Small untethered soft robots have potential for diverse applications, particularly in constrained spaces where the use of a tethered device would be infeasible. Examples include biomedical applications such as brachytherapy, fine-needle biospy and micro-needle drug delivery. To advance soft robots towards these applications, there is a need to establish methods for tracking and control using clinically-relevant methods. This study demonstrates motion planning and magnetic control of a soft untethered robot, using ultrasound images as feedback. The closed-loop control of the Millipede soft robot is first validated using a camera-based tracker, where the deviation between the planned path and the trajectory of the robot is 1.71 mm. Afterwards, two methods for ultrasound-based tracking capable of estimating the pose of the robot are proposed, a geometric approach and a convolutional neural network (CNN), and their performance is compared using a video camera as ground truth. Following this, the CNN method replaces the camera tracker to estimate the position and orientation of the robot. The closed-loop system using ultrasound images guides the robot through the workspace while avoiding virtual obstacles, and achieves an average tracking error of 1.59 mm and an angle error of 2.24°.