish uncertain situations, and enabling to track multiple cases when ambiguities arise. Transformations are produced extracting some spectral information from the maps. The approach is deterministic, non-iterative, and fast. The algorithm has been tested on public available datasets, as well as on maps produced by two robots concurrently exploring both indoor and outdoor environments. Throughout the experimental validation stage the technique we propose consistently merged maps exhibiting very different characteristics.

Rescue robotics — a crucial milestone on the road to autonomous systems

Rescue robotics is an important steppingstone in the scientific challenge to create autonomous systems. There is a significant market for rescue robots, which have unique features that allow a fruitful combination of application-oriented developments and basic research. Unlike other markets for advanced robotics systems like service robots, the rescue robotics domain benefits from the fact that there is a human in the loop, which allows a stepwise transition from dumb teleoperated devices to truly autonomous systems. Current teleoperated devices are already very useful in this domain and they benefit from any bit of autonomy added. Human rescue workers are a scarce resource at disaster scenarios. A single operator should, hence, ideally supervise a multitude of robots. We present results from the rescue robots at the International University Bremen in a core area supporting autonomy, i.e., mapping.

Bridging the Gap Between Simulation and Reality in Urban Search and Rescue

Research efforts in urban search and rescue grew tremendously in recent years. In this paper we illustrate a simulation software that aims to be the meeting point between the communities of researchers involved in robotics and multi-agent systems. The proposed system allows the realistic modeling of robots, sensors and actuators, as well as complex unstructured dynamic environments. Multiple heterogeneous agents can be concurrently spawned inside the environment. We explain how different sensors and actuators have been added to the system and show how a seamless migration of code between real and simulated robots is possible. Quantitative results supporting the validation of simulation accuracy are also presented.

Validating USARsim for use in HRI Research

HRI is an excellent candidate for simulator based research because of the relative simplicity of the systems being modeled, the behavioral fidelity possible with current physics engines and the capability of modern graphics cards to approximate camera video. In this paper we briefly introduce the USARsim simulation and discuss efforts to validate its behavior for use in Human Robot Interaction (HRI) research.

High fidelity tools for rescue robotics: results and perspectives

USARSim is a high fidelity robot simulation tool based on a commercial game engine. We illustrate the overall structure of the simulator and we argue about its use as a bridging tool between the RoboCupRescue Real Robot League and the RoboCupRescue Simulation League. In particular we show some results concerning the validation of the system. Algorithms useful for the search and rescue task have been developed in the simulator and then executed on real robots providing encouraging results.

Cooperative leader following in a distributed multi-robot system

The cooperative leader following task for multi-robot teams is introduced and discussed. We describe the design and implementation of a distributed technique to coordinate team level and robot level behaviors for this task, as well as a multi-threaded framework for the implementation of a multi-robot system with heterogeneous sensing capabilities. This approach enables robots to remain in formation as they deal with other obstacles that may appear within the formation. We describe how single robot behaviors are realized and scheduled. We show some of the results of the team implementations. The proposed approach has been run and validated on a team of robots performing both in indoor and outdoor environments.

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

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Pursuit-Evasion on Trees by Robot Teams

We present graph-clear: a novel pursuit-evasion problem on graphs which models the detection of intruders in complex indoor environments by robot teams. The environment is represented by a graph, and a robot team can execute sweep and block actions on vertices and edges, respectively. A sweep action detects intruders in a vertex and represents the capability of the robot team to detect intruders in the region associated to the vertex. Similarly, a block action prevents intruders from crossing an edge and represents the capability to detect intruders as they move between regions. Both actions may require multiple robots to be executed. A strategy is a sequence of block and sweep actions to detect all intruders. When instances of graph-clear are being solved, the goal is to determine optimal strategies, i.e., strategies that use the least number of robots. We prove that for the general case of graphs, the problem of computation of optimal strategies is NP-hard. Next, for the special case of trees, we provide a polynomial-time algorithm. The algorithm ensures that throughout the execution of the strategy, all cleared vertices form a connected subtree, and we show that it produces optimal strategies.