Obstacle avoidance using Raycasting and Riemannian Motion Policies at kHz rates for MAVs
Author:
Affiliation:
1. ETH,Autonomous Systems Lab,Zürich,Switzerland
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10161365.pdf?arnumber=10161365
Reference18 articles.
1. CHOMP: Covariant Hamiltonian optimization for motion planning
2. Sampling-based algorithms for optimal motion planning
3. Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles
4. Fast, autonomous flight in GPS-denied and cluttered environments
5. Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning
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1. nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
2. Waverider: Leveraging Hierarchical, Multi-Resolution Maps for Efficient and Reactive Obstacle Avoidance;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
3. A Novel Obstacle-Avoidance Solution With Non-Iterative Neural Controller for Joint-Constrained Redundant Manipulators;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01
4. Design and Implementation of Dynamic Motion Policy for Multi-Sensor Omnidirectional Mobile Arm Robot System in Human-Robot Coexisted Healthcare Workplace;2023 International Conference on Advanced Robotics and Intelligent Systems (ARIS);2023-08-30
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