Correct-by-Construction Approach for Self-Evolvable Robots

The paper presents a new formal way of modeling and designing reconfigurable robots, in which case the robots are allowed to reconfigure not only structurally but also functionally. We call such kind of robots "self-evolvable", which have the potential to be more flexible to be used in a wider range of tasks, in a wider range of environments, and with a wider range of users. To accommodate such a concept, i.e., allowing a self-evovable robot to be configured and reconfigured, we present a series of formal constructs, e.g., structural reconfigurable grammar and functional reconfigurable grammar. Furthermore, we present a correct-by-construction strategy, which, given the description of a workspace, the formula specifying a task, and a set of available modules, is capable of constructing during the design phase a robot that is guaranteed to perform the task satisfactorily. We use a planar multi-link manipulator as an example throughout the paper to demonstrate the proposed modeling and designing procedures.Comment: The paper has 17 pages and 4 figure

Robotic Monitoring of Habitats: The Natural Intelligence Approach

In this paper, we first discuss the challenges related to habitat monitoring and review possible robotic solutions. Then, we propose a framework to perform terrestrial habitat monitoring exploiting the mobility of legged robotic systems. The idea is to provide the robot with the Natural Intelligence introduced as the combination of the environment in which it moves, the intelligence embedded in the design of its body, and the algorithms composing its mind. This approach aims to solve the challenges of deploying robots in real natural environments, such as irregular and rough terrains, long-lasting operations, and unexpected collisions, with the final objective of assisting humans in assessing the habitat conservation status. Finally, we present examples of robotic monitoring of habitats in four different environments: forests, grasslands, dunes, and screes

Robotic Monitoring of Habitats: the Natural Intelligence Approach

In this paper, we first discuss the challenges related to habitat monitoring and review possible robotic solutions. Then, we propose a framework to perform terrestrial habitat monitoring exploiting the mobility of legged robotic systems. The idea is to provide the robot with the Natural Intelligence introduced as the combination of the environment in which it moves, the intelligence embedded in the design of its body, and the algorithms composing its mind. This approach aims to solve the challenges of deploying robots in real natural environments, such as irregular and rough terrains, long-lasting operations, and unexpected collisions, with the final objective of assisting humans in assessing the habitat conservation status. Finally, we present examples of robotic monitoring of habitats in four different environments: forests, grasslands, dunes, and screes

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach

Fabricate

Bringing together pioneers in design and making within architecture, construction, engineering, manufacturing, materials technology and computation, Fabricate is a triennial international conference, now in its third year (ICD, University of Stuttgart, April 2017). Each year it produces a supporting publication, to date the only one of its kind specialising in Digital Fabrication. The 2017 edition features 32 illustrated articles on built projects and works in progress from academia and practice, including contributions from leading practices such as Foster + Partners, Zaha Hadid Architects, Arup, and Ron Arad, and from world-renowned institutions including ICD Stuttgart, Harvard, Yale, MIT, Princeton University, The Bartlett School of Architecture (UCL) and the Architectural Association

Fabricate 2017

Bringing together pioneers in design and making within architecture, construction, engineering, manufacturing, materials technology and computation, Fabricate is a triennial international conference, now in its third year (ICD, University of Stuttgart, April 2017). Each year it produces a supporting publication, to date the only one of its kind specialising in Digital Fabrication. The 2017 edition features 32 illustrated articles on built projects and works in progress from academia and practice, including contributions from leading practices such as Foster + Partners, Zaha Hadid Architects, Arup, and Ron Arad, and from world-renowned institutions including ICD Stuttgart, Harvard, Yale, MIT, Princeton University, The Bartlett School of Architecture (UCL) and the Architectural Association