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-A failsafe control strategy is presented for online safety certification of robot movements in a collaborative workspace with humans. This approach plans, predicts and uses formal guarantees on reachable sets of a robot arm and a human obstacle to verify the safety and feasibility of a trajectory in real time. The robots considered are serial link robots under Computed Torque schemes of control. We drastically reduce the computation time of our novel verification procedure through precomputation of non-linear terms and use of interval arithmetic, as well as representation of reachable sets by zonotopes, which scale easily to high dimensions and are easy to convert between joint space and Cartesian space. The approach is implemented in a simulation, to show that real time is computationally within reach.
Abstract-We investigate the effect of dominant and submissive movement strategies and a movement cue in a human-robot cooperation scenario on perceived predictability and trust. Four different movement strategies in proximal cooperation between a robot manipulator and a human participant were tested in an experiment in which participants had to arrange small objects in a shared workspace working on the same product as the robot. The features of the robot motion were characterized by dominance or a movement cue. The robot modifies its motion in two ways resulting in four different movement strategies: either it stops when the human is in danger of collision (submissive) or not (dominant), and either it performs a backing-off movement cue or not. The participants evaluated the movement strategies in terms of trust and predictability in a questionnaire. We found that the submissive backing-off movement strategy significantly enhanced the users' trust compared to the dominant movement strategy without movement cue. Other strategies showed no significant differences in trust or predictability.
Abstract-Predicting the occupancy of a human in real time is of great interest in human-robot coexistence for obtaining regions that a robot should avoid in safe motion planning. The human body is composed of joints and links, suiting approximation by a kinematic chain, but the control strategy of the human is completely unknown, meaning the potential occupancy grows very fast and it is difficult to compute tightly in real time. As such, most previous work considers only specific, known, or probable movements, and usually does not account for a range of human dimensions. Focusing on the human arm, we analyze archetypal movements performed by test subjects to create a dynamic model. Motion-capture data of subjects are fitted, for modeling purposes, to two abstractions: a 4-degree of freedom (DOF) model and a 3-DOF model, to obtain dynamic parameters. We validate our approach on movements from a publicly available database. The prediction is shown to be computationally fast, and reachable sets of the abstraction are shown to enclose all possible future occupancies of the arm for different subjects, tightly but overapproximatively. The 3-DOF model has advantages over the 4-DOF in terms of speed, though the 4-DOF model is tighter at smaller time horizons. Such an overapproximative representation is intended for certifiable safety-guaranteed collision avoidance algorithms for robots.Note to Practitioners-Motivated by the need to keep humans safe when working alongside robots, our earlier work proposes a method of trajectory planning where the robot certifies each movement as safe before it performs it. For this to prove that unsafe collisions cannot occur, an overapproximative prediction of the human is needed, meaning that no possible future position of the human is outside the predicted region, or reachable occupancy. However, making this prediction both small enough (so that it does not include unreachable regions) and fast enough for real-time use is not straightforward. We find the limits of human motion by asking a range of test subjects to perform movements as fast as possible. We calculate the reachable occupancies based on these limits and show that our predictions are indeed overapproximative, fast, and not wasteful of volume. One can then use the aforementioned approach to guarantee safety; future challenges are reliably sensing the human's pose and implementing our approach on an industrial robot.
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