Ruben Van Parys

Online distributed motion planning for multi-vehicle systems

This work presents an online distributed motion planning strategy for cooperating vehicles. The motion planning is formulated as an optimization problem that returns smooth trajectories. These are parameterized as splines, which allows a representation with a limited number of variables and enables guaranteed constraint satisfaction with a finite set of constraints. The computations for solving the problem are distributed among the agents by using the Alternating Direction Method of Multipliers (ADMM). In order to cope with a dynamic environment and disturbances, the algorithm is formulated in a receding horizon fashion, such that the future part of a motion trajectory is reoptimized iteratively. The required update time and the amount of inter-agent communication are reduced by performing only one ADMM iteration per update. In this way the method converges over the subsequent path updates. Simulations with a formation of holonomic vehicles in a dynamic environment demonstrate the capability of the proposed approach to generate optimal trajectories at an update rate of 20 Hz.

Distributed MPC for multi-vehicle systems moving in formation

Abstract This work presents a novel distributed model predictive control (DMPC) strategy for controlling multi-vehicle systems moving in formation. The vehicles’ motion trajectories are parameterized as polynomial splines and by exploiting the properties of the B-spline basis functions, constraints on the trajectories are efficiently enforced. The computations for solving the resulting optimization problem are distributed among the agents by the Alternating Direction Method of Multipliers (ADMM). In order to reduce the computation time and the amount of inter-vehicle interaction, only one ADMM iteration is performed per control update. In this way the method converges over the subsequent control updates. Simulations for various nonholonomic vehicle types and an experimental validation on in-house developed robotic platforms prove the capability of the proposed approach. A supporting software toolbox is provided that implements the proposed approach and that facilitates its use.

Spline-Based Motion Planning for Autonomous Guided Vehicles in a Dynamic Environment

Autonomous vehicles require a collision-free motion trajectory at every time instant. This brief presents an optimization-based method to calculate such trajectories for autonomous vehicles operating in an uncertain environment with moving obstacles. The proposed approach applies to linear system models, as well as to a particular class of nonlinear models, including industrially relevant vehicles, such as autonomous guided vehicles with front wheel, differential wheel, and rear-wheel steering. The method computes smooth motion trajectories, satisfying the vehicle’s kinematics, by using a spline parameterization. Furthermore, it exploits spline properties to keep the resulting nonlinear optimization problem small scale and to guarantee constraint satisfaction, without the need for time gridding. The resulting problem is solved sufficiently fast for online motion planning, dealing with uncertainties and changes in the environment. This brief demonstrates the potential of the method with extensive numerical simulations. In addition, it presents an experimental validation in which a KUKA youBot, steered as a holonomic or differential drive vehicle, drives through an environment with moving obstacles. To facilitate the further development and the numerical and experimental validation of the presented method, it is embodied in a user-friendly open-source software toolbox.

Optimal autonomous quadrotor navigation in an obstructed space

This paper presents an ambitious methodology of autonomous navigation for multirotor UAVs in obstructed environments. The strategy was formulated to provide the multirotor vehicles the capability to produce autonomously quasi-optimal and safe trajectories, although generally they have at their disposal limited computational resources on board. The problem is formulated in a model predictive control (MPC) architecture in which motion planning and trajectory tracking processes are solved separately as if they were stored in two different devices. The first process uses a spline-based motion planning approach to generate smooth and safe trajectories. At this step also a multirotors simpified dynamic model and environment information are taken into account. The second process uses trajectory inputs, which are total thrust and attitude angle rates, to steer the multirotor during the flight. Both adequate time horizon and update frequency are chosen in order to account for disturbances and dynamics model mismatch. The methodology is validated by simulations and future work will include experimental tests in outdoor environment.