Alessandro Renzaglia

Short Bio:

I'm a research faculty member in the Chroma team at Inria Lyon/CITI Lab. I received my M.Sc. degree in Physics from the University of Rome La Sapienza, Italy, and my Ph.D. degree in Computer Science from the University of Grenoble, France.

Successively I have been Postdoctoral Researcher in the Robotic Sensor Networks Lab at the Computer Science & Engineer Department, University of Minnesota, Minneapolis, USA, and later with the Laboratory for Analysis and Architecture of Systems (LAAS), Toulouse, France, in the Robotics and Interactions group, before to join the Chroma team.

My main research interests include multi-robot systems, autonomous vehicles, path planning, and optimization.

Main Research

  • Distributed Multi-Robot Exploration of 3D environments: This work, developed in the Chroma team at INRIA, addresses the problem of exploring unknown terrains with a fleet of cooperating aerial vehicles. We presented a novel decentralized approach which alternates gradient-free stochastic optimization and frontier-based approaches. Our method allows each robot to generate its trajectory based on the collected data and the local map built integrating the information shared by its teammates. Whenever a local optimum is reached, which corresponds to a location surrounded by already explored areas, the algorithm identifies the closest frontier to get over it and restarts the local optimization. Its low computational cost, the capability to deal with constraints and the decentralized decision-making make it particularly suitable for multi-robot applications in complex 3D environments. Furthermore, in terms of exploration time, our algorithm significantly outperforms a standard solution based on closest frontier points while providing similar performances compared to a computationally more expensive centralized greedy solution.

  • Search and Localization of a Weak Source with a Multi-Robot Formation: This work focuses on the problem of guiding a formation of mobile robots, subject to communication constraints, from an arbitrary position to the location of the source of a physical signal in a planar environment. The information on the signal is only based on noisy measurements of its strength collected during the mission and the signal is considered to be weak and indistinguishable from the noise in a large portion of the environment. The goal of the team is thus to search for a reliable signal and finally converge to the source location. An accurate estimation of the signal gradient is obtained by fusing the data gathered by the robots while moving in a circular formation. The algorithm proposed to steer the formation exploits the gradient estimation to bias a correlated random walk, which ensures an efficient non-oriented search motion when far from the source. The resulting strategy is so able to obtain a suitable trade-off between exploration and exploitation. ( In parallel, also a work exclusively focused on the estimation of the gradient and Hessian matrix of an unknown signal via noisy measurements collected by a group of robots has been carried out. In particular, we proposed symmetric formations with a reduced number of robots for both the two-dimensional (2-D) and the three-dimensional (3-D) cases, such that the gradient and Hessian of the signal are estimated at the center of the formation via simple computation on local quantities independently of the orientation of the formation. (

  • Cooperative exploration in currents fields: This work, developed in the framework of the SkyScanner project (, focuses on the optimal trajectory planning for cooperative exploration and data gathering by using a team of fixed-wings UAVs. The goal of this project is to exploit a swarm of UAVs to collect data (winds, pressure, humidity, temperature, etc.) within low-altitude convective clouds. The resulting study of their behavior and evolution is aimed to improve meteorological models and, consequently, small and large scale weather forecasts. The central research topic I am focusing on is the achievement of complex cooperative tasks in highly dynamic environments and in presence of a current field, where currents simultaneously represent the objective of study and a strong constraint for the motion. (

  • Limited energy optimal search: This research addresses a fundamental search problem in which a searcher subject to time and energy constraints tries to find a mobile target. The target's motion is modeled as a random walk on a discrete set of points. We initially consider 1D bounded environments and study the process taking into account two different detection models. The objective is to optimize the motion, seen as a sequence of actions, such that the probability of capturing the random-walking target is maximized. We assume that move and stay actions correspond to different energy costs. The problem has been approached analytically as well as modeled as a Partially Observable Markov Decision Process (POMDP) and solved using a reduced state-space representation of the belief. This work is part of a collaborative project funded by the National Science Foundation: A Robotic Network for Locating and Removing Invasive Carp from Inland Lakes (

  • Stochastic optimization for optimal coverage: This research has been carried out in the framework of the European project sFly ( The objective of this project was to develop several small and safe helicopters which can fly autonomously in city-like environments and which can be used to assist humans in rescue and monitoring tasks. The main motivations were not only to achieve tasks impossible for a human team, but also to be able to substitute the human intervention in very dangerous scenarios. This means that the helicopters must be able to operate in complex environments where GPS signals are often shadowed, cooperatively and in a complete autonomous way. This involves a number of challenges on all levels of helicopter design, perception, actuation, control, navigation and power supply that have yet to be solved. My research focused on the problem of deploying a swarm of Micro Air Vehicles (MAV) to perform surveillance coverage mission over an unknown terrain of arbitrary morphology. Since the considered terrain's morphology is unknown and it can be complex and non-convex, standard optimization algorithms are not applicable to this particular problem. For this reason, a new approach based on the Cognitive-based Adaptive Optimization (CAO) algorithm has been proposed. A fundamental property of this approach is that it shares the same convergence characteristics as those of constrained gradient-descent algorithms, which require perfect knowledge of the terrain's morphology. In addition, we also proposed a different formulation of the problem in order to obtain a distributed solution, which allows us to overcome the drawbacks of a centralized approach and to consider also limited communication capabilities. The proposed method was finally implemented in a real swarm of MAVs to carry out surveillance coverage in an outdoor complex area near Zürich. (

Professional Activities

Associated Editor:

  • IROS 2021 - 2022 - IEEE International Conference on Intelligent Robots and Systems

Member of Program Committee:

  • DARS 2014 - 2016 - 2018 - 2021 - 2022 - International Symposium on Distributed Autonomous Robotic System

  • DATE/ASD 2021,- 2022 - Design, Automation and Test in Europe Conference, Special Initiative on Autonomous Systems Design (ASD)

  • MRS 2017 - International Symposium on Multi-Robot and Multi-Agent Systems

  • RSS 2015 - Robotics: Science and Systems Conference

  • WiSARN 2016 - International Workshop on Wireless Sensor, Actuator and Robot Networks