Beyond onboard sensors in robotic swarms: Local collective sensing through situated communication

The constituent robots in swarm robotics systems are typically equipped with relatively simple, onboard sensors of limited quality and range. When robots have the capacity to communicate with one another, communication has so far been exclusively used for coordination. In this paper, we present a novel approach in which local, situated communication is leveraged to overcome the sensory limitations of the individual robots. In our approach, robots share sensory inputs with neighboring robots, thereby effectively extending each other’s sensory capabilities. We evaluate our approach in a series of experiments in which we evolve controllers for robots to capture mobile preys. We compare the performance of (i) swarms that use our approach, (ii) swarms in which robots use only their limited onboard sensors, and (iii) swarms in which robots are equipped with ideal sensors that provide extended sensory capabilities without the need for communication. Our results show that swarms in which local communication is used to extend the sensory capabilities of the individual robots outperform swarms in which only onboard sensors are used. Our results also show that in certain experimental configurations, the performance of swarms using our approach is close to the performance of swarms with ideal sensors.info:eu-repo/semantics/acceptedVersio

Beyond Onboard Sensors in Robotic Swarms - Local Collective Sensing through Situated Communication

The constituent robots in swarm robotics systems are typically equipped with relatively simple, onboard sensors of limited quality and range. When robots have the capacity to communicate with one another, communication has so far been exclusively used for coordination. In this paper, we present a novel approach in which local, situated communication is leveraged to overcome the sensory limitations of the individual robots. In our approach, robots share sensory inputs with neighboring robots, thereby effectively extending each other’s sensory capabilities. We evaluate our approach in a series of experiments in which we evolve controllers for robots to capture mobile preys. We compare the performance of (i) swarms that use our approach, (ii) swarms in which robots use only their limited onboard sensors, and (iii) swarms in which robots are equipped with ideal sensors that provide extended sensory capabilities without the need for communication. Our results show that swarms in which local communication is used to extend the sensory capabilities of the individual robots outperform swarms in which only onboard sensors are used. Our results also show that in certain experimental configurations, the performance of swarms using our approach is close to the performance of swarms with ideal sensors.info:eu-repo/semantics/acceptedVersio

Engineering evolutionary control for real-world robotic systems

Evolutionary Robotics (ER) is the field of study concerned with the application of evolutionary computation to the design of robotic systems. Two main issues have prevented ER from being applied to real-world tasks, namely scaling to complex tasks and the transfer of control to real-robot systems. Finding solutions to complex tasks is challenging for evolutionary approaches due to the bootstrap problem and deception. When the task goal is too difficult, the evolutionary process will drift in regions of the search space with equally low levels of performance and therefore fail to bootstrap. Furthermore, the search space tends to get rugged (deceptive) as task complexity increases, which can lead to premature convergence. Another prominent issue in ER is the reality gap. Behavioral control is typically evolved in simulation and then only transferred to the real robotic hardware when a good solution has been found. Since simulation is an abstraction of the real world, the accuracy of the robot model and its interactions with the environment is limited. As a result, control evolved in a simulator tends to display a lower performance in reality than in simulation. In this thesis, we present a hierarchical control synthesis approach that enables the use of ER techniques for complex tasks in real robotic hardware by mitigating the bootstrap problem, deception, and the reality gap. We recursively decompose a task into sub-tasks, and synthesize control for each sub-task. The individual behaviors are then composed hierarchically. The possibility of incrementally transferring control as the controller is composed allows transferability issues to be addressed locally in the controller hierarchy. Our approach features hybridity, allowing different control synthesis techniques to be combined. We demonstrate our approach in a series of tasks that go beyond the complexity of tasks where ER has been successfully applied. We further show that hierarchical control can be applied in single-robot systems and in multirobot systems. Given our long-term goal of enabling the application of ER techniques to real-world tasks, we systematically validate our approach in real robotic hardware. For one of the demonstrations in this thesis, we have designed and built a swarm robotic platform, and we show the first successful transfer of evolved and hierarchical control to a swarm of robots outside of controlled laboratory conditions.A Robótica Evolutiva (RE) é a área de investigação que estuda a aplicação de computação evolutiva na conceção de sistemas robóticos. Dois principais desafios têm impedido a aplicação da RE em tarefas do mundo real: a dificuldade em solucionar tarefas complexas e a transferência de controladores evoluídos para sistemas robóticos reais. Encontrar soluções para tarefas complexas é desafiante para as técnicas evolutivas devido ao bootstrap problem e à deception. Quando o objetivo é demasiado difícil, o processo evolutivo tende a permanecer em regiões do espaço de procura com níveis de desempenho igualmente baixos, e consequentemente não consegue inicializar. Por outro lado, o espaço de procura tende a enrugar à medida que a complexidade da tarefa aumenta, o que pode resultar numa convergência prematura. Outro desafio na RE é a reality gap. O controlo robótico é tipicamente evoluído em simulação, e só é transferido para o sistema robótico real quando uma boa solução tiver sido encontrada. Como a simulação é uma abstração da realidade, a precisão do modelo do robô e das suas interações com o ambiente é limitada, podendo resultar em controladores com um menor desempenho no mundo real. Nesta tese, apresentamos uma abordagem de síntese de controlo hierárquica que permite o uso de técnicas de RE em tarefas complexas com hardware robótico real, mitigando o bootstrap problem, a deception e a reality gap. Decompomos recursivamente uma tarefa em sub-tarefas, e sintetizamos controlo para cada subtarefa. Os comportamentos individuais são então compostos hierarquicamente. A possibilidade de transferir o controlo incrementalmente à medida que o controlador é composto permite que problemas de transferibilidade possam ser endereçados localmente na hierarquia do controlador. A nossa abordagem permite o uso de diferentes técnicas de síntese de controlo, resultando em controladores híbridos. Demonstramos a nossa abordagem em várias tarefas que vão para além da complexidade das tarefas onde a RE foi aplicada. Também mostramos que o controlo hierárquico pode ser aplicado em sistemas de um robô ou sistemas multirobô. Dado o nosso objetivo de longo prazo de permitir o uso de técnicas de RE em tarefas no mundo real, concebemos e desenvolvemos uma plataforma de robótica de enxame, e mostramos a primeira transferência de controlo evoluído e hierárquico para um exame de robôs fora de condições controladas de laboratório.This work has been supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) under the grants SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013, and by Instituto de Telecomunicações under the grant UID/EEA/50008/2013