This paper provides an overview of collaborative robotics towards manufacturing applications. Over the last decade, the market has seen the introduction of a new category of robots—collaborative robots (or “cobots”)—designed to physically interact with humans in a shared environment, without the typical barriers or protective cages used in traditional robotics systems. Their potential is undisputed, especially regarding their flexible ability to make simple, quick, and cheap layout changes; however, it is necessary to have adequate knowledge of their correct uses and characteristics to obtain the advantages of this form of robotics, which can be a barrier for industry uptake. The paper starts with an introduction of human–robot collaboration, presenting the related standards and modes of operation. An extensive literature review of works published in this area is undertaken, with particular attention to the main industrial cases of application. The paper concludes with an analysis of the future trends in human–robot collaboration as determined by the authors.

Robotic interventions with redundant mobile manipulators pose a challenge for telerobotics in hazardous environments, such as underwater, underground, nuclear facilities, particle accelerators, aerial or space. Communication issues can lead to critical consequences, such as imprecise manipulation resulting in collisions, breakdowns and mission failures. The research presented in this paper was driven by the needs of a real robotic intervention scenario in the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN). The goal of the work was to develop a framework for network optimisation in order to help facilitate Mixed Reality techniques such as 3D collision detection and avoidance, trajectories planning, real-time control, and automatized target approach. The teleoperator was provided with immersive interactions while preserving precise positioning of the robot. These techniques had to be adapted to delays, bandwidth limitation and their volatility in the 4G shared network of the real underground particle accelerator environment. The novel application-layer congestion control with automatic settings was applied for video and point cloud feedback. Twelve automatic setting modes were proposed with algorithms based on the camera frame rate, resolution, point cloud subsampling, network round-trip time and throughput to bandwidth ratio. Each mode was thoroughly characterized to present its specific use-case scenarios and the improvements it brings to the adaptive camera feedback control in teleoperation. Finally, the framework was presented according to which designers can optimize their Human-Robot Interfaces and sensor feedback depending on the network characteristics and task.

Two 6 t beam dumps, made of a graphite core encapsulated in a stainless steel vessel, are used to absorb the energy of the two Large Hadron Collider (LHC) intense proton beams during operation of the accelerator. Operational issues started to appear in 2015 during LHC Run 2 (2014–2018) as a consequence of the progressive increase of the LHC beam kinetic energy, necessitating technical interventions in the highly radioactive areas around the dumps. Nitrogen gas leaks appeared after highly energetic beam impacts and instrumentation measurements indicated an initially unforeseen movement of the dumps. A computer modelling analysis campaign was launched to understand the origin of these issues, including both Monte Carlo simulations to model the proton beam interaction as well as advanced thermo-mechanical analyses. The main findings were that the amount of instantaneous energy deposited in the dump vessel leads to a strong dynamic response of the whole dump and high accelerations (above 200 g). Based on these findings, an upgraded design, including a new support system and beam windows, was implemented to ensure the dumps' compatibility with the more intense beams foreseen during LHC Run 3 (2022–2025) of 539 MJ per beam. In this paper an integral overview of the operational behaviour of the dumps and the upgraded configurations are discussed.

Flexible, steerable, soft needles are desirable in Minimally Invasive Surgery to achieve complex trajectories while maintaining the benefits of percutaneous intervention compared to open surgery. One such needle is the multi-segment Programmable Bevel-tip Needle (PBN), which is inspired by the mechanical design of the ovipositor of certain wasps. PBNs can steer in 3D whilst minimizing the force applied to the surrounding substrate, due to the cyclic motion of the segments. Taking inspiration also from the control strategy of the wasp to perform insertions and lay their eggs, this paper presents the design of a cyclic controller that can steer a PBN to produce a desired trajectory in 3D. The performance of the controller is demonstrated in simulation in comparison to that of a direct controller without cyclic motion. It is shown that, while the same steering curvatures can be attained by both controllers, the time taken to achieve the configuration is longer for the cyclic controller, leading to issues of potential under-steering and longer insertion times.

Robotic-assisted steered needles aim to accurately control the deflection of the flexible needle’s tip to achieve accurate path following. In doing so, they can decrease trauma to the patient, by avoiding sensitive regions while increasing placement accuracy. This class of needle presents more complicated kinematics compared to straight needles, which can be exploited to produce specific motion profiles via careful controller design and tuning. Motion profiles can be optimized to minimize certain conditions such as maximum tissue deformation and target migration, which was the goal of the formalized cyclic, low-level controller for a Programmable Bevel-tip Needle (PBN) presented in this work. PBNs are composed of a number of interlocked segments that are able to slide with respect to one another. Producing a controlled, desired offset of the tip geometry leads to the corresponding desired curvature of the PBN, and hence desired path trajectory of the system. Here, we propose a cyclical actuation strategy, where the tip configuration is achieved over a number of reciprocal motion cycles, which we hypothesize will reduce tissue deformation during the insertion process. A series of in vitro, planar needle insertion experiments are performed in order to compare the cyclic controller performance with the previously used direct push controller, in terms of targeting accuracy and tissue deformation. It is found that there is no significant difference between the target tracking performance of the controllers, but a significant decrease in axial tissue deformation when using the cyclic controller.