Dynamic Optimization of a Steerable Screw In-pipe Inspection Robot Using HJB and Turbine Installation

Robotica ◽  
2020 ◽  
Vol 38 (11) ◽  
pp. 2001-2022
Author(s):  
H. Tourajizadeh ◽  
V. Boomeri ◽  
M. Rezaei ◽  
A. Sedigh
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Degrees Of Freedom ◽  
Optimal Path ◽  
Turbine Blades ◽  
Pipe Inspection ◽  
Inspection Robot ◽  
Propulsion Force ◽  
Hamilton Jacobi Bellman ◽  
Power And Energy

SUMMARYIn 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.

Optimal path planning of manipulatory systems subjected to non-autonomous motion laws

Robotica ◽  
1997 ◽  
Vol 15 (3) ◽  
pp. 251-261 ◽  
Author(s):  
A. D. Jutard-Malinge ◽  
G. Bessonnet
Keyword(s):  
Path Planning ◽  
Mechanical System ◽  
Degrees Of Freedom ◽  
Optimal Path ◽  
State Equation ◽  
Dynamic Complexity ◽  
Travelling Time ◽  
Complementary Part ◽  
The Cost ◽  
Autonomous State

A path planning method is presented based on non-autonomous dynamic modeling of open-loops in articulated systems. It is assumed that one part of the mechanical system is submitted to specified motions laws, while movements of the complementary part are free. Thus, motion optimization is related to free joint movements but it is achieved on the basis of the dynamic model of the whole mechanical system. This approach introduces a non-autonomous state equation of a special type in the sense that it can not only depend on the running time but also on the unknown travelling time. The cost function to be minimized involves the travelling time and the actuating inputs. Optimization is achieved by applying the Pontryagin Maximum Principle which yields a new optimality condition concerning the travelling time dependency of the stated problem. Two simulation examples are presented. The first one shows how the developed technique makes possible both the reducing and mastering the dynamic complexity of a four degrees of freedom-vertical manipulator. Set at four degrees of freedom, the second one deals with a redundant planar manipulator characterized by a mobile base that is submitted to a specified driving motion.

scholarly journals E2PP: An Energy-Efficient Path Planning Method for UAV-Assisted Data Collection

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiang Ji ◽  
Xianjia Meng ◽  
Anwen Wang ◽  
Qingyi Hua ◽  
Fuwei Wang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Collection ◽  
Path Planning ◽  
Greedy Algorithm ◽  
Energy Efficient ◽  
Optimal Path ◽  
Planning Method ◽  
Estimation Model ◽  
Aerial Vehicle ◽  
The Relationship

Using an unmanned aerial vehicle (UAV) to collect data from wireless sensor networks deployed in the field, one of the key tasks is to plan the path for the collection so as to minimize the energy consumption of the UAV. At present, most of the existing methods generally take the shortest flight distance as the optimal objective to plan the optimal path. They simply believe that the shortest path means the least energy consumption of the UAV and ignore the fact that changing direction (heading) can also consume the UAV’s energy in its flight. If the path can be planned based on the UAV’s energy consumption closer to the real situation, the energy consumption of the UAV can be really reduced and its working energy efficiency can be improved. Therefore, this paper proposes a path planning method for UAV-assisted data collection, which can plan an energy-efficient flight path. Firstly, by analyzing the experiment data, we, respectively, model the relationship between the angle of heading change and the energy consumption of the UAV and the relationship between the distance of straight flight and the energy consumption of the UAV. Then, an energy consumption estimation model based on distance and the angle of heading change (ECEMBDA) is put up. By using this model, we can estimate or predict the energy consumption of a UAV to fly from one point (or node) to another (including the start point). Finally, the greedy algorithm is used to plan the path for UAV-assisted data collection according to the above estimated energy consumption. Through simulation and experiments, we compare our proposed method with the conventional method based on pure distance index and greedy algorithm. The results show that this method can obtain data collection path with lower energy consumption and smoother path trajectory, which is more suitable for actual flight.

scholarly journals Design, Modelling and Laboratory Testing of a Pipe Inspection Robot

2015 ◽  
Vol 62 (3) ◽  
pp. 395-408 ◽  
Author(s):  
Michał Ciszewski ◽  
Michał Wacławski ◽  
Tomasz Buratowski ◽  
Mariusz Giergiel ◽  
Krzysztof Kurc
Keyword(s):  
Energy Consumption ◽  
Work Environment ◽  
Cross Section ◽  
Laboratory Testing ◽  
Laboratory Tests ◽  
Kinematic Model ◽  
Positioning System ◽  
Pipe Inspection ◽  
Inspection Robot

Abstract This paper presents a design of a tracked in-pipe inspection mobile robot with an adaptive drive positioning system. The robot is intended to operate in circular and rectangular pipes and ducts, oriented horizontally and vertically. The paper covers a design process of a virtual prototype, focusing on track adaptation to work environment. A mathematical description of a kinematic model of the robot is presented. Operation of the prototype in pipes with a cross-section greater than 210 mm is described. Laboratory tests that validate the design and enable determination of energy consumption of the robot are presented.

A New Method for the Path Planning of an In-Pipe Inspection Robot

2003 ◽  
Author(s):  
Vincent Artigue ◽  
Madeleine Pascal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cylindrical Body ◽  
Robot Motion ◽  
Pipe Inspection ◽  
Curved Pipe ◽  
Prismatic Joint ◽  
Inspection Robot ◽  
Dc Motors ◽  
Locking Phenomena

A mobile robot designed for in-pipe inspection is considered. The robot consists of a cylindrical body with six links, each ended by a driven wheel. For each link, a spring connected to a jack (prismatic joint) is used to push the wheel against the wall of the pipe. The robot is actuated by DC motors applied on the wheels. The aim of the study is to define the trajectory of the robot in a curved pipe. Several experiments have shown that in this case, the robot may be stopped in some parts of the elbows. The path planning is performed assuming that the robot motion is very slow. An energetic method is used in order to detect the locking zones, and to define the best trajectories avoiding these locking phenomena.

Immune–wavelet optimization for path planning of large-scale robots

Robotica ◽  
2013 ◽  
Vol 32 (1) ◽  
pp. 77-95 ◽  
Author(s):  
Saeid Asadi ◽  
Vahid Azimirad ◽  
Ali Eslami ◽  
Saeid Karimian Eghbal
Keyword(s):  
Path Planning ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Optimal Path ◽  
Robotic Systems ◽  
Mobile Manipulators ◽  
Optimal Path Planning ◽  
The Common ◽  
Planar Robot ◽  
Complexity Of Algorithm

SUMMARYIn this paper an optimal path planning method based on a new evolutionary algorithm is presented for higher order robotic systems. It is a combination of immune system and wavelet mutation. By increasing the system's dimensions, the complexity of algorithm grows linearly. The obtained results have been compared with other optimal path producing algorithms, and its excellence in terms of optimality has been proved. Strengths of this method are simplicity in large-scale path planning, being free of most of the common deadlocks in usual method, and ability to obtain more optimized results than other similar methods. The effectiveness of this approach on simulation case studies for a three-link planar robot and 5 degrees of freedom mobile manipulators as well as an experiment for a mobile robot called K-joniour is shown.

Motion Planning with Energy Reduction for a Floating Robotic Platform Under Disturbances and Measurement Noise Using Reinforcement Learning

2018 ◽  
Vol 27 (04) ◽  
pp. 1860005 ◽  
Author(s):  
Konstantinos Tziortziotis ◽  
Nikolaos Tziortziotis ◽  
Kostas Vlachos ◽  
Konstantinos Blekas
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Degrees Of Freedom ◽  
Optimal Path ◽  
Iteration Scheme ◽  
Measurement Noise ◽  
Target Point ◽  
Level Control ◽  
Reward Function ◽  
Marine Platform

This paper investigates the use of reinforcement learning for the navigation of an over-actuated, i.e. more control inputs than degrees of freedom, marine platform in unknown environment. The proposed approach uses an online least-squared policy iteration scheme for value function approximation in order to estimate optimal policy, in conjunction with a low-level control system that controls the magnitude of the linear velocity, and the orientation of the platform. Primary goal of the proposed scheme is the reduction of the consumed energy. To that end, we propose a variable reward function that depends on the energy consumption of the platform. We evaluate our approach in a complex and realistic simulation environment and report results concerning its performance on estimating optimal navigation policies under different environmental disturbances, and position GPS measurement noise. The proposed framework is compared, in terms of energy consumption, to a baseline approach based on virtual potential fields. The results show that the marine platform successfully discovers the target point following a sub-optimal path, maintaining reduced energy consumption.

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