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检索条件"机构=Distributed Intelligent Systems and Algorithms Laboratory"

共 85 条记录，以下是21-30 订阅

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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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Noise-resistant particle swarm optimization for the learning of robust obstacle avoidance controllers using a depth camera

Noise-resistant particle swarm optimization for the learning...

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Congress on Evolutionary Computation

作者： Inaki Navarro Ezequiel Di Mario Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne

The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard problem. In this article we introduce two new Particle Swarm Optimization (PSO) algorithms designed to address this noisy, high-dimensional optimization problem. Their aim is to increase the robustness of the generated robotic controllers, as compared to previous PSO algorithms. We show that we can successfully apply this set of PSO algorithms to learn 166 parameters of a robotic controller for the obstacle avoidance task. We also study the impact that an increased evaluation budget has on the robustness and average performance of the optimized controllers. Finally, we validate the control solutions learned in simulation by testing the most robust controller in three different real arenas.

关键词： Robot sensing systems Robustness Collision avoidance Mobile robots Wheels Cameras

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SwarmViz: An open-source visualization tool for Particle Swarm Optimization

SwarmViz: An open-source visualization tool for Particle Swa...

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Congress on Evolutionary Computation

作者： Guillaume Jornod Ezequiel Di Mario Iñaki Navarro Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne

Particle Swarm Optimization (PSO) is a metaheuristic for solving high dimensional optimization problems. Due to the large number of dimensions usually employed with PSO, it is not trivial to visualize and monitor the progress of the algorithm. Because of this, adjusting the parameters that govern the dynamics of the swarm for a specific problem becomes challenging. In this article, we present SwarmViz, an open-source visualization tool for PSO. Through SwarmViz, users are able to set up PSO experiments on canonical benchmark functions or input data from external experiments (e.g., learning robotic controllers), and to visualize the optimization process with state-of-the-art visualization tools. SwarmViz has two main goals. First, to enable researchers to monitor the progress of their specific optimization problem and adjust the relevant PSO parameters. Second, to give a visual insight about PSO to students in the scope of teaching optimization techniques. We demonstrate the features of the software through examples on well-known numerical benchmark functions and a case study on the optimization of a robotic controller.

关键词： Optimization Visualization

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Comparing and modeling distributed control strategies for miniature self-assembling robots

Comparing and modeling distributed control strategies for mi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： William C. Evans Grégory Mermoud Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory École Polytechnique Fédérale Lausanne Vaud Switzerland

We propose two contrasting approaches to the scalable distributed control of a swarm of self-assembling miniaturized robots, specifically the formation of chains of a desired length: (1) a deterministic controller in which robots communicate with each other in order to directly limit the size of each chain, and (2) a probabilistic controller where the average chain size is controlled by the probability a robot will choose to leave its chain. We demonstrate the feasibility of both approaches by implementing them on a real swarm of Alice robots. Using Webots, a realistic simulator for mobile robotics, and macroscopic models based on the Chemical Reaction Network (CRN) framework, we investigate the limitations of the deterministic controller and demonstrate the existence of optimal parameters for the probabilistic controller where exploration and exploitation are well balanced, thus favoring the formation of larger chains.

关键词： distributed control Robot kinematics Robot sensing systems Self-assembly Mobile robots Size control Aggregates Robotics and automation Optimal control Context modeling

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A comparison of PSO and Reinforcement Learning for multi-robot obstacle avoidance

A comparison of PSO and Reinforcement Learning for multi-rob...

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Congress on Evolutionary Computation

作者： Ezequiel Di Mario Zeynab Talebpour Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781479904532

The design of high-performing robotic controllers constitutes an example of expensive optimization in uncertain environments due to the often large parameter space and noisy performance metrics. There are several evaluative techniques that can be employed for on-line controller design. Adequate benchmarks help in the choice of the right algorithm in terms of final performance and evaluation time. In this paper, we use multi-robot obstacle avoidance as a benchmark to compare two different evaluative learning techniques: Particle Swarm Optimization and Q-learning. For Q-learning, we implement two different approaches: one with discrete states and discrete actions, and another one with discrete actions but a continuous state space. We show that continuous PSO has the highest fitness overall, and Q-learning with continuous states performs significantly better than Q-learning with discrete states. We also show that in the single robot case, PSO and Q-learning with discrete states require a similar amount of total learning time to converge, while the time required with Q-learning with continuous states is significantly larger. In the multi-robot case, both Q-learning approaches require a similar amount of time as in the single robot case, but the time required by PSO can be significantly reduced due to the distributed nature of the algorithm.

关键词： Robot sensing systems Mobile robots Collision avoidance Wheels Optimization

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distributed graph-based convoy control for networked intelligent vehicles

Distributed graph-based convoy control for networked intelli...

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IEEE Symposium on intelligent Vehicle

作者： Ali Marjovi Milos Vasic Joseph Lemaitre Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland

This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopardizing the ensemble's stability. Additionally, we integrate two essential control behaviors for lane-keeping and obstacle avoidance into the controller. To increase the performance of the convoy controller in terms of formation maintenance and fuel economy, the parameters of the controller are optimized in realistic scenarios using Particle Swarm Optimization (PSO), a powerful metaheuristic optimization method well-suited for large parameter spaces. The performances of the optimized controllers are evaluated in high-fidelity multi-vehicle simulations outlining the efficiency and robustness of the proposed strategy.

关键词： Vehicles Vehicle dynamics Collision avoidance Roads intelligent vehicles Laplace equations

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Multi-Robot Coordination in Dynamic Environments Shared with Humans

Multi-Robot Coordination in Dynamic Environments Shared with...

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IEEE International Conference on Robotics and Automation

作者： Zeynab Talebpour Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory (DISAL) Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland

This work addresses multi-robot coordination in social human-populated environments using a market-based framework for solving the Multi-Robot Task Allocation (MRTA) problem. Humans are considered in the proposed coordination mechanism by means of accounting for social costs in bid evaluations and requesting collaboration in socially blocking situations. Initially, the effect of a realistic environment with varying number of static/moving humans on the behavior and performance of our method is studied through an extensive suite of experiments in a high-fidelity simulator. Results show that the total traveled distance and time are increased when humans are present in the environments. Localization noise is also increased particularly in the case of static people. In the second series of experiments, a number of problematic cases resulting in longer modified paths, blocked passages, and long waits have been investigated. A comparative study targeting human-agnostic navigation and planning, human-aware navigation and human-agnostic planning, and human-aware navigation and planning has been conducted. Both simulated and real robot experiments confirm the effectiveness of accounting for humans at both team and individual levels. This leads to respecting social constraints as well as achieving a better performance based on MRTA metrics.

关键词： Robot kinematics Task analysis Navigation Planning Resource management Measurement

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An overtaking decision algorithm for networked intelligent vehicles based on cooperative perception

An overtaking decision algorithm for networked intelligent v...

引用

IEEE Symposium on intelligent Vehicle

作者： Milos Vasic Gael Lederrey Iñaki Navarro Alcherio Martinoli The Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781509018222

This paper presents an overtaking decision algorithm for networked intelligent vehicles. The algorithm is based on a cooperative tracking and sensor fusion algorithm that we previously developed. The ego vehicle is equipped with lane keeping and lane changing capabilities, as well as a forward-looking lidar sensor. The lidar data are fed to the tracking module which detects other vehicles, such as the vehicle that is to be overtaken (leading) and the oncoming traffic. Based on the estimated distances to the leading and the oncoming vehicles and their speeds, a risk is calculated and a corresponding overtaking decision is made. We compare the performance of the overtaking algorithm between the case when the ego vehicle only relies on its lidar sensor, and the case in which it fuses object estimates received from the leading car which also has a forward-looking lidar. Systematic evaluations are performed in Webots, a calibrated high-fidelity simulator.

关键词： Automobiles Target tracking Laser radar intelligent vehicles Clustering algorithms Fuses

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Assembly of configurations in a networked robotic system: A case study on a reconfigurable interactive table lamp

Assembly of configurations in a networked robotic system: A ...

引用

IEEE Swarm Intelligence Symposium, SIS

作者： Christopher M. Cianci Julien Nembrini Amanda Prorok Alcherio Martinoli Distributed Intelligent Systems Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

In the present study, we are interested in verifying how the progressive addition of constraints on communication and localization impact the performance of a swarm of small robots in shape formation tasks. Identified to be of importance in a swarm-user interaction context, the time required to construct a given spatial configuration is considered as a performance metric. The experimental work reported in this paper starts from global and synchronized localization information, shown to be successful both on a real hardware system and in simulation. In a second step, communication is constrained to a local scale, thus obliging a single designated robot to disseminate the global localization information to the other agents. The reliability of the radio communication channel and its impact upon the performance of the system are considered.

关键词： Robotic assembly Assembly systems Lamps Robot kinematics Mobile robots intelligent robots Humans Shape Multirobot systems System testing

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A distributed Source Term Estimation Algorithm for Multi-Robot systems

A Distributed Source Term Estimation Algorithm for Multi-Rob...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Faezeh Rahbar Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Finding sources of airborne chemicals with mobile sensing systems finds applications in safety, security, and emergency situations related to medical, domestic, and environmental domains. Given the often critical nature of all the applications, it is important to reduce the amount of time necessary to accomplish this task through intelligent systems and algorithms. In this paper, we extend a previously presented algorithm based on source term estimation for odor source localization for homogeneous multi-robot systems. By gradually increasing the level of coordination among multiple mobile robots, we study the benefits of a distributed system on reducing the amount of time and resources necessary to achieve the task at hand. The method has been evaluated systematically through high-fidelity simulations and in a wind tunnel emulating realistic and repeatable conditions in different coordination scenarios and with different number of robots.

关键词： Robot kinematics Robot sensing systems Estimation Navigation Probabilistic logic

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