Autonomous robot operations in the industry are becoming increasingly complex. It is therefore a significant challenge to comprehend the fundamental processes and to gain an understanding of the status of these systems. The RoboCup Logistics League (RCLL) represents a small smart factory environment with several workstations and operating robots. Despite its small scale the processes that occur within the league are very complex. Even with live commentary, observers have difficulties to follow the processes and game progress. This results in low interest in the RCLL and only few of visitors at competitions. To address this, we want to present RCLL-AR, an augmented reality (AR) solution visualizing highly relevant information of the RoboCup Logistics League. By using RCLL-AR, spectators of the game can see the current progress of the game, receive additional information about different workstations and understand future robot movements. To gain insights into the benefits of RCLL-AR for different stakeholders, we conducted expert interviews, a novice user study and an HMD study. Our findings showcase challenges AR faces in complex autonomous systems but also indicate benefits for novices and experts.

The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.

We are pleased to announce the publication of the Special Issue on Hybrid Control of Autonomous Mobile Robots: Architectures, Algorithms and Applications. The control problem of Autonomous Mobile Robots (AMR) in a dynamic environment is a fundament problem that has been receiving much attention from researchers from the world. The main issue here is how to obtain accurate, flexible, and reliable navigation? To perform a navigation task efficiently and effectively, the robot must have perception, decision-making and action capacities for interacting with the environment. The type and complexity of control architecture are usually related to the complexity of the environment and the task at hand. Navigation methods are classified into two main categories, namely, global planning methods (deliberative navigation) and local planning methods (reactive navigation). The main advantage of local planning methods is that they do not require a priori knowledge on the environment model and sometimes without the explicit model of the robot. In the recent years, several local planning methods have been developed. Most of them are based on artificial potential field, fuzzy logic and artificial neural networks. These methods are generally applicable to unknown environments and can be easily adapted to dynamically changing environments. However, such methods frequently suffer from the problem of local. In addition, the actual trajectory is not optimal in terms of distance and/or travel-time due to lack of global vision on the environment. In global planning methods, a navigation task can be achieved in two phases, namely, trajectory planning and tracking phases. Trajectory planning of a robot revolves around fulfilling some performance criteria (distance, travel-time, and energy consumption) and satisfying a certain number of constraints (geometric, kinematic, and/or dynamic). This ensures a safe and fast navigation solution taking into consideration kinematic and dynamic capacities of the robot, and the constraints related to the environment. However, these methods do not adapt to the dynamic of the environment (unexpected obstacles) or completely unknown environment. As regards to the trajectory planning, several approaches whereby the trajectory is generally made up of line segments connected via tangential circular arcs have been proposed. Most of these works deal with minimum-time trajectory-planning problems, under linear/angular velocity bounds of the platform. Some performance techniques have been developed to reach the goal as quickly as possible by smoothing transitions, thus achieving continuous-curvature trajectories. Concerning the problem of trajectory tracking, it revolves around following a reference trajectory by minimizing the position, orientation and sometimes speeds errors while maintaining the robot's stability. Many control methods have been proposed; some of them are the classic PID control, Lyapunov-based nonlinear control, sliding mode control, and fuzzy logic control. According to the available information on the navigation environment, methods of the first or second group are selected more often. This leads for three classes of control architectures, namely, reactive, deliberative and hybrid ones. Reactive control architectures are based on the “Sense & Act” principle that combines trajectory planning and its execution at the same level. Generally speaking, they are composed of a set of specific behavioral modules (task-specific behaviors). This allows the robot to make real-time decisions based on local perception and reactive interactions required in unknown and dynamically changing environments. The reference of most proposed solutions is the Subsumption Architecture which can be divided into two main classes based on competitive or cooperative mechanisms between behaviors modules. Deliberative control architecture is based on “Sense, Plan & Act” principle used in fully known environments. In fact, the robot model must be known and continually updated to plan the robot's actions. In this approach, one or more trajectories are first planned. Next, according to the actual state of the perceived information, the robot executes trajectory tracking strategies. Deliberative systems are considered as classical control architectures since they were the first to be tested. Given the drawbacks of the two types of methods, the combination of both types gives hybrid control architecture which enables navigation in partially known environments. This choice allows fast and reactive solution while avoiding unexpected obstacles and reducing the traveling time with introduction of partial knowledge of the environment. In fact, some interesting works adopting this approach have been reported in the literature. The last decade witnessed increasingly rapid progress in AI-powered hybrid control of AMR, mainly backed up by advances in the areas of artificial intelligence and deep learning. In particular, AI-based hybrid control architectures, convolutional, and recurrent neural networks, as well as the deep reinforcement learning paradigm have been proposed. These methodologies form a base for scene perception, path planning, behavior arbitration and motion control algorithms. Furthermore, modular perception-planning-action pipeline, where each module is built using deep learning methods which directly map sensory information to steering commands has been investigated. In this special issue, we included original contributions pertaining to architectures, algorithms and applications of hybrid control of AMR. We have performed a professional and strict review process in order to guarantee the quality of the special issue. We would also like to cordially thank all the reviewers who have participated in the review process of the articles submitted to this special issue, and the publishing team.

One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.

In this paper we present an extension of the action language Golog that allows for using fuzzy notions in non-deterministic argument choices and the reward function in decision-theoretic planning. Often, in decision-theoretic planning, it is cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, even for domain experts, it is not always easy to specify a reward function. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in Golog, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy uents. In Golog’s forward-search DT planning algorithm, these formulas are evaluated in order to find the agent’s optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order.

For numerous real-world applications teleoperated unmanned guided vehicles (UGVs) can quite successfully assist a human in fulfilling her mission objectives. It is important for the specialists to get an overview of the site quickly and with as intuitive means as possible. Our approach to an intuitive human-machine interface for visually teleoperating UGVs makes use of a spherical camera in combination with the Virtual Reality Head-Mounted Display (HMD) Oculus Rift. The Oculus Rift is equipped with an inertial measurement unit to track the teleoperator's head orientation. With this orientation information, the current field of view is synthesized from the panoramic image coming from the spherical camera allowing a free look for the operator. In this paper, we present the hardware setup and the software system of our free-look HMD approach on our UGV. Our approach allows for multi-view, i.e, several operators can collaborate on a given task. What is more, we augment the spherical image with heading and orientation information of the robot as well as of other viewers. Our preliminary evaluation with a number of untrained user suggests that our free-look HMD offers an intuitive multi-view human-machine interface for teleoperating UGVs.

The RoboCup Logistics League (RCLL) has seen major rule changes increasing the complexity, e.g. by raising the number of product variants from 3 to almost 250, and introducing new challenges like the handling of physical processing machines. We describe various aspects of our system that allowed to improve the performance in 2015 and our efforts to advance the league as a whole. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Autonomous mobile robots comprise a great deal of complexity. They require a plethora of software components for perception, actuation, task-level reasoning, and communication. These components have to be integrated into a coherent and robust system in time for the next RoboCup event. Then, during the competition, the system has to perform stable and reliably. Providing a software framework for teams to use tremendously eases that effort. Even more so when providing a fully integrated system specific for a particular domain. We have recently released our full software stack for the RoboCup Logistics League (RCLL) based on the Open Source Fawkes Robot Software Framework . This release includes all software components of the RoboCup 2014 winning team Carologistics (in cooperation with the AllemaniACs RoboCup@Home team). It specifically also includes the parts of the software which are domain or platform specific or which we consider our competitive edge and were kept private until now. We think that this will make the league much more accessible to new teams and might help existing teams to improve their performance. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the RoboCup Logistics League (RCLL), games are governed by a semi-autonomous referee box. It also records tremendous amounts of data about state changes of the game or communication with the robots. In this paper, we analyze the data of the 2014 competition by means of Key Performance Indicators (KPI). KPIs are used in industrial environments to evaluate the performance of production systems. Applying adapted KPIs to the RCLL provides interesting insights about the strategies of the robot teams. When aiming for more realistic industrial properties with a 24/7 production, where teams perform shifts (without intermediate environment reset), KPIs could be a means to score the game. This could be tried first in a simulation sub-league. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The robot programming and plan language IndiGolog allows for on-line execution of actions and offline projections of programs in dynamic and partly unknown environments. Basic assumptions are that the outcomes of primitive and sensing actions are correctly modeled, and that the agent is informed about all exogenous events beyond its control. In real-world applications, however, such assumptions do not hold. In fact, an action's outcome is error-prone and sensing results are noisy. In this paper, we present a belief management system in IndiGolog that is able to detect inconsistencies between a robot's modeled belief and what happened in reality. The system furthermore derives explanations and maintains a consistent belief. Our main contributions are (1) a belief management system following a history-based diagnosis approach that allows an agent to actively cope with faulty actions and the occurrence of exogenous events; and (2) an implementation in IndiGolog and experimental results from a delivery domain.

High-level behaviour specification of an intelligent autonomous agent or robot is a non-trivial task. Various approaches exist some of which try to combine different paradigms like programming and planning. In this paper, we show how to integrate fuzzy logic controllers into the logic-based programming language Golog. Golog already allows for combining programming and planning. By adding the instrument of fuzzy controllers we provide the means to have a natural specification of rules for tasks that require a high amount of reactivity. Since the facilities already present in Golog remain, we add to an already powerful framework thus expanding the applicability of Golog for high-level behaviour specification of a robot or agent.