Merge branch 'master' of https://git.codeocean.com/capsule-2367434

Author: Scheffe

metadata/metadata.yml

@@ -1,27 +1,16 @@
-metadata_version: 2
-name: Sequential Convex Programming Methods for Real-time Optimal Trajectory Planning in Autonomous Vehicle Racing
-description: We present a real-time-capable Model Predictive Controller (MPC)
-based on a non-linear single-track vehicle model and Pacejka’s
-magic tire formula for autonomous racing applications. After 
-formulating the general non-convex trajectory optimization 
-problem, the model is linearized around estimated operating 
-points and the constraints are convexified using the Sequential
-Convex Programming (SCP) method. We use two different methods
-to convexify the non-convex track constraints, namely Sequential
-Linearization (SL) and Sequential Convex Restriction (SCR).
-SL, a method of relaxing the constraints, was introduced in our
-previous paper. SCR, a method of restricting the constraints,
-is introduced in this paper. We show the application of SCR to
-autonomous racing and prove that it does not interfere with
-recursive feasibility. We compare the predicted trajectory
-quality of the non-linear single-track model with the linear
-double integrator model from our previous paper. The MPC
-performance is evaluated on a scaled version of the Hockenheimring
-racing track. We show that an MPC with SCR yields faster lap times
-than an MPC with SL - for race starts as well as flying laps -
-while still being real-time capable. A video showing the results
-is available at \url{https://youtu.be/21iETsolCNQ}.
-
+metadata_version: 1
+name: Sequential Convex Programming Methods for Real-time Optimal Trajectory Planning
+  in Autonomous Vehicle Racing
+description: We present a real-time-capable Model Predictive Controller (MPC) based
+  on a single-track vehicle model and Pacejka’s magic tire formula for autonomous
+  racing applications. After constructing a non-convex trajectory optimization problem,
+  it is convexified using the Sequential Convex Programming (SCP) method. We use two
+  different methods for track discretization, namely Sequential Linearization (SL)
+  and Sequential Convex Restriction (SCR). SL was already introduced in our previous
+  paper. SCR is a new addition, introduced in detail and its recursive feasibility
+  proven. We show that a controller with SCR yields faster lap times - for race starts
+  as well as flying laps - while being real-time capable. A video showing the results
+  is available at \url{https://youtu.be/21iETsolCNQ}.
 tags:
 - autonomous-vehicle
 - autonomous-vehicle-racing
@@ -44,5 +33,5 @@ authors:
   affiliations:
   - name: Chair of Embedded Software, RWTH Aachen University, Germany
 corresponding_contributor:
-  name: Theodor Mario Henneken
-  email: theodor.henneken@rwth-aachen.de
+  name: Patrick Scheffe
+  email: scheffe@embedded.rwth-aachen.de