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.

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.

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.

Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for applications of unknown complexity and nonstationary solutions. A conceptual analogy is developed and extended to theoretical conditions for modeling synaptic plasticity as network construction. Generalizing past constructive algorithms, we propose a framework for the design of novel constructive SNNs and demonstrate its application in the development of simulations for the validation of developed theory. Potential directions of future research and applications of STDC for biological modeling and machine learning are also discussed.

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.