Motion Control for Autonomous Tugger Vehicles in Dynamic Factory Floors Shared with Human Operators

Abstract

We present a motion controller that generates collision free trajectories for autonomous Tugger vehicles operating in dynamic factory environments, where human operators may coexist. The controller is formalized as a dynamic system of path velocity and heading direction, whose vector fields change as sensory information varies. By design the parameters are tuned so that the control variables are close to an attractor of the resultant dynamics most of the time. This contributes to the overall asymptotically stability of the system and makes it robust against perturbations. We present several experiments, in a real factory environment, that highlight different innovative features of the navigation system-flexible and safe solutions for human-aware autonomous navigation in dynamic and cluttered environments. This means, besides generating online collision free trajectories between via points, the system detects the presence of humans, interact with them showing awareness of their presence, and generate adequate motor behavior. Index Terms-Tugger vehicles, flexible and safe autonomous navigation, obstacle avoidance, dynamic environments shared with human operators

A neural integrator model for planning and value-based decision making of a robotics assistant

“…In both steps, the selected target direction is used as input to a path planning module driven by a non-linear attractor dynamics in which targets are modeled as attractive and obstacles as repulsive forces. The non-linear attractor approach to autonomous navigation has been successfully tested in highly dynamic industrial environments [42,43].…”

“…For future real-world tests, it is interesting to notice that the DNF approach to cognition is highly compatible with the attractor dynamics approach to autonomous navigation of mobile robots [4] (see the Discussion). Recently, the approach has been successfully tested in a factory environment cluttered with stationary and moving obstacles [43,42]. For the material handling task, the main challenge for the robot is to deal with an environment characterized by uncertainty.…”

A safe autonomous stacker in human shared workspaces

“…The vehicle's actuation is exposed on a ROS network using the ROS Control framework, to which the high-level controllers will interface. Most of this high-level control and interface has been previously developed for a tugger with similar internal architecture (see [8] for more details). The vehicle's indoor localization is also the same as in [8], i.e.…”

“…This encompasses the addition of specific sensors, the development of custom electronics, firmware and interfacing software, and also all the high-level software, ranging from the generic sensor processing, localization and mapping up to the specific controllers to the selected tasks. In previous work, we have already presented the low-level architecture and interfacing but applied to a tugger vehicle [8]. Here, one extends that work by focusing on stacker specific tasks, i.e.…”

“…Task controllers are developed using a hybrid approach. On the one hand, elementary behaviours are formalized as nonlinear dynamical systems in terms of path velocity and heading direction, which are based on the Attractor Dynamics Approach to Behavior Generation [14], and build on our previous work [8], [9]. On the other hand, these behaviours are activated and deactivated by state machines whose states also define safety parameters for vehicle operation (protection areas around the vehicle and maximum velocity, for instance).…”