The admissible gap (AG) method for reactive collision avoidance

Mujahed, Muhannad; Mertsching, Bärbel

doi:10.1109/icra.2017.8071093

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…The motion-related metrics include the distance D taken to achieve the final goal and the average trajectory curvature change C chg as in [30], representing the path's oscillations,…”

Section: B Performance Metrics and Simulation Scenariosmentioning

confidence: 99%

GP-Frontier for Local Mapless Navigation

Ali

Liu

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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We propose a new method for autonomous navigation in uneven terrains by utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP local perception model is trained on local ranging observation (pointcloud) to learn the terrain elevation profile and extract the feasible navigation subgoals around the robot. Subsequently, a cost function, which prioritizes the safety of the robot in terms of keeping the robot's roll and pitch angles bounded within a specified range, is used to select a safety-aware subgoal that leads the robot to its final destination. The algorithm is designed to run in real-time and is intensively evaluated in simulation and realworld experiments. The results compellingly demonstrate that our proposed algorithm consistently navigates uneven terrains with high efficiency and surpasses the performance of other planners. The code can be found here: https://rb.gy/3ov2r8.

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“…The motion-related metrics include the distance D taken to achieve the final goal and the average trajectory curvature change C chg as in [30], representing the path's oscillations,…”

Section: B Performance Metrics and Simulation Scenariosmentioning

confidence: 99%

GP-Frontier for Local Mapless Navigation

Ali

Liu

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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“…In [42], a solution was presented for real-time obstacle avoidance, called Admissible Gap (AG). In the proposed solution, a gap is admissible if it is possible to find a motion command that, once executed, allows the robot to safely pass through the gap, respecting its shape and movement constraints.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation of Wheelchairs in Indoor Environments using Deep Reinforcement Learning and Computer Vision

Afonso,

Ferreira

2023

Preprint

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We introduce an innovative approach to autonomous navigation for motorized wheelchairs in shared indoor spaces with human presence, combining Deep Reinforcement Learning and Computer Vision. The central aim of this study is to enhance the well-being of individuals with disabilities who rely on such assistance for their mobility needs. Our methodology merges the Deep Deterministic Policy Gradient (DDPG) algorithm with advanced computer vision techniques, empowering motorized wheelchairs to navigate through environments that encompass both stationary and moving people. Comparative tests were conducted between the DDPG and Deep Q-Network (DQN) algorithms, spanning four distinct stages. Each stage represented two different scenarios from the training environment, characterized by complexity levels exceeding those to which the robot had been trained. The DDPG algorithm showcased its superior efficiency and stability compared to DQN. Across all analyzed stages, DDPG exhibited higher average success rates: 98\% (Stage 01), 89\% (Stage 02), 86\% (Stage 03), and 86\% (Stage 04), demonstrating exceptional generalization capabilities and consistently outperforming the training environment in diverse settings. In contrast, DQN struggled to avoid collisions, resulting in significantly lower average success rates: 3\% (Stage 02), 14\% (Stage 03), and 29\% (Stage 04). Our findings underscore the promising potential of our proposed solution, thereby contributing to the progress of research in this particular domain.

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“…Gap-based processing aimed at detecting passable free-space is compatible with and improves the synthesis of local optimal paths. While there is no widely agreed-upon representation-or processing approach-for gaps, gap-based methods typically reduce 1D laser scan measurements to a set of "gaps" comprised of beginning and terminating points that represent collisionfree regions in the observable space [24]- [28]. A gap-based method would then generate reactive motion commands towards a selected gap, which gives favorable performance in reasonably sparse and structured environments.…”

Section: A Related Work and Research Contextmentioning

confidence: 99%

Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation

Feng

Vela

2021

IEEE Robot. Autom. Lett.

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This paper considers the integration of gap-based local navigation methods with artificial potential field (APF) methods to derive a local planning module for hierarchical navigation systems that has provable collision-free properties. Given that APF theory applies to idealized robot models, the provable properties are lost when applied to more realistic models. We describe a set of algorithm modifications that correct for these errors and enhance robustness to non-ideal models. Central to the construction of the local planner is the use of sensory-derived local free-space models that detect gaps and use them for the synthesis of the APF. Modifications are given for a nonholonomic robot model. Integration of the local planner, called potential gap, into a hierarchical navigation system provides the local goals and trajectories needed for collision-free navigation through unknown environments. Monte Carlo experiments in benchmark worlds confirm the asserted safety and robustness properties by testing under various robot models.

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The admissible gap (AG) method for reactive collision avoidance

Cited by 12 publications

References 29 publications

GP-Frontier for Local Mapless Navigation

GP-Frontier for Local Mapless Navigation

Autonomous Navigation of Wheelchairs in Indoor Environments using Deep Reinforcement Learning and Computer Vision

Potential Gap: A Gap-Informed Reactive Policy for Safe Hierarchical Navigation

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