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2011 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Ezequiel Di Mario Grégory Mermoud Massimo Mastrangeli Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory (DISAL) School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Switzerland

In this paper, we present a quantitative, trajectory-based method for calibrating stochastic motion models of water-floating robots. Our calibration method is based on the Correlated Random Walk (CRW) model, and consists in minimizing the Kolmogorov-Smirnov (KS) distance between the step length and step angle distributions of real and simulated trajectories generated by the robots. First, we validate this method by calibrating a physics-based motion model of a single 3-cm-sized robot floating at a water/air interface under fluidic agitation. Second, we extend the focus of our work to multi-robot systems by performing a sensitivity analysis of our stochastic motion model in the context of Self-Assembly (SA). In particular, we compare in simulation the effect of perturbing the calibrated parameters on the predicted distributions of self-assembled structures. More generally, we show that the SA of water-floating robots is very sensitive to even small variations of the underlying physical parameters, thus requiring real-time tracking of its dynamics.

关键词： Trajectory Drag Force Robots Stochastic processes Calibration Manganese

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A system implementation and evaluation of a cooperative fusion and tracking algorithm based on a Gaussian Mixture PHD filter

A system implementation and evaluation of a cooperative fusi...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： Milos Vasic David Mansolino Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781509037636

This paper focuses on a real system implementation, analysis, and evaluation of a cooperative sensor fusion algorithm based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, using simulated and real vehicles endowed with automotive-grade sensors. We have extended our previously presented cooperative sensor fusion algorithm with a fusion weight optimization method and implemented it on a vehicle that we denote as the ego vehicle. The algorithm fuses information obtained from one or more vehicles located within a certain range (that we call cooperative), which are running a multi-object tracking PHD filter, and which are sharing their object estimates. The algorithm is evaluated on two Citroen C-ZERO prototype vehicles equipped with Mobileye cameras for object tracking and lidar sensors from which the ground truth positions of the tracked objects are extracted. Moreover, the algorithm is evaluated in simulation using simulated C-ZERO vehicles and simulated Mobileye cameras. The ground truth positions of tracked objects are in this case provided by the simulator. Multiple experimental runs are conducted in both simulated and real-world conditions in which a few legacy vehicles were tracked. Results show that the cooperative fusion algorithm allows for extending the sensing field of view, while keeping the tracking accuracy and errors similar to the case in which the vehicles act alone.

关键词： Vehicles Radar tracking Cameras Sensor fusion Laser radar Algorithm design and analysis Robot sensing systems

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Real-time automated modeling and control of self-assembling systems

Real-time automated modeling and control of self-assembling ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Grégory Mermoud Massimo Mastrangeli Utkarsh Upadhyay Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory (DISAL) School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Switzerland

We present the M 3 framework, a formal and generic computational framework for modeling and controlling stochastic distributed systems of purely reactive robots in an automated and real-time fashion. Based on the trajectories of the robots, the framework builds up an internal microscopic representation of the system, which then serves as a blueprint of models at higher abstraction levels. These models are then calibrated using a Maximum Likelihood Estimation (MLE) algorithm. We illustrate the structure and performance of the framework by performing the online optimization of a bang-bang controller for the stochastic self-assembly of water-floating, magnetically latching, passive modules. The experimental results demonstrate that the generated models can successfully optimize the assembly of desired structures.

关键词： Robots Computational modeling Trajectory Aerospace electronics Coordinate measuring machines Stochastic processes Real time systems

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Automatic Multi-Robot Control Design and Optimization Leveraging Multi-Level Modeling: An Exploration Case Study

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IFAC-PapersOnLine 2023年第2期56卷 11462-11469页

作者： Wakana Endo Cyrill Baumann Hajime Asama Alcherio Martinoli Department of Precision Engineering School of Engineering The University of Tokyo Japan Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne (EPFL) Switzerland

In this work, we demonstrate the applicability of a recently proposed automatic synthesis approach for behavioral arbitrators based on Probabilistic Finite State Machines (PFSMs) for a multi-robot scenario. More specifically, a behavior-based controller for a multi-robot exploration scenario is automatically synthesized using a predefined set of basic behaviors and conditions. A key feature of the used synthesis approach is the tailored use of two modeling levels of the scenario, microscopic and submicroscopic, to significantly reduce the computational effort. Furthermore, the modeling is extended by a simplified macroscopic model of the scenario to analytically evaluate the best achievable performance given an ideal controller, taking into account real-world constraints such as limited speed and localization. Taking advantage of the interpretability of the synthesized PFSM-based arbitrators, individualistic and collaborative controllers are analyzed separately to provide insights into the theoretical and experimental effects of collaboration for the considered case study. The obtained results show that the PFSM-based synthesis approach is also suitable for multi-robot scenarios, and in particular that the collaborative solution can compete with a manually designed and fine-tuned solution.

关键词： Multi-Level Modeling Probabilistic Modeling Learning for Control Metaheuristic Optimization Multi-Robot systems Behavior-Based Control

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Safety issues in human-robot interactions

Safety issues in human-robot interactions

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Milos Vasic Aude Billard Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Learning Algorithms and Systems Laboratory School of Engineering Ecole Polytechnique Federale de Lausanne Lausanne Switzerland

Safety is an important consideration in human-robot interactions (HRI). Robots can perform powerful movements that can cause hazards to humans surrounding them. To prevent accidents, it is important to identify sources of potential harm, to determine which of the persons in the robot's vicinity may be in greatest peril and to assess the type of injuries the robot may cause to this person. This survey starts with a review of the safety issues in industrial settings, where robots manipulate dangerous tools and move with extreme rapidity and force. We then move to covering issues related to the growing numbers of autonomous mobile robots that operate in crowded (human-inhabited) environments. We discuss the potential benefits of fully autonomous cars on safety on roads and for pedestrians. Lastly, we cover safety issues related to assistive robots.

关键词： Robot sensing systems Service robots Hazards Accidents Injuries

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Design and Evaluation of Modular Gas and Wind Sensing Nodes for Static and Mobile Deployments

Design and Evaluation of Modular Gas and Wind Sensing Nodes ...

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Wanting Jin Emmanuel Droz Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Static and mobile sensor nodes can be employed in gas monitoring tasks to detect gas leaks in an early stage and localize gas sources. Due to the intermittent nature of gas plumes and the slow dynamics of commonly used gas sensors, measuring gas concentrations accurately and timely poses significant challenges. These challenges are exacerbated when measurements are gathered while moving. Actively sniffing in the airflow, facilitated by actuators, holds the potential to improve the quality of measurements obtained by the sensor nodes. In this paper, we present the design of a small-scale, modular sensor node endowed with gas and wind sensing modalities. To assess the benefits of active sampling and the rationale behind this enhancement, comparisons among three different air sampling modes in both static and mobile settings are conducted. Our findings suggest that passive sampling can adequately capture the primary features of gas plumes given sufficient exposure and measuring time at each position. However, active sampling enhances the responsiveness of sensor nodes, enabling the detection of more detailed fluctuations in the gas concentration and thus alleviating spatial shifts in the sensor response induced by mobility effects.

关键词： Actuators Mechatronics Fluctuations Position measurement Time measurement Sensors Task analysis

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Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

Sense in Motion with Belief Clustering: Efficient Gas Source...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wanting Jin Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurements, encompassing both qualitative and quantitative aspects. Most existing model-based GSL algorithms rely on lengthy stops at each sampling point to ensure accurate gas measurements. However, this approach not only prolongs the time required for a single measurement but also hinders sampling during robot motion, thus exacerbating the scarcity of available gas measurements. In this work, our goal is to push the boundaries in terms of continuity in sampling to enhance system efficiency. Firstly, we decouple and comprehensively evaluate the impact of both plume dynamics and gas sensor properties on the GSL performance. Secondly, we demonstrate that adopting a continuous sampling strategy, which has been generally overlooked in prior research, markedly enhances the system efficiency by obviating the prolonged measurement pauses and leveraging all the data gathered during the robot motion. Thirdly, we further expand the capabilities of the continuous sampling by introducing a novel informative path-planning strategy, which takes into account all the information gathered along the robot's movement. The proposed method is evaluated in both simulation and reality under different scenarios emulating indoor environmental conditions.

关键词： Location awareness Robot motion Robot sensing systems Time measurement Sensors Motion measurement Mobile robots

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High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

High Resolution Air Pollution Maps in Urban Environments Usi...

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International Conference on distributed Computing in Sensor systems (DCOSS)

作者： Ali Marjovi Adrian Arfire Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.

关键词： Atmospheric modeling Pollution measurement Atmospheric measurements Mobile communication Monitoring Air pollution Cities and towns

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Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots

Towards Efficient Gas Leak Detection in Built Environments: ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wanting Jin Faezeh Rahbar Chiara Ercolani Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

The deployment of robots for Gas Source Localization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Gas sensing using mobile robots focuses primarily on simplified scenarios, due to the complexity of gas dispersion, with a current trend towards tackling more complex environments. However, most state-of-art GSL algorithms for environments with obstacles only depend on local information, leading to low efficiency in large and more structured spaces. The efficiency of GSL can be improved dramatically by coupling it with a global knowledge of gas distribution in the environment. However, since gas dispersion in a built environment is difficult to model analytically, most previous work incorporating a gas dispersion model was tested under simplified assumptions, which do not take into consideration the impact of the presence of obstacles to the airflow and gas plume. In this paper, we propose a probabilistic algorithm that enables a robot to efficiently localize gas sources in built environments, by combining a state-of-the-art probabilistic GSL algorithm, Source Term Estimation (STE) with a learned plume model. The pipeline of generating gas dispersion datasets from realistic simulations, the training and validation of the model, as well as the integration of the learned model with the STE framework are presented. The performance of the algorithm is validated both in high-fidelity simulations and real experiments, with promising results obtained under various obstacle configurations.

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Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge

Multi-Robot 3D Gas Distribution Mapping: Coordination, Infor...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chiara Ercolani Shashank Mahendra Deshmukh Thomas Laurent Peeters Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

Environmental monitoring and mapping operations are an essential tool to combat climate change. An important branch of this domain concerns the construction of reliable gas maps. Adaptive navigation strategies coupled with multi-robot systems improve the outcome of an environmental mapping mission by focusing more efficiently on informative areas. This direction is yet to be explored in the context of gas mapping, which presents peculiar challenges due to the hard-to-sense and expensive-to-model nature of the underlying phenomenon. In this paper, we introduce the application of a multi-robot system to a gas mission with severe time constraints. We study the impact of information-based navigation strategies, coupled with increasing levels of coordination among the robots, on information gathering and consequent map reconstruction performance. We also focus on proposing solutions that inject additional knowledge into the system to enhance the final mapping outcome. We tested the strategies through extensive high-fidelity simulation experiments, and we compared the proposed approaches to three relevant baseline methods.

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