Autonomous system (mathematics)

About: Autonomous system (mathematics) is a research topic. Over the lifetime, 1648 publications have been published within this topic receiving 38373 citations.

Incorporating user inputs in motion planning for a smart wheelchair

Abstract: We describe the development and assessment of a computer controlled wheelchair equipped with a suite of sensors and a novel interface, called the SMARTCHAIR. The main focus of this paper is a shared control framework which allows the human operator to interact with the chair while it is performing an autonomous task. At the highest level, the autonomous system is able to plan paths using high level deliberative navigation behaviors depending on destinations or waypoints commanded by the user. The user is able to locally modify or override previously commanded autonomous behaviors or plans. This is possible because of our hierarchical control strategy that combines three independent sources of control inputs: deliberative plans obtained from maps and user commands, reactive behaviors generated by stimuli from the environment, and user-initiated commands that might arise during the execution of a plan or behavior. The framework we describe ensures the user's safety while allowing the user to be in complete control of a potentially autonomous system.

Perception, planning, and control for autonomous walking with the Ambler planetary rover

Abstract: Producing a robotic system that can operate autonomously on other planets is a challenging task: The robot must be ex tremely self-reliant; it must be able to operate on a limited power budget; and it must be able to traverse a wide range of rugged terrain features. This article describes an integrated software system that combines perception, planning, real-time control, and task-level control to navigate autonomously the Ambler, a six-legged rover. The overall approach to walking is highly deliberative: Where actions are goal-directed, steps are planned out in detail using explicit models of the terrain and the vehicle, and moves are checked for safety prior to execution. We believe that this approach is well-suited to meet ing the challenges of reliability, efficiency, and terrainability faced by autonomous planetary rovers. The Ambler system has been extensively tested, both indoors and outdoors, and the results confirm our expectations regarding the degree of system capability and performance afforde...

Autonomous driving: cognitive construction and situation understanding

Abstract: Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of `perception, planning, decision-making, and control. It is necessary to explore a basic computing framework that conforms to human drivers attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence.In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios.The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment.

A customizable coordination service for autonomous agents

Abstract: We address the problem of constructing multiagent systems by coordinating autonomous agents, whose internal designs may not be fully known. We develop a customizable coordination service that (a) takes declarative specifications ofthe desired interactions, and (b) automatically enacts them. Our approach is based on temporal logic, and has a rigorous semantics and a naturally distributed implementation.