Construction usually involves careful preplanning and direct human operation of tools and material. Bringing automation to construction has the potential to improve its speed and efficiency, and to enable building in settings where it is difficult or dangerous for humans to work, e.g., in extraterrestrial environments or disaster areas. Nature provides us with impressive examples of animal construction: in particular, many species of termites build complex mounds several orders of magnitude larger than themselves. Inspired by termites and their building activities, our goal is to develop systems in which large numbers of robots collectively construct human-scale structures autonomously. In this thesis I present TERMES, a system comprised of (1) A high-level control algorithm for decentralized construction of 3D user-specified structures using stigmergy, exploiting implicit rather than explicit communication; and (2) A complete physical implementation where three robots reliably assemble such structures using only local sensing, limited locomotion, and simple control, exploiting embodied rather than explicit intelligence. A major contribution of this work is the translation from abstract models to a real robotic system. I achieved this through careful co-design of algorithms and physical systems and of robots and building material, allowing passive mechanical features to minimize control complexity. To attain reliable performance without relying on costly high-precision sensors and actuators, I put an emphasis on error-tolerant control, making robots able to autonomously detect and recover from small errors. This work advances the aim of engineering collectives of robots that achieve human-specified goals, using biologically-inspired principles for robustness and scalability. While our work is inspired by models of termite construction from the 1970s and 1980s, much is still unknown about how individual termites coordinate and respond to different environmental factors. To address this issue I developed methods and tools to enable high-resolution quantitative data collection on the behavior of individual termites engaged in collective construction in confined experimental arenas. This work advances our ability to study the termites which will hopefully lead to new insights on the design of robust autonomous systems for collective construction. / Engineering and Applied Sciences

The combination or integration of locomotion modes, is analyzed through the design, development, and verification of a miniature integrated jumping and gliding robot, the MultiMo-Bat, which is inspired by the locomotion strategies of vampire bats, locusts, and pelicans. This robot has a mass of between 100 and 162 grams and exhibits high jumping and gliding performance, reaching heights of over 4.5 meters, to overcome obstacles in the environment. Integration results in a smaller, lighter robot with high cooperation between the modes. This thesis presents a previously unstudied robot design concept and highlights the understudied evolutionary concept within organism mobility of integration of locomotion modes. High performance locomotion modes also require high energy density actuators. To this end, a design methodology is developed for tailoring magnetic springs to the characteristics of shape memory alloy-actuated mechanisms, which allow the MultiMo-Bat to reach jumping heights of 3.5 m with active wing deployment and full controller. Through a combinations of permanent magnets, a magnetic spring can be customized to desired characteristics; theoretically any welldefined function of force vs. displacement can be created. The methodology is not limited to SMA but can be adapted to any smart actuator, joint, or situation which requires a fixed complex force-displacement relationship with extension other interactions and magnetic field design. Robotic locomotion is also much more idealized than that of their biological counter parts. This thesis serves to highlight just how non-ideal, yet robust, biological locomotion can inspire concepts for enhancing the robustness of robot locomotion. We studied the desert locust (Schistocerca gregaria), which is adapted for jumping at the extreme limits of its surface friction, as evident by its morphological adaptations for not only jumping, but slipping. Analysis of both foot morphology and jumping behavior are used to understand how the feet interact with different surfaces, including hydrophobic glass, hydrophilic glass, wood, sandstone, and mesh. The results demonstrate a complex interplay of embodied mechanical intelligence, allowing the foot to interact and adapt passively to different surfaces without burdening the organism with additional tasks. The key morphological and dynamical features are extracted to create a concept for developing multi-Surface Locust Inspired Passively-adaptable (SLIP) feet. A simple interpretation of the concepts are then used to construct a SLIP foot for the MultiMo-Bat. These feet allow the MultiMo-Bat to reach jumping heights of well over 4 m, greater than any other electrically powered robot, and this is achieved on a 45 degree angled surface while slipping. The SLIP foot concept can be directly applied to a wide range of robot size scales, thus enhancing their dynamic terrestrial locomotion on variable surfaces.

No description available.

No description available.

No description available.

In this thesis, the evolution of adaptive behaviour in artificial agents is studied. More specifically, two types of adaptive behaviours are studied: articulated and cognitive ones. Chapter 1 presents a general introduction together with a brief presentation of the research area of this thesis, its main goals and a brief overview of the experimental studies done, the results and conclusions obtained. On chapter 2, I briefly present some promising methods that automatically generate robot controllers and/or body plans and potentially could help in the development of adaptive robots. Among these methods I present in details evolutionary robotics, a method inspired on natural evolution, and the biological background regarding adaptive behaviours in biological organisms, which provided inspiration for the studies presented in this thesis. On chapter 3, I present a detailed study regarding the evolution of articulated behaviours, i.e., behaviours that are organized in functional sub-parts, and that are combined and used in a sequential and context-dependent way, regardless if there is a structural division in the robot controller or not. The experiments performed with a single goal task, a cleaning task, showed that it is possible to evolve articulated behaviours even in this condition and without structural division of the robot controller. Also the analysis of the results showed that this type of integrated modular behaviours brought performance advantages compared to structural divided controllers. Analysis of robots' behaviours helped to clarify that the evolution of this type of behaviour depended on the characteristics of the neural network controllers and the robot's sensorimotor capacities, that in turn defined the capacity of the robot to generate opportunity for actions, which in psychological literature is often called affordances. In chapter 4, a study seeking to understand the role of reactive strategies in the evolution of cognitive solutions, i.e. those capable of integrating information over time encoding it on internal states that will regulate the robot's behaviour in the future, is presented. More specifically I tried to understand whether the existence of sub-optimal reactive strategies prevent the development of cognitive solutions, or they can promote the evolution of solutions capable of combining reactive strategies and the use of internal information for solving a response delayed task, the double t-maze. The results obtained showed that reactive strategies capable of offloading cognitive work to the agent/environmental relation can promote, rather than prevent the evolution of solutions relying on internal information. The analysis of these results clarified how these two mechanisms interact producing a hybrid superior and robust solution for the delayed response task.