6 research outputs found
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Fast feedback control and safety guarantees are essential in modern robotics.
We present an approach that achieves both by combining novel robust model
predictive control (MPC) with function approximation via (deep) neural networks
(NNs). The result is a new approach for complex tasks with nonlinear,
uncertain, and constrained dynamics as are common in robotics. Specifically, we
leverage recent results in MPC research to propose a new robust setpoint
tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic
setpoint while guaranteeing stability and constraint satisfaction. The
presented robust MPC scheme constitutes a one-layer approach that unifies the
often separated planning and control layers, by directly computing the control
command based on a reference and possibly obstacle positions. As a separate
contribution, we show how the computation time of the MPC can be drastically
reduced by approximating the MPC law with a NN controller. The NN is trained
and validated from offline samples of the MPC, yielding statistical guarantees,
and used in lieu thereof at run time. Our experiments on a state-of-the-art
robot manipulator are the first to show that both the proposed robust and
approximate MPC schemes scale to real-world robotic systems.Comment: 8 pages, 4 figures
Nonlinear Model Predictive Control-based Collision Avoidance for Mobile Robot
This work proposes an efficient and safe single-layer Nonlinear Model Predictive Control (NMPC) system based on LiDAR to solve the problem of autonomous navigation in cluttered environments with previously unidentified static and dynamic obstacles of any shape. Initially, LiDAR sensor data is collected. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, is used to cluster the (Lidar) points that belong to each obstacle together. Moreover, a Minimum Euclidean Distance (MED) between the robot and each obstacle with the aid of a safety margin is utilized to implement safety-critical obstacle avoidance rather than existing methods in the literature that depend on enclosing the obstacles with a circle or minimum bounding ellipse. After that, to impose avoidance constraints with feasibility guarantees and without compromising stability, an NMPC for set-point stabilization is taken into consideration with a design strategy based on terminal inequality and equality constraints. Consequently, numerous obstacles can be avoided at the same time efficiently and rapidly through unstructured environments with narrow corridors. Finally, a case study with an omnidirectional wheeled mobile robot (OWMR) is presented to assess the proposed NMPC formulation for set-point stabilization. Furthermore, the efficacy of the proposed system is tested by experiments in simulated scenarios using a robot simulator named CoppeliaSim in combination with MATLAB which utilizes the CasADi Toolbox, and Statistics and Machine Learning Toolbox. Two simulation scenarios are considered to show the performance of the proposed framework. The first scenario considers only static obstacles while the second scenario is more challenging and contains static and dynamic obstacles. In both scenarios, the OWMR successfully reached the target pose (1.5m, 1.5m, 0°) with a small deviation. Four performance indices are utilized to evaluate the set-point stabilization performance of the proposed control framework including the steady-state error in the posture vector which is less than 0.02 meters for position and 0.012 for orientation, and the integral of norm squared actual control inputs which is 19.96 and 21.74 for the first and second scenarios respectively. The proposed control framework shows a positive performance in a narrow-cluttered environment with unknown obstacles