The paper researches a utilization of the Salp Swarm Algorithm (SSA), a bio-mimetic optimization technique, to improve path planning in Unmanned Ground Vehicles (UGVs). Because of the crucial role of the efficient and reliable path planning in the implementation of UGVs in such sectors as military, rescue operations, and agriculture, there is a need for algorithms that are capable of navigating complex environments. The concept of SSA, based on the natural swarming behavior of salps, represents a very promising approach that is characterized by the exploration and exploitation properties of the algorithm. This study evaluates the performance of the SSA relative to existing particle swarm optimization (PSO), in terms of path optimality, computational efficiency, and dynamic obstacle adaptability, through a number of simulated environments. Results show that the SSA has the potential to compete with the traditional algorithms in path efficiency and computational load. However, PSO shows slight superiority results compared to SSA. This study highlights the potency of bio-inspired algorithms, specifically the SSA, in enhancing the field of autonomous navigation for UGVs. It introduces new possibilities of practical application of SSA in real-life scenarios, demonstrating its scalability and resilience. The findings of this study make a contribution to the general discussion on the improvement of planning of autonomous routes and provide a possible way for more sustainable and effective UGV activities.
Path planning and obstacle avoidance are crucial tasks in the robotics and autonomous industry. Path planning seeks to determine the most efficient path between a start and an end point, whereas obstacle avoidance seeks to avoid collisions with static or dynamic obstacles in the environment. On this work, we utilize the Chameleon Swarm Algorithm (CSA), which is a metaheuristic approach, for path planning and obstacle avoidance on a predetermined map with static obstacles. This CSA extracted the optimal path from several possible different paths, and the results showed that it has slightly superior performance compared to PSO.
As the world develops, new and more advanced ways of transportation are invented; i.e. drones. Drones are used in several applications. However, the drone market does not utilize the need of medical emergency drones today, where these drones can be used to save countless lives in severe cases, e.g. sudden cardiac arrest. In case of cardiac arrest, defibrillators may save the life if it reaches the victims within short time. It raises the survival rate exponentially. Nonetheless, reaching the victims in a short period of time is challenging as the weight of the equipment is large. This work aims to design an autonomous drone that will be able to carry heavy payloads (portable medical equipment) while being fast and agile. The medical equipment/components are studied to choose the most fit for the proposed design in terms of efficiency and weight. The drone’s components are compared and studied in detail, allowing to choose the fittest motors, ESCs, frame, battery, and propellers. After which the quadcopter’s ability is expected to successfully achieve the objective of trying to save victim life in the city of Sharjah. In addition, the work includes a SolidWorks analysis to the design of the drone’s mechanical components to estimate the possibility of failure.
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