Real-Time Multi-Convex Model Predictive Control for Occlusion-Free Target Tracking With Quadrotors

Abstract

This paper proposes a Model Predictive Control (MPC) algorithm for target tracking amongst static and dynamic obstacles. Our main contribution lies in improving the computational tractability and reliability of the underlying non-convex trajectory optimization. The result is an MPC algorithm that runs real-time on laptops and embedded hardware devices such as Jetson TX2. Our approach relies on novel reformulations for the tracking, collision, and occlusion constraints that induce a multi-convex structure in the resulting trajectory optimization. We exploit these mathematical structures using the split Bregman Iteration technique, eventually reducing our MPC to a series of convex Quadratic Programs solvable in a few milliseconds. The fast re-planning of our MPC allows for occlusion and collision-free tracking in complex environments even while considering a simple constant-velocity prediction for the target trajectory and dynamic obstacles. We perform extensive bench-marking in a realistic physics engine and show that our MPC outperforms the state-of-the-art algorithms in visibility, smoothness, and computation-time metrics.

ROBOTONT – Open-source and ROS-supported omnidirectional mobile robot for education and research

“…Robotont is being continuously validated as an educational tool and a research platform - notable examples of such results have already been reported in a number of research publications [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . More specifically, this robot has been extensively used to deliver university teaching, professional training, and online courses on the topics of ROS and robotics as described in [20] , [21] , [22] , [23] .…”

“…[28] , [29] , [30] , [31] , [32] ) and a platform to validate cutting-edge robotics research about e.g. software frameworks [24] and motion-planning [25] , [26] , [27] .…”

Constraint-following control for dynamic systems with comprehensive constraints: the generalized Udwadia–Kalaba approach

“…From the perspective of the modeling process, there are mainly two modeling methods: Identification modeling and Mechanistic modeling. The former relies on observed input and output data to establish corresponding mathematical models, which lacks a profound understanding of the internal principles and mechanisms of the system [11,12]. In [13], a nonlinear dynamics model for hydraulic construction robot was established base on a novel identification algorithm to realize the trajectory tracking performance.…”