Humans must remain unharmed during their interaction with robots. We present a new method guaranteeing impact force limits when humans and robots share a workspace. Formal guarantees are realized using an online verification method, which plans and verifies fail-safe maneuvers through predicting reachable impact forces by considering all future possible scenarios. We model collisions as a coupled human-robot dynamical system with uncertainties and identify reachsetconforming models based on real-world collision experiments. The effectiveness of our approach for human-robot co-existence is demonstrated for the human hand interacting with the end effector of a six-axis robot manipulator with force sensing. By integrating a human pose detection system, the efficiency of robot movements increases.

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Accurate velocity information is often essential to the control of robot manipulators, especially for precise tracking of fast trajectories. However, joint velocities are rarely directly measured and instead estimated to save costs. While many approaches have been proposed for the velocity estimation of robot joints, no comprehensive experimental evaluation exists, making it difficult to choose the appropriate method. This paper compares multiple estimation methods running on a six degrees-of-freedom manipulator. We evaluate: 1) the estimation error using a ground-truth signal, 2) the closed-loop tracking error, 3) convergence behavior, 4) sensor fault tolerance, 5) implementation and tuning effort. To ensure a fair comparison, we optimally tune the estimators using a genetic algorithm. All estimation methods have a similar estimation error and similar closed-loop tracking performance, except for the nonlinear high-gain observer, which is not accurate enough. Sliding-mode observers can provide a precise velocity estimation despite sensor faults.