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The Effect of the Environment in the Synthesis of Robotic Controllers: A Case Study in Multi-Robot Obstacle Avoidance using distributed Particle Swarm Optimization 12

The Effect of the Environment in the Synthesis of Robotic Co...

引用

12th European Conference on the Synthesis and Simulation of Living systems: Advances in Artificial Life, ECAL 2013

作者： Mario, Ezequiel Di Navarro, Inaki Martinoli, Alcherio Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Federale de Lausanne Switzerland

ISBN: (纸本)9780262317092

The ability to move in complex environments is a fundamental requirement for robots to be a part of our daily lives. While in simple environments it is usually straightforward for human designers to foresee the different conditions a robot will be exposed to, for more complex environments the human design of high-performing controllers becomes a challenging task, especially when the on-board resources of the robots are limited. In this article, we use a distributed implementation of Particle Swarm Optimization to design robotic controllers that are able to navigate around obstacles of different shape and size. We analyze how the behavior and performance of the controllers differ based on the environment where learning takes place, showing that different arenas lead to different avoidance behaviors. We also test the best controllers in environments not encountered during learning, both in simulation and with real robots, and show that no single learning environment is able to generate a behavior general and robust enough to succeed in all testing environments. © 2013 Proceedings of the 12th European Conference on the Synthesis and Simulation of Living systems: Advances in Artificial Life, ECAL 2013. All rights reserved.

关键词： Controllers

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distributed Cooperative Localization with Efficient Pairwise Range Measurements 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： 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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Top-down vs. bottom-up model-based methodologies for distributed control: a comparative experimental study 12

Top-down vs. bottom-up model-based methodologies for distrib...

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12th International Symposium on Experimental Robotics, ISER 2010

作者： Mermoud, Grégory Upadhyay, Utkarsh Evans, William C. Martinoli, Alcherio École Polytechnique Fédérale de Lausanne School of Architecture Civil and Environmental Engineering Distributed Intelligent Systems and Algorithms Laboratory Lausanne1015 Switzerland

ISBN: (纸本)9783642285714

Model-based synthesis of distributed controllers for multi-robot systems is commonly approached in either a top-down or bottom-up fashion. In this paper, we investigate the experimental challenges of both approaches, with a special emphasis on resource-constrained miniature robots. We make our comparison through a case study in which a group of 2-cm-sized mobile robots screen the environment for undesirable features, and destroy or neutralize them. First, we solve this problem using a top-down approach that relies on a graph-based representation of the system, allowing for direct optimization using numerical techniques (e.g., linear and non-linear convex optimization) under very unrealistic assumptions (e.g., infinite number of robots, perfect localization, global communication, etc.). We show how one can relax these assumptions in the context of resource-constrained robots, and explain the resulting impact on system performance. Second, we solve the same problem using a bottom-up approach, i.e., we build up computationally efficient and accurate models at multiple abstraction levels, and use them to optimize the robots’ controller using evolutionary algorithms. Finally, we outline the differences between the top-down and bottom-up approaches, and experimentally compare their performance. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Convex optimization

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distributed particle swarm optimization for limited-time adaptation with real robots

Distributed particle swarm optimization for limited-time ada...

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作者： Di Mario, Ezequiel Martinoli, Alcherio Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique F´ed´erale de Lausanne 1015 Lausanne Switzerland

Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation. © 2014 Cambridge University Press.

关键词： Multipurpose robots

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A distributed noise-resistant Particle Swarm Optimization algorithm for high-dimensional multi-robot learning

A distributed noise-resistant Particle Swarm Optimization al...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ezequiel Di Mario Iñaki Navarro Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering

ISBN: (纸本)9781479969241

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabilities, which have inherently noisy performance evaluations. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of Particle Swarm Optimization in the presence of noise for a multi-robot obstacle avoidance benchmark task. We present a new distributed PSO OCBA algorithm suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication. Our results from simulation show that PSO OCBA outperforms other techniques for dealing with noise, achieving a more consistent progress and a better estimate of the ground-truth performance of candidate solutions. We then validate our simulations with real robot experiments where we compare the controller learned with our proposed algorithm to a potential field controller for obstacle avoidance in a cluttered environment. We show that they both achieve a high performance through different avoidance behaviors.

关键词： Robot sensing systems Noise Collision avoidance Benchmark testing Resource management Mobile robots

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The role of environmental and controller complexity in the distributed optimization of multi-robot obstacle avoidance

The role of environmental and controller complexity in the d...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ezequiel Di Mario Iñaki Navarro Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering

The ability to move in complex environments is a fundamental requirement for robots to be a part of our daily lives. Increasing the controller complexity may be a desirable choice in order to obtain an improved performance. However, these two aspects may pose a considerable challenge on the optimization of robotic controllers. In this paper, we study the trade-offs between the complexity of reactive controllers and the complexity of the environment in the optimization of multi-robot obstacle avoidance for resource-constrained platforms. The optimization is carried out in simulation using a distributed, noise-resistant implementation of Particle Swarm Optimization, and the resulting controllers are evaluated both in simulation and with real robots. We show that in a simple environment, linear controllers with only two parameters perform similarly to more complex non-linear controllers with up to twenty parameters, even though the latter ones require more evaluation time to be learned. In a more complicated environment, we show that there is an increase in performance when the controllers can differentiate between front and backwards sensors, but increasing further the number of sensors and adding non-linear activation functions provide no further benefit. In both environments, augmenting reactive control laws with simple memory capabilities causes the highest increase in performance. We also show that in the complex environment the performance measurements are noisier, the optimal parameter region is smaller, and more iterations are required for the optimization process to converge.

关键词： Optimization Complexity theory Robot sensing systems Mobile robots Wheels

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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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distributed Particle Swarm Optimization - particle allocation and neighborhood topologies for the learning of cooperative robotic behaviors

Distributed Particle Swarm Optimization - particle allocatio...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： I. Navarro E. Di Mario A. Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Poly technique Federale de Lausanne

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimization (PSO) to learn in simulation a set of 50 weights of an artificial neural network. They differ on the way the particles are allocated and evaluated on the robots, and on how the PSO neighborhood is implemented. In addition, we use a centralized approach that allows for benchmarking with the distributed versions. Regardless of the learning approach, each robot measures locally and individually the performance of the group using exclusively on-board resources. Results show that four of the distributed variations obtain similar fitnesses as the centralized version, and are always able to learn. The other distributed variation fails to properly learn on some of the runs, and results in lower fitness when it succeeds. We test systematically the controllers learned in simulation in real robot experiments.

关键词： Robot kinematics Mobile robots Robot sensing systems Collision avoidance Measurement Particle swarm optimization

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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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Real-time Optimization of Trajectories that Guarantee the Rendezvous of Mobile Robots

Real-time Optimization of Trajectories that Guarantee the Re...

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IEEE/RSJ International Conference on intelligent Robotics and systems

作者： Sven Gowal Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne.

ISBN: (纸本)9781467317375

Since the 1960s, consensus problems have puzzled the minds of many researchers in fields ranging from computer science to information aggregation. In this work, although specifically addressing the rendezvous problem for a team of mobile robots, we develop a methodology that can also be applied to other consensus problems, where optimality is important and where non-holonomicity characterizes the system at hand. In particular, we consider a group of differential-wheeled robots endowed with noisy relative positioning capabilities. We develop a distributed, real-time optimization method based on a receding horizon controller that minimizes a user-defined cost whilst guaranteeing the rendezvous. Finally, we perform experiments on real robots to confirm the validity of our approach.

关键词： ranging where non-holonomicity positioning rendezvous Mobile robots distance measurement trajectory optimization method Patient Positioning computer science optimality Robot

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