diff --git a/.vscode/tasks.json b/.vscode/tasks.json
index 24f8f7b..adebb0e 100644
--- a/.vscode/tasks.json
+++ b/.vscode/tasks.json
@@ -121,8 +121,30 @@
}
},
{
- "label": "Run Jackal (REAL)",
- "command": "source devel/setup.bash && roslaunch mpc_planner_jackal ros1_jackal.launch",
+ "label": "Jackal: Generate solver and build",
+ "detail": "Generate the solver and build mpc_planner_jackal",
+ "type": "shell",
+ "command": "./build.sh jackal true",
+ "group": {
+ "kind": "build",
+ "isDefault": true
+ },
+ "problemMatcher": "$gcc"
+ },
+ {
+ "label": "Jackal: Build",
+ "detail": "Build the ROS Package mpc_planner_jackal",
+ "type": "shell",
+ "command": "./build.sh jackal",
+ "group": {
+ "kind": "build",
+ "isDefault": true
+ },
+ "problemMatcher": "$gcc"
+ },
+ {
+ "label": "Jackal: Run Real-World Jackal",
+ "command": "source devel/setup.bash && source fix_console.sh && roslaunch mpc_planner_jackal ros1_jackal.launch",
"type": "shell",
"group": {
"kind": "build",
diff --git a/README.md b/README.md
index 7b12e47..1b36bed 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,9 @@
# MPC Planner Workspace (VSCode Docker)
-This repository provides a complete VSCode docker environment for running the `mpc_planner` (see https://github.com/tud-amr/mpc_planner for the planner documentation). The code is associated with the following publications:
+This repository provides a complete VSCode docker environment for running the `mpc_planner` (see https://github.com/tud-amr/mpc_planner for the planner documentation).
+
+The code is associated with the following publications:
**Journal Paper:** O. de Groot, L. Ferranti, D. Gavrila, and J. Alonso-Mora, *Topology-Driven Parallel Trajectory Optimization in Dynamic Environments.* IEEE Transactions on Robotics 2024. Preprint: http://arxiv.org/abs/2401.06021
@@ -13,7 +15,7 @@ This repository provides a complete VSCode docker environment for running the `m
Jackal Simulator | ROS Navigation Stack |
| ------------- | ------------- |
-|
| 
|
+| | 
|
---
@@ -71,7 +73,7 @@ You will need to set up both a regular and floating solver.
**Regular Solver:** Go to my.embotech.com, log in to your account. Assign a regular license to your computer. Then download the client to `~/forces_pro_client/` **outside of the container**. If you have the solver in a different location, add its path to `PYTHONPATH`.
**Floating Solver:**
-Go to my.embotech.com, log in to your account. Click on a license -> Download `Floating Licenses Proxy Standalone (Linux 64-bit) - FORCES PRO v5.1.0 onwards` -> unzip. In the downloaded folder, `chmod +x forcespro_floating_licenses_proxy`. Then to start the solver proxy (necessary to run it), execute:
+Go to my.embotech.com, log in to your account. Click on a license -> Download `Floating Licenses Proxy Standalone (Linux 64-bit) - FORCES PRO v5.1.0 onwards` **outside of the container** -> unzip. In the downloaded folder, `chmod +x forcespro_floating_licenses_proxy`. Then to start the solver proxy (necessary to run it), execute:
`./forcespro_floating_licenses_proxy`.
To use the floating license, set `solver_settings/floating_license` in `mpc_planner_/config/settings.yaml` to `true`.
@@ -123,25 +125,22 @@ To change the detected obstacles, see `ros1_jackal.launch`.
### Jackal Simulator
Task: `JackalSimulator: Run Simulator`
-The following example features
-
-- Topology-driven MPC (T-MPC++) for dynamic obstacle avoidance (using ellipsoidal obstacles) [1]
-- Model Predictive Contouring Control (MPCC)
+The following example features **Topology-driven MPC (T-MPC++) [1]** with a reference tracking cost and dynamic obstacle avoidance (*using ellipsoidal obstacles*)
-Applied to the Clearpath Jackal UGV (`mpc_planner_jackalsimulator`) with dynamic obstacles.
+Applied to the Clearpath Jackal mobile robot (`mpc_planner_jackalsimulator`) in an environment with pedestrians.
-
+
### ROS Navigation stack
Task: `ROSNavigation: Run Simulator`
Navigation with static and dynamic obstacles. This example features
-- Topology-Driven MPC for dynamic obstacle avoidance [1]
-- Curvature-Aware MPC (https://ieeexplore.ieee.org/document/10161177)
-- Decomp Util for static obstacle avoidance (https://arxiv.org/pdf/2406.11506)
+- **Topology-Driven MPC** for dynamic obstacle avoidance [1]
+- **Curvature-Aware MPC** for reference tracking (https://ieeexplore.ieee.org/document/10161177)
+- **Decomp Util** for static obstacle avoidance (https://arxiv.org/pdf/2406.11506)
-
+
## License
@@ -150,7 +149,7 @@ This project is licensed under the Apache 2.0 license - see the LICENSE file for
## Citing
This repository was developed at the Cognitive Robotics group of Delft University of Technology by [Oscar de Groot](https://github.com/oscardegroot) in partial collaboration with [Dennis Benders](https://github.com/dbenders1) and [Thijs Niesten](https://github.com/thijs83) and under supervision of Dr. Laura Ferranti, Dr. Javier Alonso-Mora and Prof. Dariu Gavrila.
-If you found this repository useful, please cite the following paper:
+If you found this repository useful, please cite our paper:
- [1] **Topology-Driven Model Predictive Control (T-MPC)** O. de Groot, L. Ferranti, D. Gavrila, and J. Alonso-Mora, “Topology-Driven Parallel Trajectory Optimization in Dynamic Environments.” arXiv, Jan. 11, 2024. [Online]. Available: http://arxiv.org/abs/2401.06021