# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
## 1 Introduction
Now, about two dozen former NASA engineers, many of whom helped build the robot astronaut, have turned their skills to creating underwater robots at Nauticus Robotics [1]; (**b**) Nauticus is also commercializing the robotic arm technology–known as Olympic Arm–that it developed while designing and building Aquanaut. Image credits (both): Nauticus Robotics Inc., Webster, IA, USA [1] used in compliance with image use policy [2], “NASA content (images, videos, audio, etc.) are generally not copyrighted and may be used for educational or informational purposes without needing explicit permissions”. a disparate approach based in analytical dynamics, introduced as the Udwadia-Kalaba approach were compared in reference [13]. Very recently, integration of fuel slosh with centralized sensors and actuators, without the usage of collocated devices for vibration management. into techniques to control the motion of flexible appendages was offered by [14] reiterating the relevance of classical proportional, derivative (PD) control with nonadaptive bandpass filters, where the novel proposition includes integration of wave-based control with the filtered PD control scheme. Vibration suppression was illustrated by establishing a dynamic grasping area to describe the contact procedure of the capture device grasping target in reference [15]. Control of rotation-floating space robots with flexible appendages specifically for on-orbit servicing was proposed in [16]. Techniques were suggested using a composite two-time-scale control system [17]. Open-loop methods were strictly used for analysis while closed-loop was utilized for control in reference [18]. Another alternative is adaptive feedback control [19]. The following list highlights the current state of the art developing deterministic artificial intelligence:  
1. In 2019, reference [11] revealed an optimal control revealed by pseudospectral optimization software where the solution validation was provided using six theoretical necessary conditions of optimization: (1) Hamiltonian minimization condition; (2) adjoint equations; (3) terminal transversality condition; (4) Hamiltonian final value condition; (5) Hamiltonian evolution equation; and lastly (6) Bellman's principle. The results are novel and unique in that they initially command full control in the opposite direction from the desired end state, while no such results are seen using classical control methods including classical methods augmented with structural filters typically employed for controlling highly flexible multi-body systems. 2. Later in 2022, an interesting study of the use of feedback and structural filtering to maximize system stability was offered [10], leading to the recommendation to use single-sinusoidal trajectory shaping to maximize stability. The following brief list articulates the novel growth from the current state of the art methods. 1. Rather than propose options for maximizing stability [10], this study seeks to offer advice to minimize trajectory tracking errors. 2. Rather than focus on feedforward [11] versus feedback [10], this study investigates commanded trajectory tracking options. This manuscript advises the readership on methods to shape the commanded trajectory to be tracked, where sinusoidal, whiplash, and step trajectories are critically compared using tracking errors (both angle and angular rate) and control effort as figures of merit.