This paper proposes a mobile robot motion control and planning system for trajectory tracking and obstacle avoidance in a prior unknown robot environment. The proposed system has two-level control and planning architecture: the higher is used to generate a path, while the lower provides the control actions that drive the robot. The planning level represents a reactive planer which determines on-line way-points during the robot’s movement towards the target and allowing the robot to move autonomously through an environment without colliding with obstacles. The main objective of this algorithm is to reduce the number of obstacles that are taken into consideration when determining the intermediate target point (way-points) in the movement towards the target location. This proposed algorithm is based on the concept of calculating the intersection of the variable target circle and the robot perception circle (VTPC), as well as attractive and repulsive forces. The lower level includes a fuzzy logic controller that drives the robot along generated online trajectory. It compares the current position of the mobile robot with the desired position, generating the appropriate linear speeds for the robot’s wheels to reach the target point in the shortest possible time. A series of simulations demonstrate its effectiveness in generating and executing the paths in various unknown robot environments.

This paper presents a comparative analysis of state estimation techniques, SDRE-based Kalman and H-infinity filters, ${\mathcal{H}_\infty }$, within the context of nonlinear systems controlled via the State-Dependent Riccati Equation (SDRE) framework. While the SDRE-based Kalman filter is effective under Gaussian noise assumptions, it can underperform in environments with non-Gaussian disturbances. The SDRE-based ${\mathcal{H}_\infty }$ filter, on the other hand, offers a robust alternative by minimizing the worst-case estimation error without assuming any noise statistics. To evaluate both filters, we conduct simulation studies on two real-world scenarios: temperature control in a data center and interference mitigation in a wireless cellular network. Results demonstrate that the ${\mathcal{H}_\infty }$ filter outperforms its Kalman counterpart in the presence of non-Gaussian noise, validating its suitability for robust estimation in practical applications.

This paper presents a robust control design for linear systems with matched external disturbances based on linear-quadratic regulator (LQR) and sliding mode control (SMC). The design includes a quadratic integral sliding manifold for which LQR is equivalent control, so it does not force the system to change its optimal dynamics during the transient response like in other commonly used state-of-the-art SMC design procedures. The simulation study suggests that the proposed control design robustifies optimal LQR with minimal deterioration of optimality.

Classical RRT algorithm is blind to efficiently explore configuration space for expanding the tree through a narrow passage when solving a motion planning (MP) problem. Although there have been several attempts to deal with narrow passages which are based on a wide spectrum of assumptions and configuration setups, we solve this problem in rather general way. We use dominant eigenvectors of the configuration sets formed by properly sampling the space around the nearest node, to efficiently expand the tree around the obstacles and through narrow passages. Unlike classical RRT, our algorithm is aware of having the tree nodes in front of a narrow passage and in a narrow passage, which enables a proper tree expansion in a vine-like manner. A thorough comparison with RRT, RRT-connect, and DDRRT algorithm is provided by solving three different difficult MP problems. The results suggest a significant superiority the proposed Rapidly-exploring Random Vines (RRV) algorithm might have in configuration spaces with narrow passages.

This paper proposes a receding horizon optimization framework (RHC) for finding an approximate solution to different constrained multi-vehicle coverage problems. The optimization is based on the algorithm we have already developed for unconstrained multi-vehicle coverage problem, which inherently possessed some nice properties for dealing with unconstrained coverage problem setups. Although it was shown that the algorithm had preferred to choose obstacle-free areas during the task execution, it was not possible to guarantee collision free paths. The proposed RHC is, however, capable of handling constraints that might be present within a coverage problem, such as those imposed by the presence of obstacles and/or by different time limitations imposed on the duration of the vehicles' missions.

Obtaining the optimal cost-to-go map for large scale rough terrains is computationally very expensive both in terms of duration and memory resources. A fast algorithm for approximation of the optimal cost-to-go map in terms of terrain traversability measures for path planning on known large scale rough terrains is developed. The results show that the majority of the cost-to-go map values, computed from every terrain location with respect to the goal location, are near-optimal. Unlike Dijkstra algorithm, the proposed algorithm has inherently parallel structure, and can be significantly speeded up depending on the number of used CPU cores.

This paper presents a novel navigation and motion planning algorithm for mobile vehicles in rough terrains. The main purpose of the algorithm is to generate feasible trajectories while selecting smoother paths, in the sense of level of roughness, toward the goal position. The purpose is achieved by adapting the passivity-based model predictive control optimization setup (PB/MPC), recently proposed for flat terrains, to the case of an outdoor irregular terrain. The passivity-based concept is used to enhance MPC in order to stabilize the goal position guaranteeing the task completion. The framework which is obtained can exploit any vehicle model in order to carefully take into account the vehicle dynamics and terrain structure as well as the wheel-terrain interaction. The inherited property of the MPC optimization allows to impose any additional constraint into the PB/MPC navigation, such as those needed to prevent vehicle rollover and unnecessary sideslip. The cost function representing the level of roughness along a candidate path is used to select the appropriate terrain areas toward the goal position. The results have been verified by several simulation examples.