Eliseo Ferrante

• PhD
• Professor (Assistant) at Vrije Universiteit Amsterdam

We introduce an elasticity-based mechanism that drives active particles to self-organize by cascading self-propulsion energy towards lower-energy modes. We illustrate it on a simple model of self-propelled agents linked by linear springs that reach a collectively rotating or translating state without requiring aligning interactions. We develop an a...

In flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and bearing information about neighboring robots; and alignment control, which allows the robots to align in a common direction and uses...

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature f...

In this paper we propose GESwarm, a novel tool that can automatically synthesize collective behaviors for swarms of autonomous robots through evolutionary robotics. Evolutionary robotics typically relies on artificial evolution for tuning the weights of an artificial neural network that is then used as individual behavior representation. The main c...

We introduce a simple model of self-propelled particles connected by linear springs that describes a semi-rigid formation of active agents without explicit alignment rules. The model displays a discontinuous transition at a critical noise level, below which the group self-organizes into a collectively translating or rotating state. We identify a no...

In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard...

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic senso...

Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In th...

Legged robots are well-suited for deployment in unstructured environments but require a unique control scheme specific for their design. As controllers optimised in simulation do not transfer well to the real world (the infamous sim-to-real gap), methods enabling quick learning in the real world, without any assumptions on the specific robot model...

In this paper, we compare Bayesian Optimization, Differential Evolution, and an Evolution Strategy employed as a gait-learning algorithm in modular robots. The motivational scenario is the joint evolution of morphologies and controllers, where “newborn” robots also undergo a learning process to optimize their inherited controllers (without changing...

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic senso...

A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is desi...

In this paper, we present a new method for a swarm to collectively sense and follow a gradient in the environment. The agents in the swarm only rely on relative distance and bearing measurements of neighbors. Additionally, only a minority of agents in the swarm perceive the scalar value of the gradient at their location. We test the method with inc...

Strongly opinionated minorities can have a dramatic impact on the opinion dynamics of a large population. Two factions of inflexible minorities, polarised into two competing opinions, could lead the entire population to persistent indecision. Equivalently, populations can remain undecided when individuals sporadically change their opinion based on...

Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope. State-of-the-Art solutions leverage Ultra-WideBand (UWB) technology, allowing distance range measur...

Legged robots, locomoting through ‘limbs’, are well-suited for deployment in unstructured environments. Limbs allow a large range of robot morphologies, with various strengths, but each requiring a unique control scheme. As controllers optimized in simulation do not transfer well to the real world (the infamous sim-to-real gap), methods enabling qu...

In the field of evolutionary robotics, choosing the correct genetic representation is a complicated and delicate matter, especially when robots evolve behaviour and morphology at the same time. One principal problem is the lack of methods or tools to investigate and compare representations. In this paper we introduce and evaluate such a tool based...

In Evolutionary Robotics where both body and brain are malleable, it is common practice to evaluate individuals in isolated environments. With the objective of implementing a more naturally plausible system, we designed a single interactive ecosystem for robots to be evaluated in. In this ecosystem robots are physically present and can interact eac...

In this paper, we present a swarm robotics control and coordination approach that can be used for locating a moving target or source in a GNSS-denied indoor setting. The approach is completely onboard and can be deployed on nano-drones such as the Crazyflies. The swarm acts on a simple set of rules to identify and trail a dynamically changing sourc...

Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through different virtual potential sources in real-time has prov...

Strongly opinionated minorities can have a dramatic impact on the opinion dynamics of a large population. Two factions of inflexible minorities, polarised into two competing opinions, could lead the entire population to persistent indecision. Equivalently, populations can remain undecided when individuals sporadically change their opinion based on...

We propose a method for the chain formation of multiple agents in an open space. Chaining can be considered as a building block for several application scenarios, including exploration, maintaining connectivity, or path formation. The proposed method was designed for a very sensing and computationally constrained robot platform, more specifically f...

In this paper, we present “bearing-and-range-only” approach for a self-organized flocking, which allows the flocking alignment without the assumption of measuring agent’s velocity or orientation. This last assumption challenges the implementation with real robot, since common off-the-shelf sensors do not provide such information unless inter-agent...

In this paper, we study the problem of collective and emergent sensing with a flying robot swarm in which social interactions among individuals lead to following the gradient of a scalar field in the environment without the need of any gradient sensing capability. We proposed two methods—desired distance modulation and speed modulation—with and wit...

In this paper, we introduce a distributed autonomous flocking behavior of Unmanned Aerial Vehicles (UAVs) in demanding outdoor conditions, motivated by search and rescue applications. We propose a novel approach for decentralized swarm navigation in the direction of a candidate object of interest (OOI) based on real-time detections from onboard RGB...

This paper is concerned with learning transferable contact models for aerial manipulation tasks. We investigate a contact-based approach for enabling unmanned aerial vehicles with cable-suspended passive grippers to compute the attach points on novel payloads for aerial transportation. This is the first time that the problem of autonomously generat...

Designing controllers for robot swarms is challenging, because human developers have typically no good understanding of the link between the details of a controller that governs individual robots and the swarm behavior that is an indirect result of the interactions between swarm members and the environment. In this paper we investigate whether an e...

This paper is concerned with learning transferable contact models for aerial manipulation tasks. We investigate a contact-based approach for enabling unmanned aerial vehicles with cable-suspended passive grippers to compute the attach points on novel payloads for aerial transportation. This is the first time that the problem of autonomously generat...

Designing controllers for robot swarms is challenging, because human developers have typically no good understanding of the link between the details of a controller that governs individual robots and the swarm behaviour that is an indirect result of the interactions between swarm members and the environment. In this paper we investigate whether an...

Evolving morphologies and controllers of robots simultaneously leads to a problem: Even if the parents have well-matching bodies and brains, the stochastic recombination can break this match and cause a body-brain mismatch in their offspring. We argue that this can be mitigated by having newborn robots perform a learning process that optimizes thei...

In this paper we study a generalized case of best-of- n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their o...

In the field of evolutionary robotics, choosing the correct encoding is very complicated, especially when robots evolve both behaviours and morphologies at the same time. With the objective of improving our understanding of the mapping process from encodings to functional robots, we introduce the biological notion of heritability, which captures th...

Local interactions and communication are key features in swarm robotics, but they are most often fixed at design time, limiting flexibility and causing a stiff and inefficient response to changing environments. Motivated by the need for higher adaptation abilities, we propose that information about emergent collective structures should percolate on...

The elephant in the room for evolutionary robotics is the reality gap. In the history of the field, several studies investigated this phenomenon on fixed robot morphologies where only the controllers evolved. This paper addresses the reality gap in a wider context, in a system where both morphologies and controllers evolve. In this context the morp...

The joint evolution of morphologies and controllers of robots leadsto a problem: Even if the parents have well-matching bodies andbrains, the stochastic recombination can break this match and causea body-brain mismatch in their offspring. This can be mitigatedby having newborn robots perform a learning process that opti-mizes their inherited brain...

Evolutionary robot systems are usually affected by the properties of the environment indirectly through selection. In this paper, we present and investigate a system where the environment also has a direct effect—through regulation. We propose a novel robot encoding method where a genotype encodes multiple possible phenotypes, and the incarnation o...

In swarm robotics, self-organized aggregation refers to a collective process in which robots form a single aggregate in an arbitrarily chosen aggregation site among those available in the environment, or just in an arbitrarily chosen location. Instead of focusing exclusively on the formation of a single aggregate, in this study we discuss how to de...

In this paper we study the effect of inflexible individuals with fixed opinions, or zealots, on the dynamics of the best-of-n collective decision making problem, using both the voter model and the majority rule decision mechanisms. We consider two options with different qualities, where the lower quality option is associated to a higher number of z...

Animals can carry their environmental sensing abilities beyond their own limits by using the advantage of being in a group. Some animal groups use this collective ability to migrate or to react to an environmental cue. The environmental cue sometimes consists of a gradient in space, for example represented by food concentration or predators’ odors....

Aggregation is a process observed in natural systems whereby individuals gather together to form large cluster. Recent studies with cockroaches and robots have shown that relatively simple individual mechanisms can account for how individuals manage to gather on a single shelter when two or more are available in the environment. In this paper, we u...

We study how the structure of the interaction network affects self-organized collective motion in two minimal models of self-propelled agents: the Vicsek model and the Active-Elastic (AE) model. We perform simulations with topologies that interpolate between a nearest-neighbour network and random networks with different degree distributions to anal...

The field of Evolutionary Robotics addresses the challenge of automatically designing robotic systems. Furthermore, the field can also support biological investigations related to evolution. In this paper, we evolve (simulated) modular robots under diverse environmental conditions and analyze the influences that these conditions have on the evolved...

Evolutionary robot systems are usually affected by the properties of the environment indirectly through selection. In this paper, we present and investigate a system where the environment also has a direct effect: through regulation. We propose a novel robot encoding method where a genotype encodes multiple possible phenotypes, and the incarnation...

While direct local communication is very important for the organization of robot swarms, so far it has mostly been used for relatively simple tasks such as signaling robots preferences or states. Inspired by the emergence of meaning found in natural languages, more complex communication skills could allow robot swarms to tackle novel situations in...

Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of...

In this paper, we use simulated swarms of robots to further explore the aggregation dynamics generated by these simple individual mechanisms. Our objective is to study the introduction of "informed robots", and to study how many of these are needed to direct the aggregation process toward a pre-defined site among those available in the environment....

Probabilistic aggregation is a self-organised behaviour studied in swarm robotics. It aims at gathering a population of robots in the same place, in order to favour the execution of other more complex collective behaviours or tasks. However, probabilistic aggregation is extremely sensitive to experimental conditions, and thus requires specific para...

Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adapti...

The ability to collectively choose the best among a finite set of alternatives is a fundamental cognitive skill for robot swarms. In this paper, we propose a formal definition of the best-of-n problem and a taxonomy that details its possible variants. Based on this taxonomy, we analyze the swarm robotics literature focusing on the decision-making p...

In this paper, we propose a collective decision-making method for swarms of robots. The method enables a robot swarm to select, from a set of possible actions, the one that has the fastest mean execution time. By means of positive feedback the method achieves consensus on the fastest action. The novelty of our method is that it allows robots to col...

Achieving fast and accurate collective decisions with a large number of simple agents without relying on a central planning unit or on global communication is essential for developing complex collective behaviors. In this paper, we investigate the speed versus accuracy trade-off in collective decision-making in the context of a binary discriminatio...

Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A...

Dynamism was originally defined as the proportion of online versus offline orders in the literature on dynamic logistics. Such a definition however, loses meaning when considering purely dynamic problems where all customer requests arrive dynamically. Existing measures of dynamism are limited to either (1) measuring the proportion of online versus...

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to a...

We consider a model of self-propelled agents with spring-like interactions that depend only on relative positions, and not on relative orientations. We observe that groups of these agents self-organize to achieve collective motion (CM) through a mechanism based on the cascading of self-propulsion energy towards lower elastic modes. By computing the...

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature f...

We propose a self-adaptive communication strategy for controlling the heading direction of a swarm of mobile robots during flocking. We consider the problem where a small group of informed robots has to guide a large swarm along a desired direction. We consider three versions of this problem: one where the desired direction is fixed; one where the...

We introduce a simple model of self-propelled agents connected by linear springs, with no explicit alignment rules. Below a critical noise level, the agents self-organize into a collectively translating or rotating group. We derive analytical stability conditions for the translating state in an elastic sheet approximation. We propose an elasticity-...

In this paper, we present a novel method for performing collective transport in the presence of obstacles. Three robots are physically connected to an object to be transported from a start to a goal location. The task is particularly challenging because the robots have a heterogeneous perception of the environment. In fact, the goal and the obstacl...

In this paper, we reinterpret the most basic exponential smoothing equation, S t + 1 = (1 − α)S t + αX t , as a model of social influence. This equation is typically used to estimate the value of a series at time t + 1, denoted by S t + 1, as a convex combination of the current estimate S t and the actual observation of the time series X t . In our...

We present a novel multi-robot simulator named ARGoS. ARGoS is designed to simulate complex experiments involving large swarms of robots of different types. ARGoS is the first multi-robot simulator that is at the same time both efficient (fast performance with many robots) and flexible (highly customizable for specific experiments). Novel design ch...