Localization of a unicycle-like mobile robot using LRF and omni-directional camera

Abstract

This paper addresses the localization problem. The extended Kalman filter (EKF) is employed to localize a unicyclelike mobile robot equipped with a laser range finder (LRF) sensor and an omni-directional camera. The LRF is used to scan the environment which is described with line segments. The segments are extracted by a modified least square quadratic method in which a dynamic threshold is injected. The camera is employed to determine the robot's orientation. The prediction step of the EKF is performed by extracting parameters from the kinematic model and input signal of the robot. The correction step is conducted with the implementation of a line matching algorithm and the comparison between line's parameters of the local and global maps. In the line matching algorithm, a conversion matrix is introduced to reduce the computation cost. Experiments have been carried out in a real mobile robot system and the results prove the applicability of the method for the purpose of localization.

Composite Cooperative Adaptive Learning and Control for Multi-Robot Coordination

“…Specifically, we consider four identical unicycle-type ground vehicles tracking four different trajectories while communicating inside the MAS. For each vehicle, the state-space model is described by the kinematics (7) and dynamics (17), with matrices of the dynamic model given by (18) and friction modelF = 0.1mv…”

“…For each i = 1, 2, 3, 4, we normalize Figure 3, and the Laplacian matrix L associated with the graph G is dynamics (17), and the weight updating law (32). The vehicles are simulated on the time period from 0 to 300 seconds, with the initial position of the vehicles set at the origin of the ground frame, the velocities set to be zero, and the initial weights of RBFNNs set to be zero as well.…”

“…Theorem 3. Consider the closed-loop system including n unicycle-type vehicles described by equation 7and (17), the desired reference trajectory q ri ∈ ∪ Proof. Similar to the proof of Theorem 1, we first derive the error dynamics of the velocity error u i with the proposed experience-based controller (46) aṡ…”

“…Theorem 5. Consider the closed-loop system including n unicycle-type vehicles described by equation 7and (17), the desired reference trajectory q r (t), high-gain observer (53) and (55), adaptive NN controller (61) with the virtual velocity (22), and the online weight updating law (62), under the assumptions 1, 2, and 3, for any bounded initial condition of all the vehicles andŴ i = 0, both tracking control and learning objectives can be achieved at the same time for all vehicle agents in the MAS, i.e.,…”

“…. [1,2,3,4,5], formation control [6,7,8,9,10], path planning [11,12,13,14,15], localization [16,17,18,19,20], etc.…”