He Du

A novel fixed-time cooperative guidance law is developed to enable multiple flight vehicles to simultaneously intercept various target motions with desired impact angle, including stationary, constant-velocity moving and manoeuvring targets, with a unified guidance structure that requires no mode switching or target motion classification. First, a fixed-time distributed cooperative guidance law is developed using the theory of multi-intelligence cooperative control, which is formulated along the line of sight (LOS) direction. This approach ensures that the impact time is regulated, enabling multiple flight vehicles to intercept the target simultaneously within a fixed time. In the second phase, employing a sliding manifold and a fixed-time reaching law, the normal acceleration of each flight vehicle is designed to ensure that the LOS angle converges to its target value within a fixed duration. Unknown components of the target’s acceleration are estimated via fixed-time observers and incorporated into the guidance commands, enhancing precision and robustness in the guidance process. In conclusion, the proposed approach proves the effectiveness and superiority through numerical simulations, including various target motions, switching communication topology, robustness against uncertainties based on Monte Carlo experiment and comparative studies.

In this paper, a sliding mode guidance law for impact angle control without violating a seeker’s field-of-view limits is proposed against targets with various motions and unknown acceleration, including stationary, constant-velocity moving and manoeuvring targets. To develop the guidance law, the kinematic conditions for engagement geometry are defined, and the sliding mode control is applied to satisfy the homing constraint and impact angle control. Then, the relation between look angle, the desired line-of-sight angle and desired impact angle is established to guarantee the target in the field of the missile’s view. The stability of the proposed approach is analysed using Lyapunov theory. Furthermore, the look angle is examined to verify the field-of-view constraint, and a capturability analysis is conducted. To evaluate the performance of the proposed law, numerical simulations demonstrate that the proposed approach achieves satisfactory miss distance and impact angle error while adhering to the field-of-view limit.

The path-planning approach plays an important role in determining how long the mobile robots can travel. To solve the path-planning problem of mobile robots in an unknown environment, a potential and dynamic Q-learning (PDQL) approach is proposed, which combines Q-learning with the artificial potential field and dynamic reward function to generate a feasible path. The proposed algorithm has a significant improvement in computing time and convergence speed compared to its classical counterpart. Experiments undertaken on simulated maps confirm that the PDQL when used for the path-planning problem of mobile robots in an unknown environment outperforms the state-of-the-art algorithms with respect to two metrics: path length and turning angle. The simulation results show the effectiveness and practicality of the proposal for mobile robot path planning.

To solve the problem of automatic recharging path planning for cleaning robots in complex industrial environments, this paper proposes two environmental path planning types based on designated charging location and multiple charging locations. First, we use the improved Maklink graph to plan the complex environment; then, we use the Dijkstra algorithm to plan the global path to reduce the complex two-dimensional path planning to one dimension; finally, we use the improved fruit fly optimization algorithm (IFOA) to adjust the path nodes for shorting the path length. Simulation experiments show that the effectiveness of using this path planning method in a complex industrial environment enables the cleaning robot to select a designated location or the nearest charging location to recharge when the power is limited. The proposed improved algorithm has the characteristics of a small amount of calculation, high precision, and fast convergence speed.