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

研究将集中在传感器的新用途以及发明新的共享控制系统和人工智能(AI)，以显著和积极地影响现有和潜在的电动轮椅用户的生活。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。这将允许一些人第一次单独使用轮椅，并将使许多其他人的驾驶和转向更容易。这将减少对照顾者的需求，改善健康状况，并为残疾人提供更多独立行动的机会。对于一些人来说，这将是第一次提供行动能力。独立行动能力对自尊和幸福感很重要。爬行和行走等自然独立行动能力通常是在生命的头两年获得的；如果不这样做，人们可能会发现以后很难获得这些技能。目前，轮椅可以提供一些自我发起的移动性，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，否则它不能被引入。能够运送自己对总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得出了模拟的碰撞避免和减少努力的系统，这样人们就可以开车更长时间。柴利传统基金会的工作创造了引导轮椅的轨道系统，可以沿着与墙平行的路径行驶的新颖系统，以及安全检测环境的传感器。所有的设备都将被重新设计为数字系统，以将它们连接到专家系统，以改善控制。新的数字版本将与微型计算机接口。新系统将解读手部运动和震颤，以进一步改善控制。这将允许最终用户在不需要助手的情况下驾驶他们的电动轮椅，并提供更大的成就感和自由感，同时帮助降低护理成本。轮椅使用者的能力将不断被评估，以便可以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、疲劳度、最近的驾驶表现等计算自力更生系数来实现。智能避让系数将取决于是否接近障碍物，安全系数将表示驾驶员的能力，辅助系数将取决于驾驶时间和疲劳度。传感器系统会影响轮椅的运动，对这些区域进行补偿。这是第一次尝试这样做。不同的人工智能系统将被用于不同的任务，以在不同的环境中利用它们各自不同的优势。将使用基于人工神经网络结构的原始层次结构来集成它们。将使用至少三种人工智能技术为轮椅选择行动方案，并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需求来确定最佳行动方案。每个系统将为潜在的行动路线提供一定程度的信心，例如左转、停车等。DMS将确定要采取的行动。这个EPSRC项目将生产新设备和将设备集成到轮椅中的新方法，以确保安全导航和个性化辅助，总体成本低但可自动调整的解决方案，使系统定制和实时适应。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，提供一种经济高效的方式来提高生活质量和独立性。

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.