부산대학교 도서관

Autonomous mobile robots (AMRs) have proven useful for smart factories and have potential to revolutionize critical missions, such as disaster rescue [1]. As illustrated in Fig. 2.5.1, AMRs can perceive the environment, plan for assigned tasks and act on the plan [2]. Motion control is critical to trajectory adjustment for joint control or intelligent navigation, especially when AMRs are operated in a fast-changing environment. This is accomplished through trajectory optimization to refine the robot states using a physics model [3]. A command sequence for motion control is generated by taking the next possible states into account. The next states of the trajectory are predicted by robots' dynamics. As one would expect, robots can respond faster and act more resiliently when the control rate and the number of trajectory time steps increase, respectively. However, there is a fundamental tradeoff between the control rate and the number of trajectory time steps. A motion control accelerator is demonstrated in [4] to improve the motion control capability. It achieves a 1kHz maximum control rate for up to 30 trajectory time steps, but the control rate decreases to