Receding horizon control of autonomous aerial vehicles

By John S. Bellingham, Arthur G. Richards, Jonathan P. How. Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002

319 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a5d00bf04c0dcb71157ea4211cbad9a0e3c975de

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Rui Zhang
2. Qingqing Wu
3. Yong Zeng
4. J. Bagnell
5. Shuowen Zhang
6. Merouane Debbah
7. Robert Schober
8. Jie Xu

In Computer Vision and Pattern Recognition

1. Jitendra Malik
2. Luc Van Gool
3. Andrew Zisserman
4. Sergey Levine
5. J. Bagnell
6. Li Fei-Fei
7. Martial Hebert
8. Trevor Darrell

Citations in the corpus

• Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results (2012, background): “[9], [28], [33] study the problem of cooperative path planning and trajectory optimization.”
• A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance (2010, methodology): “[97] uses a cost function based on a visibility graph and [7] estimates the cost function by searching a graph representation of the environment with Dijkstra’s algorithm.”
• Deep Haptic Model Predictive Control for Robot-Assisted Dressing (2017, background): “Some examples include aerial control vehicles [11], [12] and robot locomotion [13], [14].”
• Stable task load balancing strategies for Cooperative control of networked autonomous air vehicles (2006, background): “Other work based on receding horizon approaches can be found in [7].”
• Learning monocular reactive UAV control in cluttered natural environments (2012, methodology): “When accounting for all failure types, the average distance flown per failure after all iterations of training was around 50m.”
• Minimum-Time Travel for a Vehicle with Acceleration Limits: Theoretical Analysis and Receding-Horizon Implementation (2008, methodology): “One way to achieve this, is for the trajectory optimization to be implemented in a receding horizon scheme rather than executed in one shot, from the start point to the end point [13, 14, 15].”
• Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, background): “For such scenarios, mixed-integer linear program (MILP) has been shown to be an effective approach [70,71,122,123].”
• Stable Cooperative Surveillance With Information Flow Constraints (2008, methodology): “Additional VRP-related work focusing on cooperative search and coordinated sequencing of tasks is in [3]–[6].”
• Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments (2017, background): “I, the RHP is a variant of Receding Horizon Control where people solve a optimal control problem over a fixed future time interval [18], [19].”
• Robust constrained model predictive control (2005, background): “Centralized MPC has been widely developed for constrained systems [3], with many results concerning stability [11, 48] and robustness [12, 20, 10], and has been applied to the co-operative control of multiple vehicles [10, 49, 50, 7].”