Aaron Pereira

Safety control of robots under Computed Torque control using reachable sets

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.

Online motion synthesis with minimal intervention control and formal safety guarantees

We present a framework for online coordinated obstacle avoidance with formal safety guarantees. Such a formally verified trajectory planner can be used in shared human-robot workspaces to guarantee safety. The obstacle avoidance is based on estimation of the human occupancy on two different time scales. A long-term plan is created based on a probabilistic task representation, learned by demonstration, and an estimate of the human occupancy to be avoided. Using an additional overapproximative, short-term prediction of human motion we guarantee that the robot can always account for sudden or reflex movements. We demonstrate our two-level obstacle avoidance in simulation. The results show that our method reduces the number of safety stops one would encounter when using only the formal safety verification, and synthesizes alternative movement plans that preserves the coordination observed in the original demonstrations.

Overapproximative arm occupancy prediction for human-robot co-existence built from archetypal movements

Human motion is fast and hard to predict. To implement a provably safe collision-avoidance strategy for robots in collaborative spaces with humans, an overapproximative prediction of the occupancy of the human is required, which needs to be calculated faster than real time. We present a method for computing volumes containing the entire possible future occupancy of the human, given its state, faster than real time. The dynamic model of the human is built from analysing a set of archetypal movements performed by test subjects. The occupancy prediction is tested on a publicly available database of motion capture data, and shown to be overapproximative for all movements relating to everyday activities, sport and dance. Our novel algorithm is useful to guarantee safety in human-robot collaboration scenarios.

Overapproximative Human Arm Occupancy Prediction for Collision Avoidance

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.