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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
The Effectiveness of Kinematic Constraints on The Accurac...
Marwan Qaid Mohammed, Muhammad Fahmi Miskon, Sari Abdo Ali · 2021-05-21 · via cs.RO updates on arXiv.org

Many methods have been developed in trajectory planning in order to achieve smooth and accurate motion with considering the constraints of kinematics constraints such as angular position, velocity, acceleration, and jerk. The problem of using the combination of n-order polynomials is that there is no ideally match between the segments of trajectory path at the via point in terms of the number of kinematic constraints. It leads to generate undesirable trajectory path at the via point that connects between two segments of the trajectory path. In this paper, we aim to investigate the effect of increasing to higher order polynomial blends on the accuracy of the via points with considering different kinematics constraints. Based on that, the methodology that was used in this paper is based on the polynomial segment with the higher polynomial blend (PSPB). Three techniques implemented which are 4-3-4 PSPB, 5-4-5 PSPB, and 6-5-6 PSPB. Each technique implemented based on applying different kinematic constraints. The three techniques validated using a modeling design in SemiMechanics. According to the methodology, the result analyzed and discussed in terms of angular position, angular velocity, angular acceleration, and angular jerk based on Root Mean Square Error (RMSE) and Average Difference Error (ADE). The result shows that RMSE of angular position for 4-3-4 PSPB-1, 4-3-4 PSPB-2, 5-4-5 PSPB-1, 5-4-5 PSPB-2, 6-5-6 PSPB-1, and 6-5-6 PSPB-2 are 0.4574, 0.0172, 10.9089, 0.1242, 0.6153, and 0.3128 degrees respectively. At the same time, the ADE are 0.0455, 0.0017, 1.0855, 0.0124, 0.0612, and 0.0311 degrees respectively. Thus, the error is increased obviously when there is no ideal match at the via point in terms of a number of kinematic constraints.