Dynamically feasible trajectory planning for Anguilliform-inspired robots in the presence of steady ambient flow

Raj, Aditi; Thakur, Ajay Singh

doi:10.1016/j.robot.2019.05.001

Cited by 9 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The articulated robots have been extensively applied for several industrial applications, such as welding [1], painting [2], and assembling [3]. It is important to note, the articulated robots are not limited only to industrial robots but also to other robots that contain multiple joints, including the bio-inspired robot [4], surgery robot [5], humanoid robot [6], and so on.…”

Section: Introductionmentioning

confidence: 99%

An Improved Performance of the Designed Robotic Motion Control for NAO Robot Arms Using Hybrid Neural Network-Jacobian

2023

IJIES

View full text Add to dashboard Cite

Inverse kinematics plays a significant role in the robotic motion control. The flexible way to formulate the inverse kinematics can be achieved by using a neural network. The drawback of the neural network-based inverse kinematics is that it has no feedback mechanism to compensate for the remaining error. To improve its performance, further development should be conducted. In the design of the robotic motion control for the NAO robot arms, this paper proposed a new approach that combines the neural network-based inverse kinematics with the Jacobian. This combination yields a closed-loop control system. This control system utilizes the neural network as the feedforward controller and the Jacobian as the feedback controller. In particular, the neural network-based inverse kinematics has a function to estimate a set of required joint angles for the joint actuators, and the Jacobian function is to compensate for the remaining error of the neural network-based inverse kinematics in the estimation of joint angles. By using this proposed approach, we obtained more accurate joint angles for controlling the joint actuators. The comparison result showed that the averaged MSE for the particle swarm optimization (PSO) was 3.47 x 10 -3 rad, 1.19 x 10 -3 rad for the neural network, and 3.72 x 10 -5 rad for the proposed approach. The performance comparison result indicated that our proposed approach has a lower averaged MSE than the other ones. Accordingly, the result of this research confirmed that our proposed approach can provide more accurate joint angles for controlling the joint actuators such that the robot's end-effector can be driven along the desired path in the cartesian space.

show abstract

Section: Introductionmentioning

confidence: 99%

An Improved Performance of the Designed Robotic Motion Control for NAO Robot Arms Using Hybrid Neural Network-Jacobian

2023

IJIES

View full text Add to dashboard Cite

show abstract

“…Therefore, the study of trajectory planning for autonomous driving can refer to current studies on the trajectory planning of intelligent robots. 1 –9 The problem of trajectory planning for autonomous driving can be regarded as a time–space curve optimization problem in a two-dimensional plane, and solving the optimization problem means solving the problem of trajectory planning for autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment

Zhang

Xia

Chen

2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Safe lane changing of the dynamic industrial park and port scenarios with autonomous trucks involves the problem of planning an effective and smooth trajectory. To solve this problem, we propose a new trajectory planning method based on the Dijkstra algorithm, which combines the Dijkstra algorithm with a cost function model and the Bezier curve. The cost function model is established to filter target trajectories to obtain the optimal target trajectory. The third-order Bezier curve is employed to smooth the optimal target trajectory. Road experiments are carried out with an autonomous truck to illustrate the effectiveness and smoothness of the proposed method. Compared with other conventional methods, the improved method can generate a more effective and smoother trajectory in the truck lane change.

show abstract

Hydrodynamic Parameter Estimation for an Anguilliform-inspired Robot

Raj

Thakur

2020

J Intell Robot Syst

View full text Add to dashboard Cite

Dynamically feasible trajectory planning for Anguilliform-inspired robots in the presence of steady ambient flow

Cited by 9 publications

References 21 publications

An Improved Performance of the Designed Robotic Motion Control for NAO Robot Arms Using Hybrid Neural Network-Jacobian

An Improved Performance of the Designed Robotic Motion Control for NAO Robot Arms Using Hybrid Neural Network-Jacobian

An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment

Hydrodynamic Parameter Estimation for an Anguilliform-inspired Robot

Contact Info

Product

Resources

About