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和发明新的共享控制系统和人工智能（AI），以显着和积极的影响，对当前和潜在的电动轮椅使用者的生活。最近开发的传感器将被数字化，然后以新的方式与AI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，并将使许多其他人更容易驾驶和转向。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的行动机会。对于一些人来说，这将是第一次提供流动性。获得独立的流动性对自尊和幸福感很重要。自然的独立行动能力，如爬行和行走，通常在生命的头两年获得;如果没有发生，那么人们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启动的移动性，但它不能被引入，除非一个人有空间意识，身体能力和认知技能来理解这个概念。能够自行运输对总体发展有着不可低估的积极影响。朴茨茅斯大学的研究已经产生了模拟碰撞避免和省力系统，这样人们就可以更长时间地驾驶。在Chailey传统基金会的工作创造了轨道系统来引导轮椅和新颖的系统，可以沿着与墙壁平行的路径和传感器来安全地检测环境。所有的设备都将被重新设计为数字系统，将它们与专家系统连接起来，以改善控制。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震颤，以进一步改善控制。这将使最终用户能够在不需要帮助的情况下驾驶电动轮椅，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被不断评估，以便可以为传感器系统和人类驾驶员自动设置控制增益。这将通过计算取决于能力，疲劳，最近的驾驶表现等的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程度，安全因素将表示驾驶员的能力，辅助因素将取决于驾驶和疲劳所花费的时间。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，以在不同的情况下利用其各自的独特优势。基于人工神经网络结构的原始层次结构将用于集成它们。至少有三种人工智能技术将用于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要求来确定最佳行动方案。每个系统都将为潜在的行动过程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。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，具有一般低成本但可自动调节的解决方案，使系统定制和真实的适应性。这将有助于确保用户实现最大的功能。这些设备可以添加到现有的轮椅上，提供一种提高生活质量和独立性的经济有效的方式。

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.