The agricultural domain has been experiencing extensive automation interest over the past decade. The high demands from the growing population and the environmental impact put pressure on agricultural productivity. The Autonomous Robot for Orchard Surveying (AROS) was developed to mitigate such issues. AROS is an autonomous Skid-Steer Mobile Robot capable of collecting visual and spatial data of apple trees. It is set to autonomously navigate the orchard while collecting visual and spatial data of the trees. This is achieved by designing an inter-row path planner and the control framework. Additionally, further evaluations are made on multiple controllers. Thus, the following six controllers are evaluated:​ Proportional-Derivative (PD) controller, Sliding Mode Controller (SMC), Control-Lyapunov Function (CLF), Nonlinear Model Predictive Controller (NMPC), TubeBased Nonlinear Model Predictive Controller (TBNMPC), and Model Predictive Sliding Mode Control (MPSMC). Simulated and experimental results show that the model predictive controls, namely the NMPC and TBNMPC, performed the best, and the non-optimal controllers, such as the CLF, SMC, PD, and MPSMC, had low performance due to the underactuated constraint. Furthermore, the MPSMC showed to have the least amount of control effort.