Dynamic Optimization of a Steerable Screw In-pipe Inspection Robot Using HJB and Turbine Installation. Issue 11 (16th November 2020)

• Record Type: Journal Article
• Authors: Tourajizadeh, H.; Boomeri, V.; Rezaei, M.; Sedigh, A.
• Is Part Of: Robotica. Volume 38:Issue 11(2020)
• Page Start: 2001
• Page End: 2022
• Publication Date: 2020-11-16
• Subjects: In-pipe inspection robot, -- Optimal control, -- Energy harvesting, -- Optimal modeling; Robots -- Periodicals; 629.89205
• DOI: 10.1017/S0263574719001784
• Languages: English
• ISSNs: 0263-5747

Abstract

SUMMARY: In this paper, two strategies are proposed to optimize the energy consumption of a new screw in-pipe inspection robot which is steerable. In the first method, optimization is performed using the optimal path planning and implementing the Hamilton–Jacobi–Bellman (HJB) method. Since the number of actuators is more than the number of degrees of freedom of the system for the proposed steerable case, it is possible to minimize the energy consumption by the aid of the dynamics of the system. In the second method, the mechanics of the robot is modified by installing some turbine blades through which the drag force of the pipeline fluid can be employed to decrease the required propulsion force of the robot. It is shown that using both of the mentioned improvements, that is, using HJB formulation for the steerable robot and installing the turbine blades can significantly save power and energy. However, it will be shown that for the latter case this improvement is extremely dependent on the alignment of the fluid stream direction with respect to the direction of the robot velocity, while this optimization is independent of this case for the former strategy. On the other hand, the path planning dictates a special pattern of speed functionality while for the robot equipped by blades, saving the energy is possible for any desired input path. The correctness of the modeling is verified by comparing the results of MATLAB and ADAMS, while the efficiency of the proposed optimization algorithms is checked by the aid of some analytic and comparative simulations.