Dealing safely with nuclear waste is an imperative for the nuclear industry. Increasingly, robots are being developed to carry out complex tasks such as perceiving, grasping, cutting, and manipulating waste. Radioactive material can be sorted, and either stored safely or disposed of appropriately, entirely through the actions of remotely controlled robots. Radiological characterisation is also critical during the decommissioning of nuclear facilities. It involves the detection and labelling of radiation levels, waste materials, and contaminants, as well as determining other related parameters (e.g., thermal and chemical), with the data visualised as 3D scene models. This paper overviews work by researchers at the QMUL Centre for Advanced Robotics (ARQ), a partner in the UK EPSRC National Centre for Nuclear Robotics (NCNR), a consortium working on the development of radiation-hardened robots fit to handle nuclear waste. Three areas of nuclear-related research are covered here: human–robot interfaces for remote operations, sensor delivery, and intelligent robotic manipulation.

Vection refers to the illusion of self-motion when a significant portion of the visual field is stimulated by visual flow, while body is still. Vection is known to be strong for peripheral vision stimulation and relatively weak for central vision. In this paper, the results of an experimental study of central linear vection with and without vibrotactile feet stimulation are presented. Three types of vibratory stimuli were used: a sinusoidal signal, pink noise, and a chirp signal. Six subjects faced a screen looking at a looming visual flow that suggested virtual forward motion. The results showed that the sensation of self-motion happened faster and its intensity was the strongest for sinusoidal vibrations at constant frequency. For some subjects, a vibrotactile stimulus with an increasing frequency (a chirp) elicited as well a stronger vection. The strength of sensation of self-motion was the lowest in the cases when pink noise vibrations and no vibrotactile stimulation accompanied the visual flow stimulation. Possible application areas are mentioned.

In this paper, new force feedback rendering scheme for mobile robot teleoperation is presented. Previous research indicated the problem of the low quality of mobile robot's motion control during the teleoperation with feedback force based on obstacle range information. Human-operator's commands were distorted by the feedback force, as a result, mobile robot could not follow human-operator's intention. To solve this problem, a new force rendering approach with variable feedback gain is proposed. Force feedback gain is variable based on measured distances to the obstacle and derivatives of the distances. Simulation and experimental study showed that the variable haptic feedback improves the quality of mobile robot teleoperation. variable feedback force improved the quality of mobile robot teleoperation by making robot's trajectory smooth and accurate.

With the fast development of wearable IoT devices, their applications are becoming more and more pervasive, ranging from social networking, payment, and navigation to health and activity monitoring. The security of the communication between these devices is essential to protect the transmitted sensitive information from tampering and eavesdropping. With the integration of accelerometers into wearable IoT devices, the gait-based biometric cryptography technology has emerged as a data securing tool for wearables. This paper proposes a light-weight noise-based group key generation method, which utilizes the noise signals imposed on the raw acceleration signals to generate an M-bit key with high randomness and bit generation rate. Moreover, a signed sliding window coding (SSWC)-based common feature extraction method was designed to extract the common feature for sharing the generated M-bit key among devices worn on different body parts. Finally, a fuzzy vault-based group key distribution system was implemented and evaluated using a public dataset. The performed comprehensive analysis of the proposed key generation and distribution method proved that the binary keys generated via the introduced noise-based procedure have high entropy and can pass both the NIST and Dieharder statistical tests with high efficiency. The experimental results obtained prove the robustness of the proposed SSWC-based common feature extraction method in terms of the similarity and discriminability of intra-and inter-class features, respectively.