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distributed Cooperative Localization with Efficient Pairwise Range Measurements 1

15th International Symposium on distributed Autonomous Robotic systems, DARS 2021 and 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics, SWARM 2021

作者： Quraishi, Anwar Martinoli, Alcherio Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (数字)9783030927905

ISBN: (纸本)9783030927899

We present a method based on covariance intersection for cooperative localization with pairwise range-only relative measurements. Our method was designed for underwater robots equipped with an acoustic communication and ranging system. Range measurements are not sufficient to compute a complete relative 3D position. Therefore, covariance intersection is performed in a transformed space along their relative estimated positions, while preserving cross-correlations between other state variables. Given the characteristics of the acoustic channel, only one robot can transmit data or a ranging request at a time, hence the pairwise limitation. We also present a heuristic for choosing a peer robot for a range measurement by maximizing mutual information. Our method places no further restrictions on the order, timing or scheduling of relative measurements. We evaluated our method for accuracy and consistency, and present results from simulations as well as outdoor experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robots

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Lumped Drag Model Identification and Real-Time External Force Detection for Rotary-Wing Micro Aerial Vehicles

Lumped Drag Model Identification and Real-Time External Forc...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lucas Wälti Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory (DISAL) School of Architecture Civil and Environmental Engineering (ENAC) Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This work focuses on understanding and identifying the drag forces applied to a rotary-wing Micro Aerial Vehicle (MAV). We propose a lumped drag model that concisely describes the aerodynamical forces the MAV is subject to, with a minimal set of parameters. We only rely on commonly available sensor information onboard a MAV, such as accelerometer data, pose estimate, and throttle commands, which makes our method generally applicable. The identification uses an offline gradient-based method on flight data collected over specially designed trajectories. The identified model allows us to predict the aerodynamical forces experienced by the aircraft due to its own motion in real-time and, therefore, will be useful to distinguish them from external perturbations, such as wind or physical contact with the environment. The results show that we are able to identify the drag coefficients of a rotary-wing MAV through onboard flight data and observe the close correlation between the motion of the MAV, the measured external forces, and the predicted drag forces.

关键词： Accelerometers Force measurement Drag Perturbation methods Atmospheric modeling Wind tunnels Real-time systems

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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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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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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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Robust Localization for Multi-robot Formations: An Experimental Evaluation of an Extended GM-PHD Filter 1

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15th International Symposium on distributed Autonomous Robotic systems, DARS 2021 and 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics, SWARM 2021

作者： Hirayama, Michiaki Wasik, Alicja Kamezaki, Mitsuhiro Martinoli, Alcherio Department of Modern Mechanical Engineering Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Distributed Intelligent Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (数字)9783030927905

ISBN: (纸本)9783030927899

This paper presents a thorough experimental evaluation of an extended Gaussian Mixture Probability Hypothesis Density filter which is able to provide state estimates for the maintenance of a multi-robot formation, even when the communication fails and the tracking data are insufficient for maintaining a stable formation. The filter incorporates, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. By combining communicated data, information about the formation, and sensory detections, the resulting algorithm preserves accuracy in the state estimates despite frequent occurrences of long-duration sensing occlusions, and provides the necessary state information when the communication is sporadic or suffers from short-term outage. Differently from our previous contributions, in which the tracking strategy has only been tested in simulation, in this paper we present the results of experiments with a real multi-robot system. The results confirm that the algorithm enables robust formation maintenance in cluttered environments, under conditions affected by sporadic communication and high measurement uncertainty. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multipurpose robots

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Collision avoidance with limited field of view sensing: A velocity obstacle approach

Collision avoidance with limited field of view sensing: A ve...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Roelofsen, Steven Gillet, Denis Martinoli, Alcherio Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne United States Coordination and Interaction System Group School of Engineering École Polytechnique Fédérale de Lausanne United States

ISBN: (纸本)9781509046331

Collision avoidance, in particular between robots, is an important component for autonomous robots. It is a necessary component in numerous applications such as humanrobot interaction, automotive or unmanned aerial vehicles. While many collision avoidance algorithms take into account actuation constraints, only a few consider sensing limitations. In this paper, we present a reciprocal collision avoidance algorithm based on the velocity obstacle approach that guarantees collision-free maneuvers even when the robots are only capable to sense their environment within a limited Field Of View (FOV). We also present the challenges associated to sensors with limited FOV, show the conditions under which maneuvering can be safely done, and the modifications that a velocity obstacle approach requires to satisfy such conditions. We provide simulations and real robot experiments to validate our approach. © 2017 IEEE.

关键词： Collision avoidance

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Extending Urban Air Quality Maps Beyond the Coverage of a Mobile Sensor Network: Data Sources, Methods, and Performance Evaluation 17

Extending Urban Air Quality Maps Beyond the Coverage of a Mo...

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Proceedings of the 2017 International Conference on Embedded Wireless systems and Networks

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

ISBN: (纸本)9780994988614

Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on public transportation vehicles, (ii) explanatory air-quality and meteorological variables obtained from two static monitoring stations, (iii) land-use data of the city, and (iv) traffic statistics. We propose two novel approaches for estimating the targeted pollutant level at desired time-location pairs, extending also to areas of the city that are beyond the coverage of our mobile sensor network. The first is a log-linear regression model which is built over a virtual dependency graph based on land-use data. The second is a deep learning framework that automatically captures the dependencies of the data based on autoencoders. We have evaluated the two proposed approaches against three canonical modeling techniques considering metrics of coefficient of determination (R²), root mean square error (RMSE), and the fraction of predictions within a factor of two of observations (FAC2). Using more than 45 million real measurements in the models, the results show consistently superior performance in respect to the canonical techniques.

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