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Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep.

This research work presents the development of an optimal path planning using elite opposition based bat algorithm (EOBA) for mobile robot, such that the robot avoids obstacle(s) without making contact with them. The bat algorithm (BA) is a nature inspired meta-heuristic algorithm that works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The EOBA is developed by modifying the BA with the elite opposition-based learning (EOBL) so as to diversify the solution search space and the inertial weight in order to balance its exploration and exploitation. The performance of the proposed path planning technique was compared with that of the standard BA based on the ability to generate an optimal path for a mobile robot in a developed simulation environment. The simulation results showed that EOBA provide an optimal path with minimum elapsed time as compared to that of the standard BA. All simulations were carried out using MATLAB R2013b.