A review of control architectures for autonomous navigation of mobile robots (original) (raw)

An Adaptive Multi-Controller Architecture for Mobile Robot Navigation

This paper proposes a hybrid control architecture dedicated to the navigation of autonomous mobile robots in presence of obstacles. The proposed control structure is flexible, adaptive and takes into account the different possible interactions between the multitude of controllers composing the architecture of control. This is enabled by the use of stable elementary controllers and to specific mechanisms of coordination and adaptation which guaranties the stability of the overall architecture.

A Control Architecture for Intelligent Mobile Robots

Intelligent mobile robots interacting with an uncertain and dynamic environment should be able to identify their own goals and perform tasks based on goals and their interactions with the environment. In addition, they should have planning, control, and reaction capabilities spanning several orders of magnitude in reaction time and solution complexity. We present a conceptual architecture for intelligent mobile robots that adheres to these specifications. "B-Robot" is a heterogeneous architecture inspired by the gross anatomy and physiology of the mammalian central nervous system. It combines goal-and task-level reasoning and planning skills with intermediate-level servo control loops and low-level reactivity.

ARCHITECTURE OF AN AUTONOMOUS SYSTEM: APPLICATION TO MOBILE ROBOT NAVIGATION

We investigate the behavioural approach for building autonomous systems like mobile robots. In this paper we describe the mobile robot MANO together with its experimental behavioural architecture and present several advanced topics related to the interplay of the physical, behavioural and cognitive levels of the architecture like integration of vision, navigation with selfpositioning, methodology of task implementation, and two original behavioural based knowledge representation approaches.

A multi-agent architecture for mobile robot navigation control

Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294), 2000

The challenge of this work is to implement an algorithm which enables the robot to achieve independent activities in the purpose of achieving a common goal, which consists in autonomous navigation in a partially unknown environment. The use of multiagent system is convenient for such a problem. Hence, we have designed a structure composed of four agents dedicated to perception, navigation, static, and dynamic obstacle avoidance. Those agents interact through a coordination system.

Development of behavior-based reactive navigation system for mobile robot

2013

Robotics technology has been evolved rapidly these last two decades especially in autonomous mobile robots development. One of the most important issues that related to autonomous mobile robots is it navigation systems. Deliberative navigation and reactive navigation are two types of navigation in mobile robot navigations. Reactive navigation in unknown and changing territories without prior knowledge to the environment is one of the most challenging problems in robotics, thus has been investigated by researchers for many years. Previous researchers gave particular attention to local minimum and multiple minimum problems which trap the robot into an infinite loop during its navigation. This could ruin the objective of good local navigation which a good local navigation should able the robot to navigate the unknown environment safety without having collision with the available obstacles. To solve the problems and achieve the above objective, a new behavior-based reactive navigation s...

Can Planning and Reactive Systems Realize an Autonomous Navigation

The major challenges facing navigation of an autonomous mobile robot and need to be addressed are stem from, incomplete and uncertain knowledge of the environment, unpredictable aspects of real environment and surroundings, sensor limitation and uncertainty of sensory information (range, resolution limits, noise and occlusions), uncertainty in the effects of the robot own action (imperfect actuators), the need to respond quickly to environment demand in which the robot has to operate at a pace dictated by the interaction with its surrounding (limits on computation time due to restrictions imposed by the environment). These issues are fundamental to autonomous systems that have to function effectively while navigating and interacting with unknown, unstructured and dynamic environment. Several approaches have been developed to address this important issue at various levels in mobile robot's control architectures. This paper discusses the main approaches in the field of autonomous navigation. It focuses on the challenges, needs, fundamental issues along with the requirements that enable a mobile robot to move autonomously, purposefully, reliably and without human intervention through unstructured real world environments that have not specifically prepared for them at design time.

Implementation of a Reactive Autonomous Navigation System on An Outdoor Mobile Robot

… for Unmanned Vehicle …, 1994

Researchers at the University of Michigan's AI Lab are working to produce mobile robot systems capable of intelligently reacting to events and objects in a real world environment. A system has been built which integrates a reactive planner with perception and navigation capabilities in order to generate exible, responsive vehicle behavior. The system architecture consists of four layers; The vehicle control layer, the behavior control layer, the manager layer and the planner layer. This system design was implemented on a outdoor mobile robot. Results have shown that the robot, using this architecture, is able to perform reactive autonomous navigation in an outdoor environment.

A Nested-Loop Architecture for Mobile Robot Navigation

The International Journal of Robotics Research, 2000

This paper describes a navigation architecture for mobile robots, structured as a set of nested control loops whose depth is related to their knowledge of the environment and the ability to drive the actuator, and involving as well competing behaviors that will ultimately generate the robot motion. The architecture has been successfully used on a mobile platform to support three-dimensional environment reconstruction tasks. The architecture may be classified as belonging to the hybrid type but made up of hybrid elements as well, allowing virtually any level of input awareness and ranging from high-level task planning to direct motion commands issued by external user or applications. A monitorized data path ensures the construction of the most adequate and safe motion, as well as an unlimited set of behaviors depending on the already known and perceived environment. Added concepts of path recovering and assisted navigation fulfill the demands for the three-dimensional acquisition scheme involved. Some comparison with existing architectures is carried out throughout the text. The versatility and robustness of the architecture are supported by extensive results.

A hybrid mobile robot architecture with integrated planning and control

Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 1 - AAMAS '02, 2002

Research in the planning and control of mobile robots has received much attention in the past two decades. Two basic approaches have emerged from these research efforts: deliberative vs. reactive. These two approaches can be distinguished by their different usage of sensed data and global knowledge, speed of response, reasoning capability, and complexity of computation. Their strengths are complementary and their weaknesses can be mitigated by combining the two approaches in a hybrid architecture. This paper describes a method for goal-directed, collision-free navigation in unpredictable environments that employs a behavior-based hybrid architecture with asynchronously operating behavioral modules. It differs from existing hybrid architectures in two important ways: (1) the planning module produces a sequence of checkpoints instead of a conventional complete path, and (2) in addition to obstacle avoidance, the reactive module also performs target reaching under the control of a self-organizing neural network. The neural network is trained to perform fine, smooth motor control that moves the robot through the checkpoints. These two aspects facilitate a tight integration between high-level planning and low-level control, which permits real-time performance and easy path modification even when the robot is en route to the goal position.