We provide a Dual-GA technique for solving the Multiple Travelling Salesman Problem (mTSP) while constraining distance between robots. Other techniques primarily solve for full network connectivity, with energy efficiency as a secondary objective. Our technique makes no assumptions about the desired balance between connectivity and energy efficiency. Instead, it produces a range of solutions for the decision-maker to select from. It uses NSGA-II for the primary GA, with a secondary GA periodically adding waypoints for greater connectivity. We introduce the Dual-GA and analyse its performance compared to other algorithms.

Renewable forms of energy are becoming increasingly important to consider, as the global energy demand continues to grow. Wave energy is one of these widely available forms, but it is largely unexploited. A common design for a wave energy converter is called a point absorber or buoy. The buoy typically floats on the surface or just below the surface of the water, and captures energy from the movement of the waves. It can use the motion of the waves to drive a pump to generate electricity and to create potable water. Since a single buoy can only capture a limited amount of energy, large-scale wave energy production necessitates the deployment of buoys in large numbers called arrays. However, the efficiency of arrays of buoys is affected by highly complex intra-buoy interactions. The contributions of this article are two-fold. First, we present an approximation of the buoy interactions model that results in a 350-fold computational speed-up to enable the use inside of iterative optimisation algorithms, Second, we study arrays of fully submerged three-tether buoys, with and without shared mooring points.

Biological visual systems provide excellent examples of robust target detection and tracking mechanisms capable of performing in a wide range of environments. Consequently, they have been sources of inspiration for many artificial vision algorithms. However, testing the robustness of target detection and tracking algorithms is a challenging task due to the diversity of environments for applications of these algorithms. Correlation between image quality metrics and model performance is one way to deal with this problem. Previously we developed a target detection model inspired by physiology of insects and implemented it in a closed loop target tracking algorithm. In the current paper we vary the kinetics of a salience-enhancing element of our algorithm and test its effect on the robustness of our model against different natural images to find the relationship between model performance and background clutter.

We developed a biologically inspired model for detection and pursuit of small targets against complex backgrounds and tested it in a closed-loop flight arena. A winner-takes-all network of local feature detectors based on insect Small Target Motion Detector (STMD) neurons was used to direct the gaze of a camera mimicking the viewpoint of the pursuer in a series of small steps (saccades) whilst fixating the background. The output of a direction-selective network of 2ndorder local motion correlators was then used to enhance the relative salience of features in the direction of travel of the winning feature. The combination of saccadic fixation and robust target-ground discrimination provided by the STMD front-end, with an attention-like 2ndorder salience enhancement provided very reliable capture of tiny targets even in visually challenging scenarios.

There is an increasing trend to use Stewart platforms to implement ultra-high precision tasks under large external loads (e.g. biomechanical testing) mainly due to their high stiffness, and high load carrying capacity. However, the backlash or joint clearance in the system can significantly degrade the accuracy. This work studied the application of actuation redundancy in a general Stewart platform to regulate the preloads on its active joints for the purpose of backlash prevention. A novel active preload control method was proposed to achieve a real-time approach that is robust to large six degree of freedom external loads. The proposed preload method applies an inverse-dynamics based online optimization algorithm to calculate the desired force trajectory of the redundant actuator, and uses a force control scheme to achieve the required force. Simulation results demonstrate that this method is able to eliminate backlash inaccuracies during application of large external loads and therefore ensure the precision of the system.

Piezoelectric actuators are the most common among a variety of smart actuators due to their high resolution, low power consumption and wide operating frequency but they suffer hysteresis which affects linearity. In this paper a novel digital charge amplifier is presented which reduces hysteresis and linearizes the piezoelectric actuator. A frequency-weighted data fusion algorithm uses a non-linear ARX model to remove drift and increase the bandwidth of digital charge amplifier. Experimental results are presented.