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Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system.

使用人工智能在轮椅使用者和智能传感器系统之间共享电动轮椅的控制。

基本信息

批准号：
EP/S005927/1
负责人：
David Sanders
金额：
$ 59.32万
依托单位：
University of Portsmouth
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FS005927%2F1
关键词：
Using artificial intelligence share control

项目摘要

Research will focus on the novel use of sensors and inventing new shared control systems and artificial intelligence (AI) to significantly and positively impact on the lives of both current and potential powered-wheelchair users.Recently developed sensors will be digitised and then used in novel ways with AI to assist people with driving a powered wheelchair. This will allow some people to use a wheelchair by themselves for the first time, and will make driving and steering easier for many others. That will reduce the need for carers, improve health outcomes and give disabled people an opportunity for more independent mobility. For some it will provide mobility for the first time.Access to independent mobility is important for self-esteem and a feeling of wellbeing. Natural independent mobility such as crawling and walking are usually acquired in the first two years of life; if this does not happen then people can find it difficult to acquire the skills later. Currently a wheelchair can provide some self-initiated mobility but it cannot be introduced unless a person has the spatial awareness, physical ability and cognitive skills to understand the concept. Being able to transport oneself has a positive effect on general development that cannot be underestimated. This research will provide that opportunity.Research at the University of Portsmouth has already resulted in analogue collision avoidance and effort-reduction systems, so that people can drive for longer. Work at the Chailey Heritage Foundation created track systems to guide wheelchairs and novel systems that can follow a path parallel to a wall and sensors to safely detect the environment. All the devices will be redesigned as digital systems to connect them to expert systems for improved control. The new digital versions will interface to microcomputers. The new systems will interpret hand movements and tremors to improve control further. That will allow end-users to steer their powered wheelchairs without needing helpers and provide a greater sense of accomplishment and freedom, whilst simultaneously helping to reduce carer costs. The abilities of the wheelchair user will be constantly assessed so that control gains can be automatically set for the sensor system and the human driver. This will be achieved by calculating a self-reliance factor depending on ability, tiredness, recent driving performance etc. An intelligent avoidance-factor will depend on obstacle proximity, a safety-factor will denote the ability of the driver and an assistance-factor will depend on time spent driving and tiredness. The sensor system will influence the motion of the wheelchair to compensate in those areas. This is the first time that this has been attempted.Different AI systems will be used for different tasks to capitalise on their separate distinct strengths in diverse circumstances. An original hierarchy based upon the structure of Artificial Neural Networks will be used to integrate them. At least three AI techniques will be used to select courses of action for a wheelchair and a new Decision Making System (DMS) will be created to determine a best course of action by considering and comparing the outputs from the different artificial systems and the requirements of the human user. Each system will provide a level of confidence for a potential course of action, for example turn left, stop etc. The DMS will determine the action to take.This EPSRC project will produce both new devices and new ways of integrating devices into wheelchairs to ensure safe navigation and personalized assistance with general low cost but automatically adjustable solutions that make the systems bespoke and adaptable in real time. This will help to ensure users achieve maximum functionality. The devices can be added to existing wheelchairs, providing a cost-effective way of improving quality of life and independence.

研究将集中在传感器的新颖使用上，并发明新的共享控制系统和人工智能（AI），以对当前和潜在的电动轮椅用户的生活产生重大和积极的影响。最近开发的传感器将被数字化，然后以新颖的方式与人工智能一起使用，以帮助人们驾驶电动轮椅。这将使一些人第一次自己使用轮椅，并使许多其他人的驾驶和转向变得更容易。这将减少对护理人员的需求，改善健康状况，并为残疾人提供更独立行动的机会。对于一些人来说，这将是第一次提供活动能力。获得独立的活动能力对于自尊和幸福感非常重要。自然的独立活动能力，如爬行和行走，通常是在生命的头两年获得的；如果这种情况没有发生，那么人们以后会发现很难获得这些技能。目前，轮椅可以提供一些自发的移动能力，但除非一个人具有空间意识、身体能力和认知技能来理解这个概念，否则无法引入轮椅。能够自行运输对整体发展具有不可低估的积极影响。这项研究将提供这样的机会。朴茨茅斯大学的研究已经开发出了模拟防撞和省力系统，使人们可以驾驶更长时间。柴利遗产基金会的工作创建了引导轮椅的轨道系统和可以沿着与墙壁平行的路径行驶的新颖系统以及安全检测环境的传感器。所有设备都将被重新设计为数字系统，将它们连接到专家系统以改进控制。新的数字版本将与微型计算机连接。新系统将解释手部动作和颤抖，以进一步改善控制。这将使最终用户无需帮助即可驾驶电动轮椅，并提供更大的成就感和自由感，同时有助于降低护理成本。轮椅使用者的能力将不断得到评估，以便为传感器系统和人类驾驶员自动设置控制增益。这将通过根据能力、疲劳程度、近期驾驶表现等计算自力更生因素来实现。智能回避因素将取决于障碍物的接近度，安全因素将表示驾驶员的能力，辅助因素将取决于驾驶时间和疲劳程度。传感器系统将影响轮椅的运动以在这些区域进行补偿。这是第一次尝试。不同的人工智能系统将用于不同的任务，在不同的情况下发挥各自的独特优势。基于人工神经网络结构的原始层次结构将用于集成它们。至少三种人工智能技术将用于选择轮椅的行动方案，并且将创建一个新的决策系统（DMS），通过考虑和比较不同人工系统的输出和人类用户的要求来确定最佳行动方案。每个系统都将为潜在的行动方案提供一定程度的信心，例如左转、停止等。DMS 将确定要采取的行动。这个 EPSRC 项目将生产新设备以及将设备集成到轮椅中的新方法，以确保安全导航和个性化帮助，并提供通用低成本但可自动调节的解决方案，使系统能够实时定制和适应。这将有助于确保用户实现最大功能。这些设备可以添加到现有的轮椅上，提供一种经济有效的方式来提高生活质量和独立性。