Convex optimization obstacle avoidance

  • A minimization problem is convex, if the objective function is convex, all inequality constraints of the type ��(��) ≤ 0 has g(x) convex and all equality constraints linear or affine.
    To check the convexity of a function you can use the second order partial derivatives.
Collision avoidance maneuver is formulated as a convex optimization problem. •. Collision probability calculation and nonconvex feasible region are 
The unavoidable presence of uncertainty associated with any physical system adds to the challenge. In this paper, a method is presented to determine the fuel- 

Can convex optimization be used to plan trajectories around obstacles?

Here we show that convex optimization can, in fact, be used to reliably plan trajectories around obstacles

Specifically, we consider planning problems with collision-avoidance constraints, as well as cost penalties and hard constraints on the shape, the duration, and the velocity of the trajectory

Can convex optimization reformulate nondifferentiable collision avoidance constraints into smooth differentiable constraints?

Abstract: This article presents a novel method for exactly reformulating nondifferentiable collision avoidance constraints into smooth, differentiable constraints using strong duality of convex optimization

We focus on a controlled object whose goal is to avoid obstacles while moving in an n-dimensional space

Is sequential convex programming a good solution for complex obstacle avoidance constraints?

Still, the current sequential convex programming (SCP) method suffers from a low convergence rate and poor real-time performance when dealing with complex obstacle avoidance constraints (OACs)

Given the above challenges, this work combines homotopy and neural network techniques with SCP to propose an innovative algorithm


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