Biomimetic vision-based collision avoidance system for MAVs.

This thesis proposes a secondary collision avoidance algorithm for micro aerial vehicles based on luminance-difference processing exhibited by the Lobula Giant Movement Detector (LGMD), a wide-field visual neuron located in the lobula layer of a locust’s nervous system. In particular, we address the design, modulation, hardware implementation, and testing of a computationally simple yet robust collision avoidance algorithm based on the novel concept of quadfurcated luminance-difference processing (QLDP). Micro and Nano class of unmanned robots are the primary target applications of this algorithm, however, it could also be implemented on advanced robots as a fail-safe redundant system. The algorithm proposed in this thesis addresses some of the major detection challenges such as, obstacle proximity, collision threat potentiality, and contrast correction within the robot’s field of view, to establish and generate a precise yet simple collision-free motor control command in real-time. Additionally, it has proven effective in detecting edges independent of background or obstacle colour, size, and contour. To achieve this, the proposed QLDP essentially executes a series of image enhancement and edge detection algorithms to estimate collision threat-level (spike) which further determines if the robot’s field of view must be dissected into four quarters where each quadrant’s response is analysed and interpreted against the others to determine the most secure path. Ultimately, the computation load and the performance of the model is assessed against an eclectic set of off-line as well as real-time real-world collision scenarios in order to validate the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision (real-world), moving at 1.2 msˉ¹ with a successful avoidance rate of 90% processing at an extreme frequency of 120 Hz, that is much superior compared to the results reported in the contemporary related literature to the best of our knowledge.MSc by Researc

Design and Development of a Bioinspired Gliding-optimised Tandem Wings for Micro Aerial Robots

Research on aerial robotics particularly the articulated/flapping wing robots have gained a remarkable attention during the recent years due to their agility and stealthiness in surveillance and reconnaissance missions. However, wing flapping is highly energy-intensive that can be complemented by gliding to conserve energy for long-range flights as demonstrated by desert locusts (Schistocerca gregaria). Therefore, inspired by this insect we explore novel solutions in this thesis to address some of the challenging problems of aeronautics concerning development, fabrication, and aerodynamic optimisation of a bio-inspired glid�ing wing for micro aerial vehicle (MAV) applications. Initially, we investigate the aerofoil geometries (2D wings) of a locust by performing a pseudo-microscopic scanning of its wings in gliding posture. Using numerical analysis and a novel optimisation methodology based on Nash-Genetic Algorithms, we study and enhance the aerodynamic performance of the digitally reconstructed aerofoils. The optimised as well as the original aerofoils are integrated using Computer-Aided Design (CAD) to form 3D wings that are subjected to a Computational Fluid Dynamics (CFD) modelling, and a Finite Element Analysis (FEA) to validate their aerodynamic performance and manufacturing-worthiness, respectively. Having established the required performance criteria numerically, a novel combination of fabrication techniques involving 3D printing, vacuum thermoforming, and laser trimming is proposed to realise the digital wings as artificial wing prototypes. Furthermore, we explore the novel Computer-Associated Design (CAsD) based on machine learning technology to obtain computer-generated wings for a detailed comparative study of the mechanical properties associated with each wing prototype designed by an engineer, a computer, and the nature

Generative design and fabrication of a locust-inspired gliding wing prototype for micro aerial robots

Gliding is generally one of the most efficient modes of flight in natural fliers that can be further emphasised in the aircraft industry to reduce emissions and facilitate endured flights. Natural wings being fundamentally responsible for this phenomenon are developed over millions of years of evolution. Artificial wings on the other hand, are limited to the human-proposed conceptual design phase often leading to sub-optimal results. However, the novel Generative Design (GD) method claims to produce mechanically improved solutions based on robust and rigorous models of design conditions and performance criteria. This study investigates the potential applications of this Computer-Associated Design (CAsD) technology to generate novel micro aerial vehicle wing concepts that are structurally more stable and efficient. Multiple performance-driven solutions (wings) with high-level goals are generated by an infinite scale cloud computing solution executing a machine learning based GD algorithm. Ultimately, the highest performing CAsD concepts are numerically analysed, fabricated, and mechanically tested according to our previous study, and the results are compared to the literature for qualitative as well as quantitative analysis and validations. It was concluded that the GD-based tandem wings' (fore-& hindwing) ability to withstand fracture failure without compromising structural rigidity was optimised by 78% compared to its peer models. However, the weight was slightly increased by 11% with 14% drop in stiffness when compared to our models from previous study

Aerodynamic Analysis and Optimization of Gliding Locust Wing Using Nash Genetic Algorithm

Natural fliers glide and minimize wing articulation to conserve energy for endured and long range flights. Elucidating the underlying physiology of such capability could potentially address numerous challenging problems in flight engineering. This study investigates the aerodynamic characteristics of an insect species called desert locust (Schistocerca gregaria) with an extraordinary gliding skills at low Reynolds number. Here, locust tandem wings are subjected to a computational fluid dynamics (CFD) simulation using 2D and 3D Navier-Stokes equations revealing fore-hindwing interactions, and the influence of their corrugations on the aerodynamic performance. Furthermore, the obtained CFD results are mathematically parameterized using PARSEC method and optimized based on a novel fusion of Genetic Algorithms and Nash game theory to achieve Nash equilibrium being the optimized wings. It was concluded that the lift-drag (gliding) ratio of the optimized profiles were improved by at least 77% and 150% compared to the original wing and the published literature, respectively. Ultimately, the profiles are integrated and analyzed using 3D CFD simulations that demonstrated a 14% performance improvement validating the proposed wing models for further fabrication and rapid prototyping presented in the future study

A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm

Natural fliers glide and minimize wing articulation to conserve energy for endured and long range flights. Elucidating the underlying physiology of such capability could potentially address numerous challenging problems in flight engineering. However, primitive nature of the bioinspired research impedes such achievements, hence to bypass these limitations, this study introduces a bioinspired non-cooperative multiple objective optimization methodology based on a novel fusion of PARSEC, Nash strategy, and genetic algorithms to achieve insect-level aerodynamic efficiencies. The proposed technique is validated on a conventional airfoil as well as the wing crosssection of a desert locust (Schistocerca gregaria) at low Reynolds number, and we have recorded a 77% improvement in its gliding ratio

Towards locust-inspired gliding wing prototypes for micro aerial vehicle applications

In aviation, gliding is the most economical mode of flight explicitly appreciated by natural fliers. They achieve it by high-performance wing structures evolved over millions of years in nature. Among other prehistoric beings, locust (Schistocerca gregaria) is a perfect example of such natural glider capable of endured transatlantic flights that could inspire a practical solution to achieve similar capabilities on micro aerial vehicles. This study investigates the effects of haemolymph on the flexibility of several flying insect wings further showcasing the superior structural performance of locusts. However, biomimicry of such aerodynamic and structural properties is hindered by the limitations of modern as well as conventional fabrication technologies in terms of availability and precision, respectively. Therefore, here we adopt finite element analysis (FEA) to investigate the manufacturing-worthiness of a 3D digitally reconstructed locust tandem wing, and propose novel combinations of economical and readily-available manufacturing methods to develop the model into prototypes that are structurally similar to their counterparts in nature while maintaining the optimum gliding ratio previously obtained in the aerodynamic simulations. Latter is evaluated in the future study and the former is assessed here via an experimental analysis of the flexural stiffness and maximum deformation rate. Ultimately, a comparative study of the mechanical properties reveals the feasibility of each prototype for gliding micro aerial vehicle applications

Investigating Multiple Pheromones in Swarm Robots - A Case Study of Multi-Robot Deployment

Social insects are known as the experts in handling complex task in a collective smart way although their small brains contain only limited computation resources and sensory information. It is believed that pheromones play a vital role in shaping social insects' collective behaviours. One of the key points underlying the stigmergy is the combination of different pheromones in a specific task. In the swarm intelligence field, pheromone inspired studies usually focus one single pheromone at a time, so it is not clear how effectively multiple pheromones could be employed for a collective strategy in the real physical world. In this study, we investigate multiple pheromone-based deployment strategy for swarm robots inspired by social insects. The proposed deployment strategy uses two kinds of artificial pheromones; the attractive and the repellent pheromone that enables micro robots to be distributed in desired positions with high efficiency. The strategy is assessed systematically by both simulation and real robot experiments using a novel artificial pheromone platform ColCOSΦ. Results from the simulation and real robot experiments both demonstrate the effectiveness of the proposed strategy and reveal the role of multiple pheromones. The feasibility of the ColCOSΦ platform, and its potential for further robotic research on multiple pheromones are also verified. Our study of using different pheromones for one collective swarm robotics task may help or inspire biologists in real insects' research

Fabrication and Mechanical Analysis of Bioinspired Gliding-optimized Wing Prototypes for Micro Aerial Vehicles

Gliding is the most efficient flight mode that is explicitly appreciated by natural fliers. This is achieved by high-performance structures developed over millions of years of evolution. One such prehistoric insect, locust (Schistocerca gregaria) is a perfect example of a natural glider capable of endured transatlantic flights, which could potentially inspire numerous solutions to the problems in aerospace engineering. However, biomimicry of such aerodynamic properties is hindered by the limitations of conventional as well as modern fabrication technologies in terms of precision and availability, respectively. Therefore, we explore and propose novel combinations of economical manufacturing methods to develop various locust-inspired tandem wing prototypes (i.e. fore and hindwings), for further wind tunnel based aerodynamic studies. Additionally, we determine the flexural stiffness and maximum deformation rate of our prototypes and compare it to their counterparts in nature and literature, recommending the most suitable artificial bioinspired wing for gliding micro aerial vehicle applications