Dynamic Evaluation of Classical and Control-Aware Optimal Trajectory Planning in Robot Manipulators

• URL: http://arxiv.org/abs/2607.05544v2
• Date: Wed, 08 Jul 2026 20:42:22 GMT
• Title: Dynamic Evaluation of Classical and Control-Aware Optimal Trajectory Planning in Robot Manipulators
• Abstract summary: Trajectory planning influences tracking accuracy, actuator demand, and overall execution behavior in robotic manipulators.<n>Classical planners such as cubic, quintic, and trapezoidal profiles are widely used for their simplicity and smoothness.<n>This paper presents a control-aware optimal trajectory planning framework that explicitly incorporates manipulator dynamics and actuator effort.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory planning strongly influences tracking accuracy, actuator demand, and overall execution behavior in robotic manipulators. Classical planners such as cubic, quintic, and trapezoidal profiles are widely used for their simplicity and smoothness, yet they remain purely kinematic and ignore system dynamics and control effort during trajectory generation. As a result, nominally smooth trajectories can lead to inefficient nonlinear execution and increased corrective control action. This paper presents a control-aware optimal trajectory planning framework that explicitly incorporates manipulator dynamics and actuator effort within a finite-horizon formulation. A midpoint linearization strategy is introduced to improve approximation accuracy for large point-to-point motions. In contrast to prior comparisons, the proposed approach enables fair, isolated evaluation of trajectory generation effects under identical closed-loop nonlinear execution conditions. To this end, a unified evaluation framework is developed in which all planners are executed under identical nonlinear dynamics, controller structure, and actuator constraints. Simulations on a nonlinear simplified UR5 manipulator show that the proposed approach consistently reduces tracking error, corrective torque, and closed-loop execution cost compared to classical methods, achieving substantial reductions in actuator effort and execution cost across all evaluated scenarios, demonstrating that kinematic smoothness alone does not ensure dynamically efficient execution.

Related papers

• Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems [6.655837620295535]
  Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances.<n>We propose a unified framework that integrates differential flatness-based trajectory generation with predictive, constraint-aware control.<n>We validate the proposed framework through extensive simulations and real-world flight experiments, demonstrating improved tracking accuracy and robustness for complex trajectories.
  arXiv  Detail & Related papers  (2026-08-21T01:25:35Z)
• Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking [0.0]
  This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states.<n>The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories.
  arXiv  Detail & Related papers  (2026-08-11T06:27:45Z)
• Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation [57.9825059129472]
  We propose a planner-agnostic attack framework for tolerance-aware manipulation.<n>Our approach shifts the evaluation paradigm to task-level feasibility over goal regions.<n>Online, we formulate the attack as a budgeted maximum-coverage optimization.
  arXiv  Detail & Related papers  (2026-07-04T08:14:44Z)
• Saturation-Aware Robust Trajectory Optimization for Reusable Launch Vehicles via Differentiable Physics [9.011250637911248]
  High-angle-of-attack flip maneuver of reusable launch vehicles presents challenges for robust trajectory optimization.<n>This paper presents a differentiable physics framework for saturation-aware robust trajectory optimization.
  arXiv  Detail & Related papers  (2026-07-02T14:30:37Z)
• Invertible Neural Network Adapter for One-Step Flow Matching in Robot Manipulation [48.77928097573051]
  This paper presents an invertible neural network adapter for general robotic manipulation.<n>Built upon a flow-matching formulation, the proposed adapter constrains the action generation trajectory within an invertible latent space.<n>Experiments are conducted across a diverse set of simulation benchmarks and real-world robotic platforms to evaluate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2026-06-17T15:35:27Z)
• AURA: Asymptotically Optimal Uncertainty-Robust Replanning Algorithm for Kinodynamic Systems [1.137457877869062]
  Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning.<n>These planners are typically used offline, requiring execution to begin only after the trajectory has been computed.<n>In this work, these limitations were addressed within a unified framework, method, that improves both path quality and tracking performance during execution.
  arXiv  Detail & Related papers  (2026-05-26T21:20:09Z)
• Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets [0.6445605125467574]
  Motion planning for autonomous vehicles requires generating collision-free and dynamically feasible trajectories under real-time constraints.<n>In this paper, we introduce a new geometric control method to approximate nonlinear motion planning problems.<n>The method provides a structured approximation of nonlinear motion planning problems, capturing dominant geometric dynamic effects while preserving convexity in the continuous relaxation.
  arXiv  Detail & Related papers  (2026-05-13T23:29:54Z)
• AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models [60.04879435087352]
  Vision-language-action (VLA) policies generate actions within a single unified space.<n>We propose AnchorRefine, a hierarchical framework that factorizes VLA action modeling into trajectory anchor and residual refinement.<n> Experiments on LIBERO, CALVIN, and real-robot tasks demonstrate that AnchorRefine consistently improves both regression-based and diffusion-based VLA backbones.
  arXiv  Detail & Related papers  (2026-04-20T04:25:24Z)
• Curvature-Guided LoRA: Steering in the pretrained NTK subspace [60.35296431630704]
  We introduce the prediction alignment problem, aiming to match the predictor obtained via PEFT to that of full fine-tuning at the level of outputs.<n>We show that this objective naturally leads to a curvature-aware, second-order formulation, where optimal low-rank updates correspond to a Newton-like, curvature-whitened gradient.<n>Based on this insight, we propose Curvature-Guided LoRA (CG-LoRA), which selects and scales adaptation directions using local curvature information.
  arXiv  Detail & Related papers  (2026-03-31T14:46:39Z)
• Conservative Continuous-Time Treatment Optimization [9.553261527696067]
  We develop a conservative continuous-time control framework for treatment optimization from irregularly sampled patient trajectories.<n>Experiments on benchmark datasets show improved robustness and performance compared to non-conservative baselines.
  arXiv  Detail & Related papers  (2026-03-17T17:01:23Z)
• Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning [10.551597725130675]
  Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems.<n>This paper presents a self-supervised residual learning and trajectory optimization framework to address the aforementioned challenges.
  arXiv  Detail & Related papers  (2026-01-06T06:01:58Z)
• Dynamic Tube MPC: Learning Tube Dynamics with Massively Parallel Simulation for Robust Safety in Practice [28.37162791852146]
  Inevitable tracking error necessitates robustification of the nominal plan to ensure safety. In this work, we present a novel method leveraging massively parallel simulation to learn a dynamic tube representation. The resulting Dynamic MPC Tube is applied to the 3D hopping robot ARCHER.
  arXiv  Detail & Related papers  (2024-11-22T21:22:51Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion [73.37945453998134]
  Traditional approaches to quadruped control employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
  arXiv  Detail & Related papers  (2020-07-03T07:04:18Z)

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.