Industrial robots cannot be reconfigured to optimally fulfill a given task and often have to be caged to guarantee human safety. Consequently, production processes are meticulously planned so that they last for long periods to make automation affordable. However, the ongoing trend toward mass customization and small-scale manufacturing requires purchasing new robots on a regular basis to cope with frequently changing production. Modular robots are a natural answer: Robots composed of standardized modules can be easily reassembled for new tasks, can be quickly repaired by exchanging broken modules, and are cost-effective by mass-producing standard modules usable for a large variety of robot types. Despite these advantages, modular robots have not yet left research laboratories because an expert must reprogram each new robot after assembly, rendering reassembly impractical. This work presents our set of interconnectable modules (IMPROV), which programs and verifies the safety of assembled robots themselves. Experiments show that IMPROV robots retained the same control performance as nonmodular robots, despite their reconfigurability. With respect to human-robot coexistence, our user study shows a reduction of robot idle time by 36% without compromising on safety using our self-verification concept compared with current safety standards. We believe that by using self-programming and self-verification, modular robots can transform current automation practices.

Abstract-Mobile robots operating in a shared environment with pedestrians are required to move provably safe to avoid harming pedestrians. Current approaches like safety fields use conservative obstacle models for guaranteeing safety, which leads to degraded performance in populated environments. In this paper, we introduce an online verification approach that uses information about the current pedestrian velocities to compute possible occupancies based on a kinematic model of pedestrian motion. We demonstrate that our method reduces the need for stopping while retaining safety guarantees, and thus goals are reached between 1.4 and 3.5 times faster than the standard ROS navigation stack in the tested scenarios.

Model-based design of robotic systems has many advantages, among them faster development cycles and reduced costs due to early detections of design flaws. Approximate models are sufficient for many classical robotic applications; however, they no longer suffice for safety-critical applications. For instance, a dangerous situation which has not been detected by model-based testing might occur in a human-robot coexistence scenario since models do not exactly replicate behaviors of real systems-this problem arises no matter how accurate a model is, since even disturbances and sensor noise can cause a mismatch. We address this issue by adding nondeterminism to robotic models and by computing the whole set of possible behaviors using reachability analysis. By using reachset conformance, we automatically adjust the required non-determinism so that all recorded behaviors are captured. For the first time this approach is demonstrated for a real robot.

Flexible manufacturing and automation require robots that can adapt to changing tasks. We propose to use modular robots that are customized from given modules for a specific task. This work presents an algorithm for proposing a module composition that is optimal with respect to performance metrics such as cycle time and energy efficiency, while considering kinematic, dynamic, and obstacle constraints. Tasks are defined as trajectories in Cartesian space, as a list of poses for the robot to reach as fast as possible, or as dexterity in a desired workspace. In a simulative comparison with commercially available industrial robots, we demonstrate the superiority of our approach in randomly generated tasks with respect to the chosen performance metrics. We use our modular robot proModular.1 for the comparison.