Mobile service robots deployed in populated environments like train stations, airports or offices are not only required to move safely, but also in socially acceptable ways. In order to achieve this, robots need to be able to track people within their vicinity. This work presents an approach to tracking people from a mobile robot platform, incorporating a novel approach to human motion prediction. Unfavorable viewing angles, motion blur and ever-changing light conditions are constant issues for sensors on mobile vehicles. Therefore, a system is needed which increases the tracking quality of humans in order to cope with a low detection rate. The scientific contribution of this paper lies in a precise human model for tracking which utilizes historical spatial data of pedestrians from previous detection and from simulation. The model is embedded into a particle-filter based tracking approach designed for the use on a moving platform and is able to incorporate a variety of person detectors. Experiments conducted prove that the proposed method increases tracking quality, especially at a low detection rate.

Autonomous vehicles are, in contrast to classical automated guided vehicles (AGVs), less predictable in their behavior and drive time. Therefore, the issue of how to efficiently control these vehicles arises, because autonomous agents need to be coordinated and not controlled, to give autonomous behaviors and actions space. The scientific contribution of this paper is a novel approach, based on prioritized planning to target this issue as well as an open source framework for evaluation and comparison. Prioritized planning has the disadvantage of being neither optimal nor complete, however, it has the advantage of being computationally feasible. This work utilizes prioritized planning to significantly increase the set of feasible scenarios through collision prevention: by locally finding alternative routes and adding them to the search graph. The paper clearly formulates the extensions needed and delineates the approach's limits, as it is neither optimal nor complete. More importantly, however, our method calculates routes for each vehicle with inter-vehicle synchronization, enabling vehicles to execute the plan in a distributed fashion without centralized control, thereby allowing autonomous behavior. Finally, results are verified by comparing our Multi Robot Router (MRR) proposed in this work to classical approaches. The software developed as well as the test sets are publicly available for ROS and the simulation environment.

Emergency trajectories enable one to move faster through an environment while still moving safely. Having an emergency trajectory within an observed vacant space makes it possible to safely navigate through unknown territory or through a door without slowing down. Emergency trajectories allow for safe navigation of a vehicle into a safe system state, e.g. a stop, in the event of recognition of an obstacle. This work formally proves the benefit of using emergency trajectories to generate safe and faster motion controls as compared to vehicle operation without such trajectories. Furthermore, this work also presents a working integration of this formalism into a vehicle's low level control system in a Moving Horizon Trajectory Planner (MHTP) with an update rate of 10Hz. Using an MHTP along with a dynamic model of the environment and the proposed constraints, the system is able to derive emergency trajectory candidates which fulfill our safety requirements. This distinguishes the approach from that of others, which replans discrete paths that are then followed by the vehicle's local control system. This approach was implemented on a differential-drive mobile agent and tested using non-static environment assumptions. Simulated and real-robot experimental results illustrate the quality of our approach.

This paper presents a novel Local-Path Planning approach for an independent four-wheel steering (I4WS) mobile base. The approach smoothly drives and rotates the platform while still following a given path and avoiding obstacles. I4WS vehicles are omnidirectional vehicles, similar to shopping carts, if one neglects the time needed to steer the wheels. However, due to the danger of mechanical parts breaking while actuating the wheels, they are commonly controlled in a stop-and-go fashion, where the vehicle stops to turn its wheels perpendicular to a desired instantaneous center of curvature (ICC) before starting to move again. The approach overcomes this limitation and ensures an ICC-based kinematic constraint during continuous motion using a flat-input controller running at 100 Hz. Therefore, the trajectory of the ICC is both predictable and suitable for model predictive control (MPC). The MPC implemented generates optimized collision-free trajectories of up to several meters ahead at 10 Hz, given a set of points along a path and laser contour readings. Furthermore, the planner realized here is able to deal with additional constraints such as the vehicle's view direction to focus on attention points while driving. Experimental results highlight the capabilities of the approach on a simulated robot, using GazeboSim, and demonstrate its applicability for the field of service robotics.

When we think of service robots for public spaces, we often consider human-like systems that socially interact with users in short-term and dynamically changing scenarios. Many design assumptions exist for this type of robot, but what about for a service robot that needs to interact with people in a very task/role-oriented manner? This paper presents a research-through-design approach, which explores the idea of a "luggage-carrying robot guide" for train stations which supports travellers. Contrary to the classical paper describing the requirements, design, implementation, and evaluation of the robot, this work presents an exploratory design study. An interdisciplinary team composed of two industrial designers, a roboticist, and a social scientist performed a study with users to establish a design space for the aforementioned service robot.