Wool harvesting remains an important industry in Australia, but its workers suffer from extreme rates of injury, in particular, the lower back injuries. Reducing injuries in sheep shearing could be as simple as extending shearer rest periods between sheep, but the effect of this has not previously been studied. The lumbar flexion-relaxation phenomenon is present in sheep shearing and the onset angle of this phenomenon can provide insight into lower back injury risk. The increase in the onset angle of lumbar flexion-relaxation over several work-rest periods for a simulated sheep shearing task is studied. The rate of increase in the onset angle of lumbar flexion-relaxation was higher when shorter breaks were taken for all participants at least unilaterally, indicating that longer rest breaks could reduce back injury risk. Due to the constraints of the sheep shearing occupation, this type of intervention is better suited to learner and novice shearers. Assistive robotic devices would be more suited to reduce injuries in expert shearers, and some insight is provided for the application of these within sheep shearing. Further study of this phenomenon in sheep shearing could provide additional insight to developing an assistive device that could reduce injury.
In this paper we consider the problem of constraint handling for an airbreathing hypersonic vehicle (HSV) through a hierarchical control architecture. A reference manager is incorporated as an intermediate control loop whose role is to modify an offline generated reference trajectory, without knowledge of disturbances, to enforce state and input constraints. Compared with traditional constraint handling approaches in HSV literature, this proposed approach allows for the deployment of controllers that are not typically formulated to handle constraints. We provide a computation time and constraint management comparison between a scheme that directly utilizes the nonlinear vehicle model and one that performs online linearization of the model.
This paper proposes a radial basis function (RBF) based approach for the fuel injection control problem. In the past, neural controllers for this problem have centred on using a cerebellar model articulation controller (CMAC) type network with some success. The current production engine control units also use look-up tables in their fuel injection controllers, and if adaptation is permitted to these look-up tables the overall effect closely mimics the CMAC network. Here it is shown that an RBF network with significantly fewer nodes than a CMAC network is capable of delivering superior control performance on a mean value engine model simulation. The proposed approach requires no a priori knowledge of the engine systems, and on-line learning is achieved using gradient descent updates. The RBF network is then implemented on a four-cylinder engine and, after a minor modification, outperforms a production engine control unit.
To date, there is limited rigorous analysis of object manipulation using robotic hands, where more focus has been placed on heuristic and experiment-based approaches. In this paper, we analyze the effects of grasp uncertainties based on realistic assumptions and propose a robust control framework for object manipulation. The framework considers a hand-object system subject to disturbances resulting from uncertainties in the object center of mass/inertia, hand kinematics, external wrenches, and contact locations. The proposed framework is then applied to practical object manipulation scenarios with different levels of uncertainty related to the sensors available to the robotic hand. These scenarios include when the hand-object system is known perfectly; when vision sensors are available; when tactile sensors are available; and when no vision/tactile sensors are available to the robotic hand (i.e. blind grasping). The analysis also addresses the internal force control in relation to the various practical cases. Simulation and experimental results validate the effectiveness of the proposed approach.
With advances in image processing and machine learning, it is now feasible to incorporate semantic information into the problem of simultaneous localisation and mapping (SLAM). Previously, SLAM was carried out using lower level geometric features (points, lines, and planes) which are often view-point dependent and error prone in visually repetitive environments. Semantic information can improve the ability to recognise previously visited locations, as well as maintain sparser maps for long term SLAM applications. However, SLAM in repetitive environments has the critical problem of assigning measurements to the landmarks which generated them. In this paper, we use k-best assignment enumeration to compute marginal assignment probabilities for each measurement landmark pair, in real time. We present numerical studies on the KITTI dataset to demonstrate the effectiveness and speed of the proposed framework.
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