Autonomous construction using scarce resources in unknown environments: Ingredients for an intelligent robotic interaction with the physical world

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a "real but contrived” experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real worl

Autonomous Construction of Separated Artifacts by Mobile Robots Using SLAM and Stigmergy

Autonomous mobile robots equipped with arms have the potential to be used for automated construction of structures in various sizes and shapes, such as houses or other infrastructures. Existing construction processes, like many other additive manufacturing processes, are mostly based on precise positioning, which is achieved by machines that have a fixed mechanical link with the construction and therefore relying on absolute positioning. Mobile robots, by nature, do not have a fixed referential point, and their positioning systems are not as accurate as fixed-based systems. Therefore, mobile robots have to employ new technologies and/or methods to implement precise construction processes. In contrast to the majority of prior work on autonomous construction that has relied only on external tracking systems (e.g., GPS) or exclusively on short-range relative localization (e.g., stigmergy), this paper explores localization methods based on a combination of long-range self-positioning and short-range relative localization for robots to construct precise, separated artifacts in particular situations, such as in outer space or in indoor environments, where external support is not an option. Achieving both precision and autonomy in construction tasks requires understanding the environment and physically interacting with it. Consequently, we must evaluate the robot’s key capabilities of navigation and manipulation for performing the construction in order to analyze the impact of these capabilities on a predefined construction. In this paper, we focus on the precision of autonomous construction of separated artifacts. This domain motivates us to combine two methods used for the construction: 1) a self-positioning system and 2) a short-distance relative localization. We evaluate our approach on a miniature mobile robot that autonomously maps an environment using a simultaneous localization and mapping (SLAM) algorithm; the robot’s objective is then to manipulate blocks to build desired artifacts based on a plan given by a human. Our results illuminate practical issues for future applications that also need to integrate complex tasks under mobile robot constraints

Model Based On-Line Energy Prediction System for Semi-Autonomous Mobile Robots

Maximizing energy autonomy is a consistent challenge when deploying mobile robots in ionizing radiation or other hazardous environments. Having a reliable robot system is essential for successful execution of missions and to avoid manual recovery of the robots in environments that are harmful to human beings. For deployment of robots missions at short notice, the ability to know beforehand the energy required for performing the task is essential. This paper presents a on-line method for predicting energy requirements based on the pre-determined power models for a mobile robot. A small mobile robot, Khepera III is used for the experimental study and the results are promising with high prediction accuracy. The applications of the energy prediction models in energy optimization and simulations are also discussed along with examples of significant energy savings

Robotique collective et auto-assemblage:une étude mécatronique

We present a study of collective robotics by including a mechatronics point of view. In the field it is usually claimed that collective robots are simple and relatively cheap because they are produced in large quantities. Instead in our study we show that collective robots are not simple because they need sensors and actuators for additional work. Our experience in designing robots and producing them allows us to analyze the manufacturing costs of different collective robots . We present in this work four robots developed prior and during to this thesis. Chapter 2 concerns the e-puck robot. This is a robot designed for education, however, it is used in research to experiment collective behaviors. The s-bot robot is presented in Chapter 3. This is a robot that has the collective ability to self-assemble to form larger structures and increase its performance. In Chapter 4 we present the marXbot robot. It is a modular robot developed in the laboratory to meet the needs of different research hubs. One of its modules allows it to self-assemble with its teammates and with the robot handbot presented in Chapter 5. The handbot is a robot that can climb and handle objects. It can climb for example a shelf to grasp a book. On the ground it is transported by marXbot. In Chapter 6 the performance of robots are presented. We expose a study of their performance gain when the robots are self-assembled. Finally we compare in Chapter 7 the four robots from the mechatronics point of view and in respect to their cost

Autonomous construction using scarce resources in unknown environments - Ingredients for an intelligent robotic interaction with the physical world

The goal of creating machines that autonomously perform useful work in a safe, robust and intelligent manner continues to motivate robotics research. Achieving this autonomy requires capabilities for understanding the environment, physically interacting with it, predicting the outcomes of actions and reasoning with this knowledge. Such intelligent physical interaction was at the centre of early robotic investigations and remains an open topic. In this paper, we build on the fruit of decades of research to explore further this question in the context of autonomous construction in unknown environments with scarce resources. Our scenario involves a miniature mobile robot that autonomously maps an environment and uses cubes to bridge ditches and build vertical structures according to high-level goals given by a human. Based on a "real but contrived" experimental design, our results encompass practical insights for future applications that also need to integrate complex behaviours under hardware constraints, and shed light on the broader question of the capabilities required for intelligent physical interaction with the real world

Towards a Formal Verification Methodology for Collective Robotic Systems

We introduce a UML-based notation for graphically modeling systems’ security aspects in a simple and intuitive way and a model-driven process that transforms graphical specifications of access control policies in XACML. These XACML policies are then translated in FACPL, a policy language with a formal semantics, and the resulting policies are evaluated by means of a Java-based software tool

Software integration in mobile robotics, a science to scale up machine intelligence

The present work tackles integration in mobile robotics. Integration is often considered to be a mere technique, unworthy of scientific investigation. On the contrary, we show that integrating capabilities in a mobile robot entails new questions that the parts alone do not feature. These questions reflect the structure of the application and the physics of the world. We also show that a successful integration process transforms the parts themselves and allows to scale up mobile-robot intelligence in real-world applications. In Chapter 2 we present the hardware. In Chapter 3, we show that building a low-level control architecture considering the mechanic and electronic reality of the robot improves the performances and allows to integrate a large number of sensors and actuators. In Chapter 4, we show that globally optimising mechatronic parameters considering the robot as a whole allows to implement slam using an inexpensive sensor with a low processor load. In Chapter 5, we show that based on the output from the slam algorithm, we can combine infrared proximity sensors and vision to detect objects and to build a semantic map of the environment. We show how to find free paths for the robot and how to create a dual geometric-symbolic representation of the world. In Chapter 6, we show that the nature of scenarios influences the implementation of a task-planning algorithm and changes its execution properties. All these chapters contribute results that together prove that integration is a science. In Chapter 7, we show that combining these results improves the state of the art in a difficult application : autonomous construction in unknown environments with scarce resources. This application is interesting because it is challenging at multiple levels : For low-level control, manipulating objects in the real world to build structures is difficult. At the level of perceptions, the fusion of multiple heterogeneous inexpensive sensors is not trivial, because these sensors are noisy and the noise is non-Gaussian. At the level of cognition, reasoning about elements from an unknown world in real time on a miniature robot is demanding. Building this application upon our other results proves that integration allows to scale up machine intelligence, because this application shows intelligence that is beyond the state of the art, still only combining basic components that are individually slightly behind the state of the art