40 test main branch ruleset (testing Issue #40)

.github/CODEOWNERS

@@ -1 +1,2 @@
 * @Sharwin24
+potential_fields/src/ros/pfield_manager.cpp @dbarsoum

potential_fields/README.md

@@ -25,4 +25,8 @@ Topics and services
 Notes on robot geometry
 
 - You can supply obstacles directly via `pfield/obstacles` from any node.
-- Alternatively, provide a URDF path to the node via `urdf_file_path`; the PF library will initialize `PFKinematics`, estimate robot extent (for influence distance), and can update geometry-derived obstacles as planning progresses.
+- Alternatively, provide a URDF path to the node via `urdf_file_path`; the PF library will initialize `PFKinematics`, estimate robot extent (for influence distance), and can update geometry-derived obstacles as planning progresses.
+
+---
+
+Copyright Notice [Sharwin Patil](https://www.sharwinpatil.info/) 2025

potential_fields_library/README.md

@@ -108,4 +108,8 @@ int main() {
 
 *   **Coordinate Frames:** Ensure all poses (start, goal, obstacles) are defined in the same reference frame (typically "world" or "base_link") before passing them to the library. In the ROS example, a fixed frame parameter is used to signify this frame.
 *   **Parameter Tuning:** The behavior of the potential field is highly dependent on gains (`attractiveGain`, `repulsiveGain`) and the influence distance (`influenceDistance`). These may need tuning for specific robot configurations and environments.
-*   **Local Minima:** Like all potential field methods, this approach is susceptible to local minima. The library includes a "planning-only" opposing force removal technique to help mitigate this, but it is not a global planner.
+*   **Local Minima:** Like all potential field methods, this approach is susceptible to local minima. The library includes a "planning-only" opposing force removal technique to help mitigate this, but it is not a global planner.
+
+---
+
+Copyright Notice [Sharwin Patil](https://www.sharwinpatil.info/) 2025