This paper proposes a solution for safe navigation of stacker vehicles in workspaces shared with people, with a focus on the docking manoeuvres for pallet picking and dropping. Behaviours for way-point and wall following are developed following the attractor dynamics approach. Then, these behaviours are orchestrated by state machines (that activate or deactivate them) depending on the specific task. Each of these states also defines different safe areas and maximum travel speeds, which is a requirement for safe operation. Results of real experiments are presented that show the standard operation and its robustness against perturbations (people in the way) and failure detection (missing pallets).

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.

We present a distributed leader-helper architecture for teams of two autonomous mobile robots that jointly transport large payloads while avoiding collisions with obstacles (either static or dynamic). The leader navigates to the goal destination and the helper is responsible for maintaining an appropriate distance (which is a function of the object's length) to the leader. Both robots share the responsibility of ensuring that the transported object does not collide with obstructions. No path needs to be given a priori to the robots nor to the payload. The team is able to perform its transportation task in unknown environments that can have corridors, corners and may change the layout online. The payload can be of different dimensions. The team is able to cope with abrupt/strong perturbations that challenge the team behavior during the execution of the task. These characteristics make this approach suitable to be deployed in warehouses or office-like environments. The motion of each robot is controlled by a time series asymptotically stable states, which is formalized using the attractor dynamics approach to behavior based robotics. The advantages are: (i) the overt behavior is smooth and stable; (ii) because the behavior is generated as a time sequence of attractor states, for the control variables, it contributes to the overall asymptotically stability of the system that makes it robust against perturbations. We present results of experiments in simulated environments and with real robots in real environments.