Aircraft trajectory planning with collision avoidance using mixed integer linear programming

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

823 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/144ff5ab85495ccfc2e203b41d020882437c6cd3

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

In Computer Vision and Pattern Recognition

Citations in the corpus

UAV assistance paradigm: State-of-the-art in applications and challenges (2020, methodology): “For example, in the works in [67], [68], the energy consumption was modeled based on the acceleration vector and control force, whereas in [69], an empirical energy consumption model was applied for a UAV path planning application that is deployed for aerial imaging where several measurements were…”
Cooperative Path Planning of Unmanned Aerial Vehicles (2010, methodology): “(2001) and Richards and How (2002). MILP is an application of the operations research method, called linear programming with integer or binary constraints.”
Survey of Robot 3D Path Planning Algorithms (2016, methodology): “Mathematic model based algorithms High time complexity [58–62] Containing almost all the information to generate an optimal path indeed No analytic solutions [63]”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, background): “It is worth noting that as a related line of research, UAV path planning for dedicated missions without communication performance consideration has been studied in [62]–[65], where the UAV paths were designed such that the UAVs can reach their pre-determined destinations while avoiding collisions wi”
Robust constrained model predictive control (2005, methodology): “Mixed-Integer Linear Programming (MILP) path-planning [56, 38] is combined with DMPC to provide a decentralized algorithm for cooperative guidance of UAVs.”
A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance (2010, methodology): “[112] describes this approach and Richards and How [91] covers both single and multiple-vehicle planning in two-dimensional cases.”
Cooperative Intersection Management: A Survey (2016, background): “• Vehicle behaviors: It is assumed that vehicles are able to follow instructions such as the driving plan from the intersection controller or mutually agreed by vehicles.”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, methodology): “In [70] and [71], the UAV energy (fuel) cost was modelled as the L 1 norm of the control force or acceleration vector, whereas in [72], it was modelled to be proportional to the square of the UAV speed.”
Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach (2012, methodology): “A method for aircraft trajectory planning with avoidance constraints that also exploits simplified dynamics and constraints can be found in [5].”
Temporal logic motion planning for dynamic robots (2009, methodology): “techniques like mixed integer linear programming [ 4 ].”
Chance-Constrained Optimal Path Planning With Obstacles (2011, background): “RELATED WORK A large body of work exists on the topic of path planning with deterministic system models, including [1], [2], [3], [4], [19].”