Adaptive control of the joints of a robotic arm on a drone for waste management

CONTEXT Terrestrial drones equipped with robotic arms offer an effective alternative for automated waste collection in public spaces. Precise control of the arm's joints is essential for grasping objects of varying shapes and sizes in unstructured environments. INTERNSHIP OBJECTIVES • Detection and classification of waste through artificial vision • Modeling and simulation of the robotic arm • Optimization of joint control for adaptive grasping TASKS REQUIRED • Bibliographic study on adaptive control of robotic arms • Development of the waste detection and classification model • Modeling of the robotic arm (Gazebo, PyBullet, ROS) • Adaptive joint control algorithm • Validation simulations in different scenarios DELIVERABLES • Waste detection/classification model • Simulation of the robotic arm in Gazebo/PyBullet • Validated adaptive control algorithm • Scientific report