Jorge C. Gomes

Evolution of swarm robotics systems with novelty search

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task—aggregation, and a more challenging task—sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.

Generic behaviour similarity measures for evolutionary swarm robotics

Novelty search has shown to be a promising approach for the evolution of controllers for swarms of robots. In existing studies, however, the experimenter had to craft a task-specific behaviour similarity measure. The reliance on hand-crafted similarity measures places an additional burden to the experimenter and introduces a bias in the evolutionary process. In this paper, we propose and compare two generic behaviour similarity measures: combined state count and sampled average state. The proposed measures are based on the values of sensors and effectors recorded for each individual robot of the swarm. The characterisation of the group-level behaviour is then obtained by combining the sensor-effector values from all the robots. We evaluate the proposed measures in an aggregation task and in a resource sharing task. We show that the generic measures match the performance of task-specific measures in terms of solution quality. Our results indicate that the proposed generic measures operate as effective behaviour similarity measures, and that it is possible to leverage the benefits of novelty search without having to craft task-specific similarity measures.

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots.

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.

Progressive Minimal Criteria Novelty Search

We propose progressive minimal criteria novelty search (PMCNS), which is an extension of minimal criteria novelty search. In PMCNS, we combine the respective benefits of novelty search and fitness-based evolution by letting novelty search freely explore new regions of behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our approach in the evolution of neurocontrollers for a swarm of robots in a coordination task where robots must share a single charging station. The robots can only survive by periodically recharging their batteries. We compare the performance of PMCNS with (i) minimal criteria novelty search, (ii) pure novelty search, (iii) pure fitness-based evolution, and (iv) with evolutionary search based on a linear blend of novelty and fitness. Our results show that PMCNS outperforms all four approaches. Finally, we analyse how different parameter setting in PMCNS influence the exploration of the behaviour space.

A Land Use Planning Ontology: LBCS

Abstract: Urban planning has a considerable impact on the economic performance of cities and on the quality of life of their populations. Efficiency at this level has been hampered by the lack of integrated tools to adequately describe urban space in order to formulate appropriate design solutions. This paper describes an ontology called LBCS-OWL2 specifically developed to overcome this flaw, based on the Land Based Classification Standards (LBCS), a comprehensive and detailed land use standard to describe the different dimensions of urban space. The goal is to provide semantic and computer-readable land use descriptions of geo-referenced spatial data. This will help to make programming strategies available to those involved in the urban development process. There are several advantages to transferring a land use standard to an OWL2 land use ontology: it is modular, it can be shared and reused, it can be extended and data consistency maintained, and it is ready for integration, thereby supporting the interoperability of different urban planning applications. This standard is used as a basic structure for the “City Information Modelling” (CIM) model developed within a larger research project called City Induction, which aims to develop a tool for urban planning and design.

Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics

Evolutionary techniques driven by behavioural diversity, such as novelty search, have shown significant potential in evolutionary robotics. These techniques rely on priorly specified behaviour characterisations to estimate the similarity between individuals. Characterisations are typically defined in an ad hoc manner based on the experimenters intuition and knowledge about the task. Alternatively, generic characterisations based on the sensor-effector values of the agents are used. In this paper, we propose a novel approach that allows for systematic derivation of behaviour characterisations for evolutionary robotics, based on a formal description of the agents and their environment. Systematically derived behaviour characterisations (SDBCs) go beyond generic characterisations in that they can contain task-specific features related to the internal state of the agents, environmental features, and relations between them. We evaluate SDBCs with novelty search in three simulated collective robotics tasks. Our results show that SDBCs yield a performance comparable to the task-specific characterisations, in terms of both solution quality and behaviour space exploration.

Novelty-driven cooperative coevolution

Cooperative coevolutionary algorithms (CCEAs) rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous agents, where each agent is modelled as a component of the solution. Previous works have, however, shown that CCEAs are biased toward stability: the evolutionary process tends to converge prematurely to stable states instead of (near-)optimal solutions. In this study, we show how novelty search can be used to avoid the counterproductive attraction to stable states in coevolution. Novelty search is an evolutionary technique that drives evolution toward behavioural novelty and diversity rather than exclusively pursuing a static objective. We evaluate three novelty-based approaches that rely on, respectively (1) the novelty of the team as a whole, (2) the novelty of the agents’ individual behaviour, and (3) the combination of the two. We compare the proposed approaches with traditional fitness-driven cooperative coevolution in three simulated multirobot tasks. Our results show that team-level novelty scoring is the most effective approach, significantly outperforming fitness-driven coevolution at multiple levels. Novelty-driven cooperative coevolution can substantially increase the potential of CCEAs while maintaining a computational complexity that scales well with the number of populations.

Hybrid Control for a Real Swarm Robotics System in an Intruder Detection Task

Control design is one of the prominent challenges in the field of swarm robotics. Evolutionary robotics is a promising approach to the synthesis of self-organized behaviors for robotic swarms but it has, so far, only produced been shown in relatively simple collective behaviors. In this paper, we explore the use of a hybrid control synthesis approach to produce control for a swarm of aquatic surface robots that must perform an intruder detection task. The robots have to go to a predefined area, monitor it, detect and follow intruders, and manage their energy levels by regularly recharging at a base station. The hybrid controllers used in our experiments rely on evolved behavior primitives that are combined through a manually programmed high-level behavior arbitrator. In simulation, we show how simple modifications to the behavior arbitrator can result in different swarm behaviors that use the same underlying behavior primitives, and we show that the composed behaviors are scalable with respect to the swarm size. Finally, we demonstrate the synthesized controller in a real swarm of robots, and show that the behavior successfully transfers from simulation to reality.

Design of Communication and Control for Swarms of Aquatic Surface Drones

The availability of relatively capable and inexpensive hardware components has made it feasible to consider large-scale systems of autonomous aquatic drones for maritime tasks. In this paper, we present the CORATAM and HANCAD projects, which focus on the fundamental challenges related to communication and control in swarms of aquatic drones. We argue for: (i) the adoption of a heterogeneous approach to communication in which a small subset of the drones have long-range communication capabilities while the majority carry only short-range communication hardware, and (ii) the use of decentralized control to facilitate inherent robustness and scalability. A heterogeneous communication system and decentralized control allow for the average drone to be kept relatively simple and therefore inexpensive. To assess the proposed methodology, we are currently building 25 prototype drones from off-the-shelf components. We present the current hardware designs and discuss the results of simulation-based experiments involving swarms of up to 1,000 aquatic drones that successfully patrolled a 20 km-long strip for 24 hours.

EvoRBC: Evolutionary Repertoire-based Control for Robots with Arbitrary Locomotion Complexity

The use of evolutionary robotics in robots with complex means of locomotion has, so far, mainly been limited to gait evolution. Increasing the number of degrees of freedom available to a controller significantly enlarges the search space, which in turn makes the evolution of solutions for a given task more challenging. In this paper, we present Evolutionary Repertoire-based Control (EvoRBC), an approach that enables the evolution of control for robots with arbitrary locomotion complexity. EvoRBC separates the synthesis of control into two levels: the generation of a repertoire of behavior primitives through the application of Quality Diversity techniques; and the evolution of a behavior arbitrator that uses the repertoires primitives to solve a particular task. We evaluate EvoRBC in simulated robots with different numbers of degrees of freedom in two tasks, navigation and foraging. Our results show that while standard evolutionary approaches are highly affected by the locomotion complexity of the robot, EvoRBC is consistently able to evolve high-performing solutions. We also show that EvoRBC allows for the evolution of general controllers, that can be successfully used in robots different than those with which they were evolved.