Tricriteria Optimization-Coordination Motion of Dual-Redundant-Robot Manipulators for Complex Path Planning

Zhang, Zhijun; Yu-jun, Lin; Li, Shuai; Li, Yuanqing; Yu, Zhuliang; Luo, Yu

doi:10.1109/tcst.2017.2709276

Cited by 61 publications

(29 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, the development of robotics has been directly or indirectly affected by human's experiences and behaviors. [23][24][25] However, these methods [24][25][26][27] mainly focus on the motion modeling of robot's body parts (e.g. arms) and the interactive applications between robots and humans.…”

Section: Introductionmentioning

confidence: 99%

Human experience–inspired path planning for robots

Gong

Xie

Liu

2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

In this article, we present a human experience-inspired path planning algorithm for service robots. In addition to considering the path distance and smoothness, we emphasize the safety of robot navigation. Specifically, we build a speed field in accordance with several human driving experiences, like slowing down or detouring at a narrow aisle, and keeping a safe distance to the obstacles. Based on this speed field, the path curvatures, path distance, and steering speed are all integrated to form an energy function, which can be efficiently solved by the A* algorithm to seek the optimal path by resorting to an admissible heuristic function estimated from the energy function. Moreover, a simple yet effective fast path smoothing algorithm is proposed so as to ease the robots steering. Several examples are presented, demonstrating the effectiveness of our human experience-inspired path planning method.

show abstract

Section: Introductionmentioning

confidence: 99%

Human experience–inspired path planning for robots

Gong

Xie

Liu

2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…In the Cartesian space, the generated trajectory can meet the geometric constraints directly, but it is quite complicated because of inverse kinematics [5]. Therefore, most of the desired trajectories are planned in the joint space [6][7][8][9][10][11][12][13][14][15]. In [6], a time-minimum algorithm is used to generate the path for m-joint mechanical manipulators based on the interpolated technique.…”

Section: Introductionmentioning

confidence: 99%

“…The generated trajectory is global time-minimum; however, this is an unconstraint algorithm. The hybrid algorithms that combine two or more optimal requirements are presented in [7][8][9][10][11][12][13][14][15][16][17]. In these schemes, besides the time-minimum, some additional characteristics are considered, such as energy optimization and jerk optimization.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Path Planning of Excavator Arm

Vu¹,

Bui²,

Tran³

2018

IJMERR

View full text Add to dashboard Cite

This paper presents a solution in path planning for a robotic arm based on the artificial neural network (ANN) architecture, particularly a Static (Feedforward) Neural Network (SNN). The inputs of the network are the sample sets that are obtained from some specific requirements of the desired trajectory. After training, the outputs of the network are the smooth curves that will be used as the reference trajectory for the joints of the excavator arm. The capabilities of the designed neural network in solving the path planning problems are clearly demonstrated through a simulation conducted with a complex trajectory for the excavator. 

show abstract

“…[25,26] Due to the extensive and significant applications of neu-ral networks, the development and investigation of neural networks have become common and heated topics for the researchers in biology, mathematics, physics, and computer science. [27][28][29][30][31][32][33][34][35][36][37][38][39][40] According to different standards of classification, neural networks can be divided into different categories. From the point of topology, neural networks can be divided into feedforward neural networks (FNNs) [41][42][43][44] and recurrent neural networks (RNNs).…”

Section: Introductionmentioning

confidence: 99%

A Review on Neural Dynamics for Robot Autonomy

Chen

2018

IJRC

Self Cite

View full text Add to dashboard Cite

Exploiting neural networks to solve control problems of robots is becoming commonly and effectively in academia and engineering. Due to the remarkable features like distributed storage, parallelism, easy implementation by hardware, adaptive self-learning capability, and free of off-line training, the solutions of neural networks break the bottlenecks of serial-processing strategies and methods, and serve as significant alternatives for robotic engineers and researchers. Especially, various types and branches of recurrent neural networks (RNNs) have been sequentially developed since the seminal works by Hopfield and Tank. Successively, many classes and branches of RNNs such as primal-dual neural networks (PDNNs), zeroing neural networks (ZNNs) and gradient neural networks (GNNs) are proposed, investigated, developed and applied to the robot autonomy. The objective of this paper is to present a comprehensive review of the research on neural networks (especially RNNs) for control problems solving of different kinds of robots. Specifically, the state-of-the-art research of RNNs, PDNNs, ZNNs and GNNs in different robot control problems solving are detailedly revisited and reported. The readers can readily find many effective and valuable solutions on the basis of neural networks for the robot autonomy in this paper.

show abstract

Tricriteria Optimization-Coordination Motion of Dual-Redundant-Robot Manipulators for Complex Path Planning

Cited by 61 publications

References 43 publications

Human experience–inspired path planning for robots

Human experience–inspired path planning for robots

Artificial Neural Network Based Path Planning of Excavator Arm

A Review on Neural Dynamics for Robot Autonomy

Contact Info

Product

Resources

About