Energy management is essential for identifying potential savings and predicting the energy requirements of buildings. Energy meters are often located in underground spaces that are difficult to reach with wireless technology. This paper presents an experimental study comparing different Low Power Wide Area Networks (LPWAN) technologies in terms of building penetration and radio coverage. The technologies Low Power Long Range Wide Area Networks (LoRaWAN®), Narrow Band Internet of Things (NB-IoT), Sigfox 0G and Wireless Smart Ubiquitous Networks (Wi-SUN®) are evaluated experimentallyThis paper also proposes a distributed hybrid IoT system design that combines multiple LPWAN technologies using an abstraction layer to optimize cost and coverage. Communication is message-based using the publish-subscribe messaging pattern. It is implemented using the MQTT protocol. The abstraction layer decodes the proprietary binary data and converts it to a normalized JSON format.

Mobile manipulators extend the workspace of industrial manipulators by mounting them on mobile platforms. In this paper we present a trajectory control scheme for a special class of mobile manipulators. The mobile manipulator consists of a mobile platform driven by two Differential Drive Steering Units (DDSUs) and a robotic arm with 6 Degrees of Freedom (DoF). The mobile platform provides additional 3 DoF in motion. This results in a total of 9 DoF in the configuration space for the entire mobile manipulator. This paper develops a trajectory control scheme for mobile platforms driven by DDSUs that incorporates the kinematic motion constraints of the system. We propose a control and sensing scheme for high accuracy tracking relative to the workpiece. The proposed trajectory control scheme is evaluated by experimental analysis, which shows that the tracking control scheme can achieve end-effector mm accuracy relative to the workpiece.

Manipulators have been used in a large range of applications mainly in the production industry, but their application is limited in scenarios which need a very large working space. Mobile manipulators extend the workspace of manipulators by mounting them on mobile platforms. The paper presents the kinematic modeling and motion control for a special class of mobile manipulators. The mobile manipulator consists of a mobile platform driven by two Differential Drive Steering Units (DDSUs) and a robotic arm with 6 Degrees of Freedom (DoF). The mobile platform provides additional 3 DoF in motion. This leads to a total of 9 DoF in the configuration space for the whole mobile manipulator. The paper develops a generic kinematic model of mobile platforms driven by DDSUs that includes the kinematic motion constraints of the system. By allowing the concurrent use of all DoF in the configuration space, this enables large scale continuous manipulation task like performing welding operations exceeding the reach of commonly used manipulators. The developed kinematic model is evaluated by experimental analysis.

In order to use the road network as efficiently as possible and to fully exploit the potential of automated vehicles, vehicle cooperation is essential. Conflicts between road users over available space on the road can only be resolved efficiently through cooperation. To this end, we investigate the space time reservation procedure (STRP) which is a generic approach to cooperation between vehicles. The method is based on a decentralized two-step negotiation procedure during which parts of the road can be reserved in order to carry out maneuvers that were originally conflicting. After the rollout of the first cooperative automated vehicles, a long transition period with mixed traffic is expected. Since the approach in this paper builds on existing road traffic regulations and does not abolish them, the method can also be applied in mixed traffic. Compared to the preliminary work on this approach, the method is simplified and further generalized for already supported traffic situations. This increases flexibility during the execution of cooperative maneuvers. Additionally, new reservation geometries covering almost all possible conflicts in traffic are investigated, e.g. lane changes, intersections, overtaking, roundabouts. Finally, the approach is evaluated by means of test drives with two research vehicles and in simulations.

Overtaking is one of the most difficult tasks during driving. This manoeuvre demands good skills to accomplish it correctly. In the overtaking considering multiple vehicles (more than a couple) is necessary to understand, predict and coordinate future actions of the other participants. These reasons make it a significant scenario for testing in the connected and automated driving field, with the main goal of predicting safe future states. In this sense, this work presents an overtaking method based on a linear Model Predictive Control (MPC) approach, which considers multiple participants involved in the scenario. This method adapts dynamically the trajectory for the manoeuvre in case of unexpected situations. Some of these changes consider other vehicles coming on the opposite lane or variations on participants' driving decisions. Additionally, the system considers passengers' comfort, the vehicle physical constraints and lateral actions of the vehicle decoupled of the longitudinal ones to simplify the problem.

Automated driving is a safety critical process, which requires complex decision making. In order to validate driving decisions, it is possible to maintain at all times a contingency maneuver, which transfers the vehicle to a safe standstill, if other decision making processes fail. In this paper we present a motion planner, which computes contingency maneuvers for an automated vehicle in a 0.1[s] time frame. A discrete set of motion primitives is assembled in a heuristic best-first search. In order to speed up the search, an obstacle sensitive heuristic is applied, which maintains properties of bounded sub-optimality and completeness. A run-time comparison with and without the obstacle sensitive heuristic is presented on two exemplary collision avoidance scenarios.

This paper presents a new approach for kinodynamic online motion planning for Automated Guided Vehicles (AGVs) in industrial environments. AGVs normally transport large and heavy transport units such as Euro-pallets or mesh pallets. Nowadays just-in-time inventory management and lean production requires the transport of small transportation units. Thus a flexible material flow is needed that can not be fulfilled by continuous material handling devices like belt or roll conveyors. Our approach uses small holonomic AGVs that are equipped with safety laser range finders for our semi autonomous motion and path planning system. This system plans trajectories with respect to the kinodynamic constraints of the AGVs and the environment. The operator retains as much control as necessary and the vehicle can react online on course deviations and unforeseen changes in the environment.

An increasing amount of robotic systems is developed for safety-critical scenarios, such as automated cars operating in public road traffic or robots collaborating with humans in flexible manufacturing systems. For this reason, it is important to provide methods that formally verify the safety of robotic systems. This is challenging since robots operate in continuous action spaces in partially unknown environments so that there exists no finite set of scenarios that can be verified before deployment. Verifying the safety during the operation based on the current perception of the environment is often infeasible due to the computational demand of formal verification methods. In this work, we compute sets of behaviors for parameterized motion primitives using reachability analysis, which is used to build a maneuver automaton that connects motion primitives in a safe way. Thus, the computationally expensive task of building a maneuver automaton is performed offline. The proposed analysis method provides the whole set of possible behaviors so that it can be verified whether forbidden state-space regions are avoided during the operation of the robot, to e.g. avoid colliding with obstacles. The method is applied to continuous sets of parameterized motion primitives, making it possible to verify infinitely many motions within the parameter space, which to the best knowledge of the authors has not been published before. The approach is demonstrated for collision avoidance of road vehicles.

Over the last years a number of different vehicle controllers has been proposed for tracking planned paths or trajectories. Most of previously published works do not compare their results with other approaches or limit the comparison to a few scenarios. Unfortunately, comparisons with existing controller concepts are very rare and a ranking is hard to establish from the literature. In this work, we rigorously compare inversion-based trajectory tracking controllers by systematically exploring the set of possible solutions when disturbances vary over time and initial states and parameters are uncertain. By using Monte-Carlo simulation, we determine the average performance and by using rapidly exploring random trees, we determine the worst-case performance, which is especially important in emergency situations when avoiding a crash is essential. The tested scenarios and the applied methodologies are documented in detail so that they serve as benchmark problems for other control concepts. The results show that the controller with smaller relative degree performs better with respect to the worst-case deviation computed by rapidly exploring random trees, while conventional simulations of random scenarios would not reveal any difference.

Automated Guided Vehicles (AGVs) are used in warehouses, distribution centers and manufacturing plants in order to automate the internal material flow. Usually AGVs are designed to transport large and heavy transport units such as Euro-pallets or mesh pallets. Just-in-time inventory management and lean production requires small transportation units to enable one-piece-flow. Furthermore short production cycles require a flexible material flow which can not be fulfilled by continuous material handling devices like belt or roll conveyors. A solution to meet these demands are small mobile robots for material transport which can replace conventional conveyor systems or large AGVs. The paper presents localization and tracking of an omnidirectional mobile robot equipped with Mecanum wheels, which was designed to transport Euro-bins in a distribution center or warehouse. Localization is realized by sensor fusion of range measurements obtained from an IEEE 802.15.4a network and laser range finders. The IEEE 802.15.4a network is used for communication as well as for global localization. Laser range finders are used to detect landmarks and to provide accurate positioning for docking maneuvers. The range measurements are fused in a Monte Carlo Particle Filter. The paper develops a new motion model for an omnidirectional robot as well as a sensor model for IEEE 802.15.4a range measurements. The experimental results presented in the paper show the effectiveness of the developed models.