Open Experimental Agv Platform For Dynamic Obstacle Avoidance In Narrow Corridors

Automated Guided Vehicles (AGVs) are a promising solution to automation in the view of Industry 4.0. The amount of goods that can be automatically transported can be further increased by efficient path planning and tracking methods. The efficiency is always a trade off in terms of cost, accuracy and flexibility, but should never influence safety. This paper proposes a flexible path planning and tracking solution, aiming to be applicable to several application domains, and which is able to dynamically avoid an (unforeseen) obstacle by an overtake manoeuvre. The approach is based on Model Predictive Control (MPC), consisting of multi-domain objectives, applicable to multiple vehicle models and is fast in calculation time due to an adjusted multiple shooting approach, which guarantees constraint satisfaction over the entire time domain. Further, a dynamic maximum velocity approach is proposed, which adapts the maximum velocity constraint to the environmental circumstances, such that an emergency brake can be applied if a human would appear behind a corner or obstacle. These algorithms are implemented on an autonomous forklift. The overall system performance is measured by the time-of-arrival of an obstacle avoidance manoeuvre. To evaluate the influence of usage of a low-cost ultra wideband (UWB) localization technology, the same algorithms and platforms are used in combination with standard off-the-shelf laser based localization technology. The UWB technology does lead to a slightly larger spread in terms of time-of-arrival, but is on average very much comparable to the laser-based setup.