Cycloidal Trajectory Realization on Staircase based on Neural Network Temporal Quantized Lagrange Dynamics (NNTQLD) with Ant Colony Optimization for a 9-Link Bipedal Robot

• URL: http://arxiv.org/abs/2012.01417v3
• Date: Wed, 21 Jul 2021 14:56:47 GMT
• Title: Cycloidal Trajectory Realization on Staircase based on Neural Network Temporal Quantized Lagrange Dynamics (NNTQLD) with Ant Colony Optimization for a 9-Link Bipedal Robot
• Authors: Gaurav Bhardwaj, Utkarsh A. Mishra, N. Sukavanam and R. Balasubramanian
• Abstract summary: In this paper, a novel optimal technique for joint angles trajectory tracking control with energy optimization is proposed. For the task of climbing stairs by a 9-link biped model, a cycloid trajectory for swing phase is proposed in such a way that the cycloid variables depend on the staircase dimensions.
• Score: 1.7205106391379026
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, a novel optimal technique for joint angles trajectory tracking control with energy optimization for a biped robot with toe foot is proposed. For the task of climbing stairs by a 9-link biped model, a cycloid trajectory for swing phase is proposed in such a way that the cycloid variables depend on the staircase dimensions. Zero Moment Point(ZMP) criteria is taken for satisfying stability constraint. This paper mainly can be divided into 3 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs and Inverse Kinematics using an unsupervised artificial neural network with knot shifting procedure for jerk minimization. 2) Modeling Dynamics for Toe foot biped model using Lagrange Dynamics along with contact modeling using spring-damper system followed by developing Neural Network Temporal Quantized Lagrange Dynamics which takes inverse kinematics output from neural network as its inputs. 3) Using Ant Colony Optimization to tune PD (Proportional Derivative) controller parameters and torso angle with the objective to minimize joint space trajectory errors and total energy consumed. Three cases with variable staircase dimensions have been taken and a brief comparison is done to verify the effectiveness of our proposed work Generated patterns have been simulated in MATLAB .

Related papers

• Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories [28.701879490459675]
  We aim to learn an implicit motion field parameterized by a neural network to predict the movement of novel points within same domain. We exploit intrinsic regularization provided by SIREN, and modify the input layer to produce atemporally smooth motion field. Our experiments assess the model's performance in predicting unseen point trajectories and its application in temporal mesh alignment with deformation.
  arXiv  Detail & Related papers  (2024-06-05T21:02:10Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
  We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
  arXiv  Detail & Related papers  (2022-07-04T13:52:10Z)
• Manifold Interpolating Optimal-Transport Flows for Trajectory Inference [64.94020639760026]
  We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) MIOFlow learns, continuous population dynamics from static snapshot samples taken at sporadic timepoints. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
  arXiv  Detail & Related papers  (2022-06-29T22:19:03Z)
• Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions [65.84090965167535]
  We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network. This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.
  arXiv  Detail & Related papers  (2022-06-29T18:47:05Z)
• Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications [59.34642007625687]
  The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots. An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
  arXiv  Detail & Related papers  (2022-05-03T17:14:47Z)
• Adaptive Model Predictive Control of Wheeled Mobile Robots [0.0]
  This paper presents a control algorithm for guiding a two wheeled mobile robot with unknown inertia to a desired point and orientation. The two wheeled mobile robot is modeled as a knife edge or a skate with nonholonomic kinematic constraints. The efficacy of the algorithm is demonstrated through numerical simulations at the end of the paper.
  arXiv  Detail & Related papers  (2022-01-03T20:07:44Z)
• Planning Brachistochrone Hip Trajectory for a Toe-Foot Bipedal Robot going Downstairs [0.0]
  A novel efficient downstairs trajectory is proposed for a 9 link biped robot model with toe-foot. In most situations, while climbing downstairs, human hip also follow brachistochrone trajectory for a more responsive motion.
  arXiv  Detail & Related papers  (2020-12-02T11:23:23Z)
• Robust Quadrupedal Locomotion on Sloped Terrains: A Linear Policy Approach [3.752600874088677]
  We use a linear policy for realizing end-foot trajectories in the quadruped robot, Stoch $2$. In particular, the parameters of the end-foot trajectories are shaped via a linear feedback policy that takes the torso orientation and the terrain slope as inputs. The resulting walking is robust to terrain slope variations and external pushes.
  arXiv  Detail & Related papers  (2020-10-30T16:02:08Z)
• MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency [72.82534577726334]
  We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. Our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.
  arXiv  Detail & Related papers  (2020-06-22T08:50:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.