Optimal Control for Unmanned Systems With One-Way Broadcast Communication

Abstract

Unmanned systems (USs) including unmanned aerial vehicles, unmanned underwater vehicles, and unmanned ground vehicles have great application prospects in military and civil fields, among which the process of finding feasible and optimal paths for the agents in USs is a kernel problem. Traditional path finding algorithms are hard to adequately obtain optimal paths in real-time under fast time-varying and poor communication environments. We propose an online optimal control algorithm for USs based on a one-way broadcast communication mode under the assumption of a poor communication environment, mobile targets, radars (or sonar), and missiles (or torpedoes). With the principle of receding horizon control, optimal (or suboptimal) paths are then generated by the approximation theory of neural networks and gradient optimization techniques, with low computation requirements. Also, we give a convergence analysis for our algorithm, and show that each agent can reach its target in finite time under some conditions on agents, targets and radar-missiles. Moreover, simulations demonstrate that the agents in USs can generate optimal (or suboptimal) paths in real time using our algorithm while effectively avoiding collision with other agents or detection by enemy radars.

Dynamic Motion Planning Model for Multirobot Using Graph Neural Network and Historical Information

“…To enhance the collaboration capability among robot clusters, Ge and Chen proposed a one‐way broadcast communication framework in which the robots can obtain the optimal path after receiving a message through the approximation theory of neural networks and gradient optimization techniques, whose shortcomings are the large bandwidth consumption and long communication delay during communication. [ 31 ]…”

“…To enhance the collaboration capability among robot clusters, Ge and Chen proposed a one-way broadcast communication framework in which the robots can obtain the optimal path after receiving a message through the approximation theory of neural networks and gradient optimization techniques, whose shortcomings are the large bandwidth consumption and long communication delay during communication. [31] Many studies in recent years have shown that learning-based approaches are fruitful in decentralized control strategies for robots. [32] Most methods combine deep learning or reinforcement learning to achieve path finding with good generalization and high solution quality.…”