arXiv subjects

Abdel Zaro

Publications and source records attributed to Abdel Zaro.

3 recordsLinked to original sources

arXiv2025

Safe Gap-based Planning in Dynamic Settings

This chapter extends the family of perception-informed gap-based local planners to dynamic environments. Existing perception-informed local planners that operate in dynamic environments often rely on emergent or empirical robustness for collision avoidance as opposed to performing formal analysis of dynamic obstacles. This proposed planner, dynamic gap, explicitly addresses dynamic obstacles through several steps in the planning pipeline. First, polar regions of free space known as gaps are tracked and their dynamics are estimated in order to understand how the local environment evolves over time. Then, at planning time, gaps are propagated into the future through novel gap propagation algorithms to understand what regions are feasible for passage. Lastly, pursuit guidance theory is leveraged to generate local trajectories that are provably collision-free under ideal conditions. Additionally, obstacle-centric ungap processing is performed in situations where no gaps exist to robustify the overall planning framework. A set of gap-based planners are benchmarked against a series of classical and learned motion planners in dynamic environments, and dynamic gap is shown to outperform all other baselines in all environments. Furthermore, dynamic gap is deployed on a TurtleBot2 platform in several real-world experiments to validate collision avoidance behaviors.

cs.RO↗

arXiv2023

Collision Detection for Multi-Robot Motion Planning with Efficient Quad-Tree Update and Skipping

This paper presents a novel and efficient collision checking approach called Updating and Collision Check Skipping Quad-tree (USQ) for multi-robot motion planning. USQ extends the standard quad-tree data structure through a time-efficient update mechanism, which significantly reduces the total number of collision checks and the collision checking time. In addition, it handles transitions at the quad-tree quadrant boundaries based on worst-case trajectories of agents. These extensions make quad-trees suitable for efficient collision checking in multi-robot motion planning of large robot teams. We evaluate the efficiency of USQ in comparison with Regenerating Quad-tree (RQ) from scratch at each timestep and naive pairwise collision checking across a variety of randomized environments. The results indicate that USQ significantly reduces the number of collision checks and the collision checking time compared to other baselines for different numbers of robots and map sizes. In a 50-robot experiment, USQ accurately detected all collisions, outperforming RQ which has longer run-times and/or misses up to 25% of collisions.

cs.MA↗

arXiv2022

Dynamic Gap: Safe Gap-based Navigation in Dynamic Environments

This paper extends the family of gap-based local planners to unknown dynamic environments through generating provable collision-free properties for hierarchical navigation systems. Existing perception-informed local planners that operate in dynamic environments rely on emergent or empirical robustness for collision avoidance as opposed to performing formal analysis of dynamic obstacles. In addition to this, the obstacle tracking that is performed in these existent planners is often achieved with respect to a global inertial frame, subjecting such tracking estimates to transformation errors from odometry drift. The proposed local planner, dynamic gap, shifts the tracking paradigm to modeling how the free space, represented as gaps, evolves over time. Gap crossing and closing conditions are developed to aid in determining the feasibility of passage through gaps, and a breadth of simulation benchmarking is performed against other navigation planners in the literature where the proposed dynamic gap planner achieves the highest success rate out of all planners tested in all environments.

cs.RO↗