Energy-optimal motion planning of a biped pole-climbing robot with kinodynamic constraints

Author(s):  
Xuefeng Zhou ◽  
Li Jiang ◽  
Yisheng Guan ◽  
Haifei Zhu ◽  
Dan Huang ◽  
...  

Purpose Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment. Design/methodology/approach First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively. Findings Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper. Research limitations/implications Only one step climbing motion planning is considered in this method. Practical implications With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint. Originality/value The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

2020 ◽  
Vol 12 (03) ◽  
pp. 2050040
Author(s):  
Cesar A. Ipanaque Zapata ◽  
Jesús González

We present optimal motion planning algorithms which can be used in designing practical systems controlling objects moving in Euclidean space without collisions. Our algorithms are optimal in a very concrete sense, namely, they have the minimal possible number of local planners. Our algorithms are motivated by those presented by Mas-Ku and Torres-giese (as streamlined by Farber), and are developed within the more general context of the multitasking (a.k.a. higher) motion planning problem. In addition, an eventual implementation of our algorithms is expected to work more efficiently than previous ones when applied to systems with a large number of moving objects.


2014 ◽  
Vol 24 ◽  
pp. 889-899 ◽  
Author(s):  
Liana Napalkova ◽  
Jerzy W. Rozenblit ◽  
George Hwang ◽  
Allan J. Hamilton ◽  
Liana Suantak

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5011
Author(s):  
Juan Parras ◽  
Patricia A. Apellániz ◽  
Santiago Zazo

We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools—namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton–Jacobi–Bellman PDE that can be used to solve continuous time and state optimal control problems. In order to make our approach more realistic, we consider that there are disturbances in the underwater medium that affect the trajectory of the autonomous vehicle. After adapting DGM by making use of a surrogate approach, our results show that our method is able to efficiently solve the proposed problem, providing large improvements over a baseline control in terms of costs, especially in the case in which the disturbances effects are more significant.


2021 ◽  
pp. 1-1
Author(s):  
Camilla Tabasso ◽  
Nicola Mimmo ◽  
Venanzio Cichella ◽  
Lorenzo Marconi

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