## 1 Introduction  
Mobility is a distinct characteristic separating mobile robots from their base-fixed counterparts [1]. Navigation is an essential issue to realize the motion of mobile robots, which brings about three basic questions: (a) where am I? (b) where am I heading for? and (c) how do I get there? The third question pertains to path-guidance control [2]. If the position reference is not available temporarily, the navigation system typically enters a mode known as dead reckoning to maintain an estimate of the position [3]. Otherwise guidance involves obtaining a trajectory for a mobile robot from its position and orientation in order to follow a given path [4]. Given paths, or guide paths, the target to be continuously followed by mobile robots can be generated in various possible ways: path planning on a map (environment model) [2, 5, 6]; a parametric reference in a fixed coordinate frame [7, 8, 9, 10, 11]; and a physical landmark (wire, tape, stripe) on the ground [12, 13, 14, 15]. Recently, computer vision has been used to measure the _posture_ of mobile robotswhen tracking guide pathways. One approach resorts to a ceiling-mounted camera overlooking a mobile robot with specific colored labels on its platform [16; 17; 18]; another approach relies on an onboard camera looking down at a physical pathway (colored stripe) on the ground from the perspective of the robot [12; 14; 15]. For the first approach, the stationary cameras fixed in the environment have a large field of view, but at the cost of measurement precision. For example, a high-resolution camera overhead provides a positioning measurement value with a resolution of 14.9 mm for a robot with dimensions of 365 \(\times\) 419 mm [17]. In this sense, cameras mounted on robots in the second approach are preferred for applications requiring a high accuracy. Line tracking via an onboard camera is perhaps the prospective vision navigation approach mostly used in industrial environments, since it offers real-time, precise and reliable performance. The computational load relevant to this kind of vision perception is mitigated by coding or learning the specific perceptual knowledge that is expected to extract from the environment [15; 19; 20; 21]. Moreover, gluing colored stripes on the floor is inexpensive, compared not only with laying down a wire under the ground, but also with setting up a plurality of retro reflectors for a laser guidance system [22]. However, the limited field of view of onboard cameras brings great difficulties to the path tracking of differential-driving wheeled mobile robots (DWMRs) in high-speed manoeuvres, especially when only kinematic models [12; 14; 17] or pure pursuit controllers [23; 24; 25] are used. In order to cope with the demands of high-speed manoeuvres, dynamic models are used to describe the inherent characteristics of correlating actuation with motion in a DWMR. These models are normally force-driven, second-order differential equations relating wheel forces to robot acceleration [13; 18; 26]. When motor dynamics is included, robot models evolve to composite models. In these models, the platform _twist_ (A vector array that includes point velocity and rigid-body angular velocity.) can be associated with the time-derivative of motor-armature voltage [8], or with the duty ratio of pulse-width modulation (PWM) of motor voltage [27], or even with the motor current [28]. Controller design is influenced by system models of mobile robots. Pure pursuit controllers are a prevalent class of path tracking algorithms for mobile robots, while their performance is rather limited to relatively low speeds. Hence, the receding-horizon strategy is used to develop a model-based predictive active yaw control implementation for pure-pursuit tracking at high speeds [25]. The strategy is a model-based feedback control solution in terms of the limited sampling and control period, in which an optimal control trajectory is generated for the initial state, and updated at each sampled instant.