In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices. This work proposes a holistic framework for construction task specification, optimization of robot morphology, and mission execution using a mobile modular reconfigurable robot. Users can specify and monitor the desired robot behavior through a graphical interface. In contrast to existing, monolithic solutions, we automatically identify a new task-tailored robot for every task by integrating Building Information Modeling (BIM). Our framework leverages modular robot components that enable the fast adaption of robot hardware to the specific demands of the construction task. Other than previous works on modular robot optimization, we consider multiple competing objectives, which allow us to explicitly model the challenges of real-world transfer, such as calibration errors. We demonstrate our framework in simulation by optimizing robots for drilling and spray painting. Finally, experimental validation demonstrates that our approach robustly enables the autonomous execution of robotic drilling.

Classical robot-programming approaches often require the definition of rigid sequences of elementary motions and actions by expert robot programmers through coding. Such solutions are suitable for environments that are more structured and less dynamic with respect to construction ones. We propose a different approach with a novel software architecture that exploits Building Information Modelling (BIM) data for facilitating robot-programming and deployment in construction. It includes mission parametrization and customization tools that abstract elementary robot functionality in terms of behaviors, that can be easily understood, assembled, and adapted on-site by non-expert programmers. We verify the effectiveness of our proposed approach by considering the execution of a spray-painting use-case for interior walls and a usability study.

Despite many recent advances both in terms of analytical and learning based approaches, grasping remains a challenging open problem in robotic manipulation. The major-ity of the research focuses on enhancing grasping capabilities by designing strategies characterized by different degree of intelligence for a given gripper. Our proposed approach to effective grasping is different: instead of optimizing a policy given a single gripper geometry, we search for the tool in order to grasp a given set of objects. To do so, we first introduce a parametrization for the geometry of two common families of grippers: two-fingers parallel-jaw and suction-cup vacuum. Then we present a novel grasp score discussing its properties for gripper design. Thanks to these we can formally cast the gripper design as an optimization problem, tackled with existing global optimization frameworks. Numerical findings on a set of industrial objects show effectiveness of our proposed approach.

We propose a safety control approach based on the online optimal scaling of the size of bounding volumes used as dynamic safety zones for collaborative robotics. Intersection tests between bounding volumes surrounding robot and human allow the safety controller to identify possible collisions. Our proposed approach optimizes online smooth stop trajectories, to be engaged if a potential collision is detected. Unlike other approaches, the robot dynamics and its torque constraints are here considered to plan optimal trajectories that minimize the size of safety zones surrounding the robot. Simulation results on a validated model of a robot with seven degrees-of-freedom verify the feasibility of the proposed approach.

In this paper, we present a novel global tracking control approach for elastic-joint robots that can be efficiently computed and is robust against model uncertainties and input disturbances. Elastic-joint robots provide enhanced safety and resiliency for interaction with the environment and humans. On the other hand, the joint elasticity complicates the motion-control problem especially when robust and precise trajectory tracking is required. Our proposed control approach allows us to merge the main benefits of the two well-known control schemes: inverse-dynamics (ID) control, which can be efficiently computed thanks to modern recursive algorithms, and passivity-based (PB) tracking control, which provides enhanced robustness to model uncertainty and external disturbances. As an extension of our previous work, we present a detailed robustness analysis of our combined ID/PB controller, a new variant of the original scheme that shows practically relevant implications, and finally, experimental results that verify the effectiveness of the approach.

We consider the global tracking control problem of robots with elastic joints. Even if joint elasticity introduces beneficial features for modern applications which require physically resilient and safer robots that can interact with the environment or humans, it challenges the achievable control performance. We propose a novel controller which combines the benefits of two approaches: the intrinsic robustness to model uncertainty from passivity-based control and the implementation efficiency of inverse-dynamics control schemes using a modern recursive algorithm. The novel controller is applied to an elastic-joint reconfigurable robotic arm using a recently proposed framework for on-the-fly control design. Simulation and experimental results validate our proposed approach.

This paper presents a time-efficient, task-based configuration synthesis algorithm for modular robot manipulators. One of the main challenges in modular manipulators is to find possible combinations of modules that are able to complete given tasks while avoiding obstacles in the environment. Most studies on modular robots focus on obtaining combinations of modules to achieve a given task without considering the required path planning in an environment with obstacles. In contrast to previous works, we present a configuration synthesis method for modular manipulators, considering collision detection and path planning in task space. Our simulations show that our approach finds possible combinations with reduced computational time compared to previous techniques.

We address the problem of controlling modular robot manipulators. The challenge of modular-robot control is that the overall system dynamics are unknown due to its flexible composition from given modules. Most previous work has faced this problem by designing decentralized controllers. Simple decentralized controllers do not guarantee global asymptotic stability without knowledge of the overall system dynamics and alternative versions involving communication with neighboring modules result in complicated control concepts. Our approach is completely different: we store parameters regarding the dynamics and kinematics of each module and a unique identification number in itself. After finishing the assembly of the modules, the parameters are gathered in a central controller, which also detects the configuration using the identification numbers. Our centralized controller uses this information to synthesize model-based control laws on-the-fly as if the full system dynamics are known beforehand. We introduce a novel and compact notation to automate this procedure and to generalize the derivation of the kinematic and dynamic model for heterogeneous modules. Finally, a possible application is shown using simulations.