Autonomous Construction by a Mobile Robot in Unknown Environments with Scarce Resources

Autonomous construction by mobile robots would be useful in various situations, such as in outer space, in hazardous environments, but also for the building industry. Current works tackle simplified scenarios where environment is flat and resources readily available; moreover robots build simple structures. However, target applications would feature complex 3D environments, remote resources, and would require the construction of multi-layer structures of various types. In this poster, we show our current work in pursuing a step toward such applications. We propose a simple experimental setup where a miniature mobile robot senses its environment and finds the right course of action to build structures. Resources are remote, requiring the robot to first locate them, then to create a way to fetch them and finally to build the structure. The robot performs SLAM to build a map and uses an HTN planner to choose its actions. It manipulates its environment through a magnetic gripper.

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

Hadi Ardiny, Francesco Mondada, Stefan John Witwicki

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.

2015

SWARM-BOT: A Swarm of Autonomous Mobile Robots with Self-Assembling Capabilities

Mirco Dorigo, Dario Floreano, Luca Maria Gambardella, André Guignard, Francesco Mondada

We present a new robotic concept, called SWARM-BOT, based on a swarm of small and simple autonomous mobile robots called S-BOTs. S-BOTs have a particular assembling capability that allows them to connect physically to other S-BOTs and form a bigger robot entity, the SWARM-BOT. A SWARM-BOT is typically composed by 10 to 30 S-BOTs physically interconnected. S-BOTs can autonomously assemble into a SWARM-BOT but also disassemble again. This feature of the S-BOTs provides SWARM-BOT with self-assembling and self-reconfiguring capabilities. Such a concept, by taking advantage from the collective and distributed approaches, ensures robustness to failures even in hard environment conditions. The approach presented finds its theoretical roots in recent studies on swarm intelligence.

2002

Functional and Adaptive Construction for Rescue

Hadi Ardiny

Although recent advances in the field of robotics have greatly increased robotic capabilities for several applications, autonomous robots are still in their infancy regarding their support of onsite construction. Unlike production robots that are used, for instance, in automotive industries, autonomous robotic systems should be designed with special considerations, such as the complexity of the cluttered and dynamic working space, inaccuracy in positioning because of the nature of mobile systems, and so forth. Thus, construction has been known as a highly complex application field for robotic systems, especially in unknown environments. In this thesis, we focus on the developments of an autonomous construction system by taking inspiration from architectural designs found in the animal kingdom. The construction system allows robots to build functional structures over a different range of environmental conditions or in unknown sites without the use of any predefined construction plan (i.e., blueprint). We call this approach adaptive and functional autonomous construction (AFAC). Indeed, AFAC is a robust and intelligent system that can tackle unforeseen problems of unknown environments and faults made during the course of building. We present two approaches for AFAC. One is a local approach that enables robots to locally sense features of their environment and then act immediately according to defined rules without using any global representation or knowledge of the world. Second is a global approach, which involves building structures by analysing global representations and knowledge produced by the robots. This approach consists of three phases: the first phase is exploration, to map the unknown environment and to find important elements. In the second phase, an effective construction plan is autonomously computed using collected data and the defined functions. Finally, in the last phase, the robot builds structures based on the computed construction plan. One interesting implementation of the introduced construction system is its potential use in a post disaster environment, where an autonomous robotic system can perform construction tasks in the rescue operation. Robots can be employed to build protective structures for rescue functions, including stabilizing large structures or protecting victims in unknown environments, where the environment are perhaps covered by debris. Inasmuch as the robots cannot plan in advance to build these structures, the rescue application is a well suited case for the study and implementation of AFAC in unknown and cluttered environments. Through implementation of AFAC in the rescue situations, we develop AFAC from scratch to the level at which the robots can autonomously apply AFAC's principles for use in the new classes of construction applications. We also study and compare the local and global approaches of AFAC. In addition, we develop new exploration methods and construction plan computation methodologies for the global approach to efficiently lead AFAC.