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.

Mobile robots have applications in military (for reconnaissance, search and rescue operations, bomb detection, surveillance), transportation (for cargo and packet delivery), data acquisition, etc. For the mobile robots to be able to execute these tasks with minimum or no human intervention, they need to be autonomous and intelligent. Path planning (PP) is one of the most critical areas of concern in the field of autonomous mobile robots. It is about obtaining a collision-free motion optimal path based on either time, distance, energy or cost in a static or dynamic environment containing obstacles. However, power limitation hinders the mobile robots to accomplish their task of reaching the target location as there are several paths they can follow. Each of these paths has its own path length, cost (i.e., time to reach destination), and energy constraint, thus, the need to plan for an optimal path according to a certain performance criterion. Significant research has been conducted in recent years to address the PP problem. Hence, this chapter is aimed at presenting the different approaches for PP of mobile robots with respect to different optimality criteria (time, distance, energy and cost), challenges and making recommendations on possible areas of future research.

This paper presents a density-based traffic light signal system that performs timing signal that changes automatically based on the amount of traffic at each of its intersections. However, as traffic congestion is a pertinent problem on all of Ahmadu Bello University’s (ABU) gates, it is time to advance from the traditional technique to an automated system that has self-decision capabilities. The current technique used on the traffic system is based on the traditional technique, which works based on time scheduling; this system is inefficient if one lane is operational while the others are not operational. The intelligent traffic control was prototyped in order to solve this perennial problem of ABU’s gate. When there is a high density on one lane of the intersection, it causes a longer waiting time on the other lanes than the regular permitted time. As a result, a process was designed through which the time periods for the green and red lights were assigned based on the traffic densities on each of the lanes at that time. Infrared (IR) sensors were used to perform this task. The Arduino Uno Microcontroller was used for allocating the glowing period of green lights once density had been calculated. Sensors were used for monitoring the presence of vehicles and communicating information to the microcontroller, which determines the duration for which a signal will change or a flank will remain open. Also displayed is the operating principle of the density-based traffic signal control system, which shows the prototype’s efficiency.

Recently, morphing quadcopters have gained an unprecedented popularity due to their nature of flexibility, self-controlled arm management and diversified application. It has been established that in morphing quadcopter control, aerial morphing generally introduces time-varying parameters into the dynamic model, thereby increasing the complexity of the control problem, in addition to the non-linearity, coupling dynamics, and external disturbances present in the model. Thus, to address those challenges, this research aimed at developing a robust backstepping sliding mode controller (BSMC) for morphing quadcopter position and orientation control. to achieve the stated aim, mathematical model of an active morphing quadcopter (a foldable drone) was presented considering five morphing formations (X, H, T, O, and Y). Following the development of the system model, the proposed control method was designed in two stages: a high-performance sliding mode controller (HSMC) for attitude control to ensure chattering-free and fast convergence of the orientation angles and a backstepping controller for position control. The robustness and effectiveness of the proposed controller were investigated and benchmarked against a backstepping control approach. The simulation results obtained show the effectiveness of the developed controller against the backstepping approach in the presence of parameter variations and external disturbances.

The multi-axle crane, a long vehicle with high inertia, has historically struggled with steering efficiency and path-tracking performance. Various control strategies, including Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC), have been employed to address these challenges. However, while improving steering efficiency, these strategies have often led to poor path-tracking performance. This work presents a significant advancement in the form of an optimized MPC for improved steering control of the multi-axle crane. A bicycle model of the multi-axle crane was adopted for the work. MPC was designed, and the smell agent optimization technique (SAO) was employed to optimize the steering input weighting factor, which determines the path-tracking performance. This provided an improved and accurate path-tracking performance for different driving speed conditions. Simulation and performance evaluation of the optimized MPC for the steering system were carried out on a curved road path for three different driving speed scenarios (25, 45, and 65 km/h). The results were compared with existing steering systems that utilized the MPC using steering efficiency, dynamic stability, and path-tracking performance. Results obtained showed improvements of 13.88%, 46.02%, and 18.35% in steering efficiency for the three scenarios over the benchmark scheme. Similarly, improvements of 2.29%, 1.03%, and 4.17%, respectively, were achieved in terms of dynamic stability for the three scenarios. For lateral error, improvements of 26.78%, 26.35%, and 27.52% were achieved, while 27.44%, 29.25%, and 28.93% were achieved for the yaw angle error in the three scenarios, respectively. A 3D simulation model for the multi-axle crane was developed in AnyLogic for visual interpretation and validation of the tracking results. These results showed that the developed MPC steering system achieved better steering performance than the existing scheme.