Editorial

Computational Intelligence, vol. 39, pp. 530–531

Abstract

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.

Authors 3

1. Meng Joo Er

   Dalian Maritime University

   Affiliation as printed

   Changjiang Scholar Distinguished Professor Director of Institute of Artificial Intelligence and Marine Robotics, College of Marine Electrical Engineering, Dalian Maritime University Dalian China

   Changjiang Scholar Distinguished Professor, Director of Institute of Artificial Intelligence and Marine Robotics, College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China

2. Zhaojie Ju

   University of Portsmouth

   Affiliation as printed

   Reader in Machine Learning and Robotics, School of Computing University of Portsmouth Portsmouth UK

   Reader in Machine Learning and Robotics, School of Computing, University of Portsmouth, Portsmouth, UK

3. Alexander Ferrein Aachen Cognitive Robotics Institute

   FH Aachen

   Affiliation as printed

   Department of Electrical Engineering and Information Technology Mobile Autonomous Systems & Cognitive Robotics Institute, Robotics and Foundations of Computer Science, FH Aachen ‐ University of Applied Sciences Aachen Germany

   Department of Electrical Engineering and Information Technology Mobile Autonomous Systems & Cognitive Robotics Institute, Robotics and Foundations of Computer Science, FH Aachen ‐ University of Applied Sciences Aachen Germany