With the introduction of Industry 4.0, occupational health and safety finds itself confronted with new types of hazards. Many Industry 4.0 innovations involve increased machine intelligence. These properties make socio-technical work in Industry 4.0 applications inherently more complex. At the same time, system failure can become more opaque to its users. This paper reviews and assesses safety analysis methods as the breakdown of interaction coupling in socio-technical systems on the one hand, and the degree of failure tractability on the other hand; the latter being used as a proxy for complexity. Previous literature confirms that traditional health and safety risk assessment methods are unable or are ‘ill-equipped’ to deal with these system properties. This paper studies the need to introduce new paradigms and safety methods related to complexity thinking with theories borrowed from the study of complex adaptive systems, all to assess the arena of abruptly changing hazards introduced by Industry 4.0. At the same time, this review makes clear that there is no one-solution-fits-all method. Occupational health and safety (OHS) covers many different hazard types and will need a combination of old, new and yet-to-be-developed safety assessment methods.

The tunnel-following nonlinear model predictive control (NMPC) scheme allows to exploit acceptable deviations around a path reference. This is done by using convex-over-nonlinear functions as objective and constraints in the underlying optimal control problem (OCP). The convex-over-nonlinear structure is exploited by algorithms such as the generalized Gauss-Newton (GGN) method or the sequential convex quadratic programming (SCQP) method to reduce the computational complexity of the OCP solution. However, the modeling effort and engineering time required to implement these methods is high. We address the problem of reducing the modeling effort in the implementation of SCQP, focusing on a standard sequential quadratic programming (SQP) implementation where symbolic linearization is applied to the nonlinear part of the convex-over-nonlinear functions in the objective and constraints. The novelty of this letter is twofold. It introduces a novel operator that applies symbolic linearization in a transparent and easy way to solve nonconvex OCPs with the SCQP method, and introduces a meaningful representation of an orientation-tunnel for robotic applications by means of a convex-over-nonlinear constraint, which preserves the convexity exploitation by the SCQP method. The proposed technique is demonstrated in a tunnel-following task for a 7-degrees-of-freedom manipulator.

Despite their undisputed potential, the uptake of collaborative robots remains below expectations. Collaborative robots (cobots) are used differently from conventional industrial robots. The current safety focus of collaborative workspaces is predominantly on the technological design; additional factors also need to be considered to cope with the emerging risks associated with complex systems. Cobot technologies are characterized by an inherent tradeoff between safety and efficiency. They introduce new, emergent risks to organizations and can create psychosocial impacts on workers. This leads to a confusing body of information and an apparent contradiction about cobot safety. Combined with a lack of safety knowledge, this impedes the introduction of cobots. A multi-step methodology was used, including a literature review and conceptual modeling. This article argues for the need for a system-wide safety awareness readiness assessment in the consideration phase of cobot implementation to alleviate the knowledge deficit and confusion. This work will benefit both researchers and practitioners. In addition, it defends the appropriateness of a maturity grid model for a readiness assessment tool. The building blocks for an easy-to-use and practically applicable tool are proposed, as well as an agenda for the next steps.

This paper introduces a generalization of the well‐known Riccati recursion for solving the discrete‐time equality‐constrained linear quadratic optimal control problem. The recursion can be used to compute problem solutions as well as optimal feedback control policies. Unlike other tailored approaches for this problem class, the proposed method does not require restrictive regularity conditions on the problem. This allows its use in nonlinear optimal control problem solvers that use exact Lagrangian Hessian information. We demonstrate that our approach can be implemented in a highly efficient algorithm that scales linearly with the horizon length. Numerical tests show a significant speed‐up of about one order of magnitude with respect to state‐of‐the‐art general‐purpose sparse linear solvers. Based on the proposed approach, faster nonlinear optimal control problem solvers can be developed that are suitable for more complex applications or for implementations on low‐cost or low‐power computational platforms. The implementation of the proposed algorithm is made available as open‐source software.

We derive a family of efficient constrained dynamics algorithms by formulating an equivalent linear quadratic regulator (LQR) problem using Gauss' principle of least constraint and solving it using dynamic programming. Our approach builds upon the pioneering (but largely unknown) $O(n + m^{2}\;d + m^{3})$ solver by Popov and Vereshchagin (PV), where $n$ , $m$ , and $d$ are the number of joints, number of constraints, and the kinematic tree depth, respectively. We provide an expository derivation for the original PV solver and extend it to floating-base kinematic trees with constraints allowed on any link. We make new connections between the LQR's dual Hessian and the inverse operational space inertia matrix (OSIM), permitting efficient OSIM computation, which we further accelerate using matrix inversion lemma. By generalizing the elimination ordering and accounting for MuJoCo -type soft constraints, we derive two original $O(n + m)$ complexity solvers. Our numerical results indicate that significant simulation speed-up can be achieved for high dimensional robots like quadrupeds and humanoids using our algorithms as they scale better than the widely used $O(nd^{2} + m^{2}\;d + d^{2}\;m)$ LTL algorithm of Featherstone. The derivation through the LQR-constrained dynamics connection can make our algorithm accessible to a wider audience and enable cross fertilization of software and research results between the fields.