Optimisation of the geometry of axisymmetric point-absorber wave energy converters

We propose a scientifically rigorous framework to find realistic optimal geometries of wave energy converters (WECs). For specificity, we assume WECs to be axisymmetric point absorbers in a monochromatic unidirectional incident wave, all within the context of linearised potential theory. We consider separately the problem of a WEC moving and extracting wave energy in heave only and then the more general case of motion and extraction in combined heave, surge and pitch. We describe the axisymmetric geometries using polynomial basis functions, allowing for discontinuities in slope. Our framework involves ensuring maximum power, specifying practical motion constraints and then minimising surface area (as a proxy for cost). The framework is robust and well-posed, and the optimisation produces feasible WEC geometries. Using the proposed framework, we develop a systematic computational and theoretical approach, and we obtain results and insights for the optimal WEC geometries. The optimisation process is sped up significantly by a new theoretical result to obtain roots of the heave resonance equation. For both the heave-only, and the heave-surge-pitch combined problems, we find that geometries which protrude outward below the waterline are generally optimal. These optimal geometries have up to 73 % less surface area and 90 % less volume than the optimal cylinders which extract the same power.

A geometrical formulation for the workspace of parallel manipulators

In study this paper, a geometric formulation is proposed to describe the workspace of parallel manipulators by using a recursive approach as an extension of volume generation for solids of revolution. In this approach, the workspace volume and boundary for each limb of the parallel manipulator is obtained with an algebraic formulation derived from the kinematic chain of the limb and the motion constraints on its joints. Then, the overall workspace of the mechanism can be determined as the intersection of the limb workspaces. The workspace of different kinematic chains is discussed and classified according to its external shape. An algebraic formulation for the inclusion of obstacles in the computation is also proposed. Both analytical models and numerical simulations are reported with their advantages and limitations. An example on a 3-SPR parallel mechanism illustrates the feasibility of the formulation and its efficiency.

Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network

Redundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator.

Motion Boundaries Formation For Autonomous Vehicles

<div>In this paper, the application of Model Predictive Control to perform curvilinear motion planning is explored. More specifically, nonlinear MPC will be focused on because of its proven efficiency in the modeling of uncertainties as well as in nonlinear model dynamics. The main objective of this report is to show that with proper modeling and formulation of motion constraints, curvilinear motion planning can be achieved with nonlinear MPC. The trajectory of the vehicle will be tracked with the least error while satisfying constraints such as speed and steering angles. Simulations are presented which demonstrate the ability of the suggested models to successfully perform curvilinear motion staying safely within the bounds, while simulations of several models validate its performance. A deterministic sensitivity analysis was conducted in order to determine the impact</div><div>of the prediction horizon time. Experimental results show that a critical prediction horizon time approximately 10 to 13 seconds was identified as the ideal range for optimal results of the model.</div>

UAV Path Planning Based on Improved A ∗ and DWA Algorithms

This work proposes a path planning algorithm based on A ∗ and DWA to achieve global path optimization while satisfying security and speed requirements for unmanned aerial vehicles (UAV). The algorithm first preprocesses the map for irregular obstacles encountered by a UAV in flight, including grid preprocessing for arc-shaped obstacles and convex preprocessing for concave obstacles. Further, the standard A ∗ algorithm is improved based on UAV’s flight environment information and motion constraints. Further, the DWA algorithm’s limitations regarding local optimization and long planning time are mitigated by adaptively adjusting the evaluation function according to the UAV’s safety threshold, obstacles, and environment information. As a result, the global optimal path evaluation subfunction is constructed. Finally, the key points of the global path are selected as the subtarget points of the local path planning. Under the premise of the optimal path, the UAV real-time path’s efficiency and safety are effectively improved. The experimental results demonstrate that the path planning based on improved A ∗ and DWA algorithms shortens the path length, reduces the planning time, improves the UAV path smoothness, and enhances the safety of UAV path obstacle avoidance.

Survey of Fragile Geologic Features and Their Quasi-Static Earthquake Ground-Motion Constraints, Southern Oregon

ABSTRACT Fragile geologic features (FGFs), which are extant on the landscape but vulnerable to earthquake ground shaking, may provide geological constraints on the intensity of prior shaking. These empirical constraints are particularly important in regions such as the Pacific Northwest that have not experienced a megathrust earthquake in written history. Here, we describe our field survey of FGFs in southern Oregon. We documented 58 features with fragile geometric characteristics, as determined from field measurements of size and strength, historical photographs, and light detection and ranging point clouds. Among the surveyed FGFs, sea stacks have particular advantages for use as ground-motion constraints: (1) they are frequently tall and thin; (2) they are widely distributed parallel to the coast, proximal to the trench and the likely megathrust rupture surface; and (3) they are formed by sea cliff retreat, meaning that their ages may be coarsely estimated as a function of distance from the coast. About 40% of the surveyed sea stacks appear to have survived multiple Cascadia megathrust earthquakes. Using a quasi-static analysis, we estimate the minimum horizontal ground accelerations that could fracture the rock pillars. We provide context for the quasi-static results by comparing them with predictions from kinematic simulations and ground-motion prediction equations. Among the sea stacks old enough to have survived multiple megathrust earthquakes (n = 16), eight yield breaking accelerations lower than the predictions, although they generally overlap within uncertainty. FGFs with the lowest breaking accelerations are distributed uniformly over 130 km of coastline. Results for inland features, such as speleothems, are in close agreement with the predictions. We conclude that FGFs show promise for investigating both past earthquake shaking and its spatial variability along the coasts of Oregon and Washington, where sea stacks are often prevalent. Future work can refine our understanding of FGF age and evolution.

Spatial Mechanism-Environment Contact Geometric Models

This work proposes a theoretical foundation for a general spatial geometric mechanism-environment contact model. In the proposed model the curvature of the environment in the vicinity of the contact is approximated by a number of spherical surfaces with known radii of curvature that constrain/define the movement of the body. We show how the modeled body-environment contact and curvature constraints can be transformed into conditions on spatial velocity and acceleration (i.e. first and second order effects) of certain points of the moving body that can be incorporated in the kinematic task for designing spatial mechanisms. Further, we explore the exact synthesis of a spatial six degrees-of-freedom TPS kinematic chain which end-effector maintains contact with objects in the environment and varies orientation in the vicinity of a contact location. It is discussed how the higher order motion constraints allow for the introduction of kinematic task variations in the vicinity of a contact, resulting in different behaviors of the designed spatial mechanism. The theoretical foundation presented in this paper is crucial in gaining understanding of the constraints in describing mechanism-environment interactions in the vicinity of a contact and is a new contribution.

Robust trajectory tracking control for unmanned surface vessels under motion constraints and environmental disturbances

To achieve a fully autonomous navigation for unmanned surface vessels (USVs), a robust control capability is essential. The control of USVs in complex maritime environments is rather challenging as numerous system uncertainties and environmental influences affect the control performance. This paper therefore investigates the trajectory tracking control problem for USVs with motion constraints and environmental disturbances. Two different controllers are proposed to achieve the task. The first approach is mainly based on the backstepping technique augmented by a virtual system to compensate for the disturbance and an auxiliary system to bound the input in the saturation limit. The second control scheme is mainly based on the normalisation technique, with which the bound of the input can be limited in the constraints by tuning the control parameters. The stability of the two control schemes is demonstrated by the Lyapunov theory. Finally, simulations are conducted to verify the effectiveness of the proposed controllers. The introduced solutions enable USVs to follow complex trajectories in an adverse environment with varying ocean currents.