Sequential nonlinear manifold learning

The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems

Fast nonlinear model predictive planner and control for an unmanned ground vehicle in the presence of disturbances and dynamic obstacles

Abstract This paper presents a solution for the tracking control problem, for an unmanned ground vehicle (UGV), under the presence of skid-slip and external disturbances in an environment with static and moving obstacles. To achieve the proposed task, we have used a path-planner which is based on fast nonlinear model predictive control (NMPC); the planner generates feasible trajectories for the kinematic and dynamic controllers to drive the vehicle safely to the goal location. Additionally, the NMPC deals with dynamic and static obstacles in the environment. A kinematic controller (KC) is designed using evolutionary programming (EP), which tunes the gains of the KC. The velocity commands, generated by KC, are then fed to a dynamic controller, which jointly operates with a nonlinear disturbance observer (NDO) to prevent the effects of perturbations. Furthermore, pseudo priority queues (PPQ) based Dijkstra algorithm is combined with NMPC to propose optimal path to perform map-based practical simulation. Finally, simulation based experiments are performed to verify the technique. Results suggest that the proposed method can accurately work, in real-time under limited processing resources

E.: Solving computational and memory requirements of feature based simultaneous localization and map building algorithms

This paper presents new algorithms to implement simultaneous localisation and map building (SLAM) in environments with very large number of features. The algorithms present an efficient solution to the full update required by the Compressed Extended Kalman Filter algorithm (CEKF). It makes uses of the Relative Landmark Representation (RLR) to develop very close to optimal de-correlation solutions. With this approach the memory and computational requirements are reduced from ~O(N 2) to ~O(N*Nb), being N and Nb proportional to the number of features in the map and features close to the vehicle respectively. Experimental results are presented to verify the operation of the system when working in large outdoor environments.

Improving computational and memory requirements of simultaneous localization and map building algorithms

This paper addresses the problem of implementing simultaneous localisation and map building (SLAM) in very large outdoor environments. A method is presented to reduce the computational requirement from ~O(N 2) to ~O(N), being N the states used to represent all the landmarks and vehicle pose. With this implementation the memory requirement are also reduced to ~O(N). This algorithm presents an efficient solution to the full update required by the Compressed Extended Kalman Filter algorithm (CEKF). Experimental results are also presented.