This paper presents a motion control method for mobile robots in partially unknown environments populated with moving obstacles. The proposed method is based on the integration of focused D* search algorithm and dynamic window local obstacle avoidance algorithm with some adaptations that provide efficient avoidance of moving obstacles. The moving obstacles are modelled as moving cells in the occupancy grid map and their motion is predicted by applying a procedure similar to the dynamic window approach. The collision points of the robot predicted trajectory and moving cells predicted trajectories form the new active obstacles in the environment, which should be avoided. The algorithms are implemented and verified using a Pioneer 3DX mobile robot equipped with laser range finder.

In this paper, the problem of path planning for active simultaneous localization and mapping (SLAM) is addressed. In order to improve its localization accuracy while autonomously exploring an unknown environment the robot needs to revisit positions seen before. To that end, we propose a path planning algorithm for active SLAM that continuously improves robot's localization while moving smoothly, without stopping, toward a goal position. The algorithm is based on the D* shortest path graph search algorithm with negative edge weights for finding the shortest path taking into account localization uncertainty. The proposed path planning algorithm is suitable for exploration of highly dynamic environments with moving obstacles and dynamic changes in localization demands. While the algorithm operation is illustrated in simulation experiments, its effectiveness is verified experimentally in real-world scenarios.

A receding horizon control (RHC) algorithm for convergent navigation of a differential drive mobile robot is proposed. Its objective function utilizes a local-minima-free navigation function to measure the cost-to-goal over the robot trajectory. The navigation function is derived from the path-search algorithm over a discretized 2-D search space. The proposed RHC navigation algorithm includes a systematic procedure for the generation of feasible control sequences. The optimal value of the objective function is employed as a Lyapunov function to prove a finite-time convergence of the discrete-time nonlinear closed-loop system to the goal state. The developed RHC navigation algorithm inherits fast replanning capability from the D* search algorithm, which is experimentally verified in changing indoor environments. The performance of the developed RHC navigation algorithm is compared with the state-of-the-art sample-based motion planning algorithm based on lattice graphs, which is combined with a trajectory tracking controller. The RHC navigation algorithm produces faster motion to the goal with significantly lower computational costs and it does not need any controller tuning to cope with diverse obstacle configurations.

In this paper we present a motion control method for mobile robots in partially known indoor environments based on integration of focussed D* search algorithm and dynamic window local obstacle avoidance algorithm. While focussed D* generates global path, dynamic window local obstacle avoider generates the admissible robot trajectories that ensure safe robot motion. A simple and efficient procedure to the selection of appropriate motion commands based upon alignment of acquired trajectories and global geometric path is proposed. The initial a priori knowledge is used about the environment in the form of the occupancy grid map that is incrementally updated in runtime. The algorithms are verified both by simulation and experiments with a Pioneer 2DX mobile robot equipped with laser range finder.

The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot.