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).

Vehicle models and dynamic system simulations are used to predict the behavior and dynamics of vehicles. In this paper two vehicle models of a tugger-trolleys system with three linked towed trolleys are compared. One model is a complete multibody model of the system. The second is a simplified version composed by a vertical and handling models for motion in a plane, assuming no planar forces on the trolleys' front wheels and linking the vehicles with multibody joints and body, representing the coupling arms. The second model was developed in Modelica language and a description of the model is presented. These models will be used to estimate the relative paths between the vehicles and the maximum admissible velocities of the system when taking a curve without rollover or sliding. Results show good agreement between the models. The second model was shown to be much faster and provide more stable results.

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.

This paper describes the modelling and simulation of a straight linear motion planar mechanism, commonly known as a Scott-Russell mechanism (SRM), for industrial application. The proposed mechanism is applied in an internal materials movement system using autonomous guided vehicles (AGVs) for supporting logistics processes along the supply chain, aiming to replace the current manual operations. The requirements of the design are identified and the modelling phases of the mechanism are described. A compression spring and a linear solenoid are selected using kinematic and kinetic simulation.

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.

Humans are experts in cooperating in a smooth and proactive manner. Action and intention understanding are critical components of efficient joint action. In the context of the EU Integrated Project JAST [16] we have developed an anthropomorphic robot endowed with these cognitive capacities. This project and respective robot (ARoS) is the focus of the video. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, error detection and anticipatory action selection. Results were considered one of the ICT “success stories”[22].

In this paper, we show how a team of autonomous mobile robots, which drive in formation, can be endowed with basic cognitive capabilities. The formation control relies on the leader-follower strategy, with three main pair-wise configurations: column, line and oblique. Furthermore, non-linear attractor dynamics are used to generate basic robotic behaviors (i.e. follow-the-leader and avoid obstacles). The control architecture of each follower integrates a representation of the leader (target) direction, which supports leader detection, selection between multiple leaders (decision) and temporary estimation of leader direction (short-term memory during occlusion and prediction). Formalized as a dynamic neural field, this additional layer is smoothly integrated with the motor movement control system. Experiments conducted in our 3D simulation software, as well as results from the implementation in middle size robotic platforms, show the ability for the team to navigate, whilst keeping formation, through unknown and unstructured environments and is robust against ambiguous and temporarily absent sensory information.

Dynamical systems theory is used as a theoretical language and tool to design a distributed control architecture for teams of mobile robots, that must transport a large object and simultaneously avoid collisions with (either static or dynamic) obstacles. Here we demonstrate in simulations and implementations in real robots that it is possible to simplify the architectures presented in previous work and to extend the approach to teams of n robots. The robots have no prior knowledge of the environment. The motion of each robot is controlled by a time series of asymptotical stable states. The attractor dynamics permits the integration of information from various sources in a graded manner. As a result, the robots show a strikingly smooth an stable team behaviour.