Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities

• URL: http://arxiv.org/abs/2412.00538v1
• Date: Sat, 30 Nov 2024 17:09:18 GMT
• Title: Prognostic Framework for Robotic Manipulators Operating Under Dynamic Task Severities
• Authors: Ayush Mohanty, Jason Dekarske, Stephen K. Robinson, Sanjay Joshi, Nagi Gebraeel,
• Abstract summary: We present a prognostic modeling framework that predicts a robotic manipulator's Remaining Useful Life (RUL)<n>Our findings show that robots in both fleets experience shorter RUL when handling a higher proportion of high-severity tasks.
• Score: 0.6058427379240697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robotic manipulators are critical in many applications but are known to degrade over time. This degradation is influenced by the nature of the tasks performed by the robot. Tasks with higher severity, such as handling heavy payloads, can accelerate the degradation process. One way this degradation is reflected is in the position accuracy of the robot's end-effector. In this paper, we present a prognostic modeling framework that predicts a robotic manipulator's Remaining Useful Life (RUL) while accounting for the effects of task severity. Our framework represents the robot's position accuracy as a Brownian motion process with a random drift parameter that is influenced by task severity. The dynamic nature of task severity is modeled using a continuous-time Markov chain (CTMC). To evaluate RUL, we discuss two approaches -- (1) a novel closed-form expression for Remaining Lifetime Distribution (RLD), and (2) Monte Carlo simulations, commonly used in prognostics literature. Theoretical results establish the equivalence between these RUL computation approaches. We validate our framework through experiments using two distinct physics-based simulators for planar and spatial robot fleets. Our findings show that robots in both fleets experience shorter RUL when handling a higher proportion of high-severity tasks.

Related papers

• Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation [88.83749146867665]
  Existing approaches learn a policy to predict a distant next-best end-effector pose. They then compute the corresponding joint rotation angles for motion using inverse kinematics. We propose Kinematics enhanced Spatial-TemporAl gRaph diffuser.
  arXiv  Detail & Related papers  (2025-03-13T17:48:35Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce kinematically feasible trajectories. The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model [13.110235244912474]
  Redundant manipulators offer enhanced kinematic performance and versatility. Motion planning for these manipulators is challenging due to increased DOFs and complex, dynamic environments. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators.
  arXiv  Detail & Related papers  (2024-12-27T07:34:54Z)
• Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning [62.3886343725955]
  We introduce Coarse-to-fine Q-Network with Action Sequence (CQN-AS), a novel value-based reinforcement learning algorithm. We study our algorithm on 53 robotic tasks with sparse and dense rewards, as well as with and without demonstrations.
  arXiv  Detail & Related papers  (2024-11-19T01:23:52Z)
• SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation [62.58480650443393]
  Segment Anything (SAM) is a vision-foundation model for generalizable scene understanding and sequence imitation. We develop a novel multi-channel heatmap that enables the prediction of the action sequence in a single pass.
  arXiv  Detail & Related papers  (2024-05-30T00:32:51Z)
• A Causal Bayesian Network and Probabilistic Programming Based Reasoning Framework for Robot Manipulation Under Uncertainty [4.087774077861305]
  We propose a flexible and generalisable physics-informed causal Bayesian network (CBN) based framework for a robot. We demonstrate our framework's ability to: (1) predict manipulation outcomes with high accuracy (Pred Acc: 88.6%); and, (2) perform greedy next-best action selection with 94.2% task success rate.
  arXiv  Detail & Related papers  (2024-03-21T15:36:26Z)
• ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation [58.615616224739654]
  Conventional robotic manipulation methods usually learn semantic representation of the observation for prediction. We propose a dynamic Gaussian Splatting method named ManiGaussian for multi-temporal robotic manipulation. Our framework can outperform the state-of-the-art methods by 13.1% in average success rate.
  arXiv  Detail & Related papers  (2024-03-13T08:06:41Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Robotic Arm Manipulation to Perform Rock Skipping in Simulation [0.0]
  This project aims to bring rock skipping into a robotic setting, utilizing the lessons we learned in Robotic Manipulation. Specifically, this project implements a system consisting of a robotic arm and dynamic environment to perform rock skipping in simulation.
  arXiv  Detail & Related papers  (2023-10-23T01:55:43Z)
• Obstacle Avoidance for Robotic Manipulator in Joint Space via Improved Proximal Policy Optimization [6.067589886362815]
  In this paper, we train a deep neural network via an improved Proximal Policy Optimization (PPO) algorithm to map from task space to joint space for a 6-DoF manipulator. Since training such a task in real-robot is time-consuming and strenuous, we develop a simulation environment to train the model. Experimental results showed that using our method, the robot was capable of tracking a single target or reaching multiple targets in unstructured environments.
  arXiv  Detail & Related papers  (2022-10-03T10:21:57Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Learning from Sparse Demonstrations [17.24236148404065]
  The paper develops the method of Continuous Pontryagin Differentiable Programming (Continuous PDP), which enables a robot learn an objective function from a few demonstrated examples. The method finds an objective function and a time-warping function such that the robot's resulting trajectorys sequentially follow the trajectorys with minimal discrepancy loss. The method is first evaluated on a simulated robot arm and then applied to a 6-DoF quadrotor to learn an objective function for motion planning in unmodeled environments.
  arXiv  Detail & Related papers  (2020-08-05T14:25:39Z)
• Predicting Sample Collision with Neural Networks [5.713670854553366]
  We present a framework to address the cost of expensive primitive operations in sampling-based motion planning. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces.
  arXiv  Detail & Related papers  (2020-06-30T14:56:14Z)

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.