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HM planner: the hybrid planning architecture for non-holonomic differential robots. The HM planner includes a global planner based on an A* algorithm with interpolation to determine the 3D path (x, y, θ ) to a final goal; the smoothing algorithm based on the elastic bands methodology and the local planner D-DWA are then applied iteratively to the global plan during its execution. The D-DWA is applied to two 2D global paths based on the 3D path originated previously, being one in relation to the robot center of mass, and the other one in relation to a middle point located at the front of the robot.
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This article presents a new hybrid motion (HM) planner, designed to allow robust indoor navigation in constrained environments of nonholonomic differential robots, such as RobChair, the brain-actuated robotic wheelchair [1] from the Institute of Systems and Robotics, University of Coimbra, Portugal. Relying on this new planning algorithm, RobChair...
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Context 1
... planning strategy was established to allow robust indoor nav- igation of non-holonomic and non-circular mobile robots in constrained environments, such as RobChair. Figure 3 shows the HM planner architecture in detail. The global planner consists of a modified version of the well-known search-based A* algorithm, which was expanded to a 3D-path planner, s i = [x i , y i , θ i ], using the interpolation module. ...
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... proposed planner uses three different costmaps, namely: a 2D global costmap, a low resolution local costmap, and a high resolution local costmap (see Fig. 3). The 2D global costmap is a low resolution costmap, with a resolution desig- nated by mg res , obtained from the a priori metric map, and therefore includes all the static obstacles provided in that map. It also includes the obstacles detected by the laserscanner in a radius designated by o radius . Obstacles are inflated in a ...
Citations
... Japanese scholars achieved control of an intelligent wheelchair combining electromyography and eye-tracking EEG [29], further expanding the range of human-machine interaction utilizing EMG signals. The RIKEN Brain Science Institute (BSI) and Toyota Collaboration Center in Japan subsequently developed intelligent wheelchairs controlled via BCI [30][31][32]. Users wear sensors to detect brainwave signals and can automatically adjust parameters through self-learning to accommodate different operators. The accuracy of operation exceeds 95%, achieving the goal of controlling the intelligent wheelchair through conscious thought and advancing the wheelchair towards greater intelligence. ...
Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain–machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.
... As described in [6] a robotic walker can have multiple functions to support different problems in the elderly locomotion. The robotic rollator can support the human's mobility, increase safety and self-empowerment, compensate unbalanced gait, aid during the sitting or getting up phases and be used for rehabilitation [7,8]. ...
Robot-assisted navigation is a perfect example of a class of applications requiring flexible control approaches. When the human is reliable, the robot should concede space to their initiative. When the human makes inappropriate choices the robot controller should kick-in guiding them towards safer paths. Shared authority control is a way to achieve this behaviour by deciding online how much of the authority should be given to the human and how much should be retained by the robot. An open problem is how to evaluate the appropriateness of the human’s choices. One possible way is to consider the deviation from an ideal path computed by the robot. This choice is certainly safe and efficient, but it emphasises the importance of the robot’s decision and relegates the human to a secondary role. In this paper, we propose a different paradigm: a human’s behaviour is correct if, at every time, it bears a close resemblance to what other humans do in similar situations. This idea is implemented through the combination of machine learning and adaptive control. The map of the environment is decomposed into a grid. In each cell, we classify the possible motions that the human executes. We use a neural network classifier to classify the current motion, and the probability score is used as a hyperparameter in the control to vary the amount of intervention. The experiments collected for the paper show the feasibility of the idea. A qualitative evaluation, done by surveying the users after they have tested the robot, shows that the participants preferred our control method over a state-of-the-art visco-elastic control.
... During navigation tasks, global path planning sets the optimal collision-free path for the robot to reach the target from the current position without considering the motion constraints. DWA is one of the local navigation methods [25] widely used in navigation tasks [27,33]. The collision-free path is calculated based on dynamic windows. ...
... For local autonomous navigation, it is essential to consider shared control velocity during trajectory evaluation. The traditional DWA method could plan safe and reachable trajectories with nonholonomic constraints [25,33]. In this work, the BSC-DWA method integrating the shared control velocity cost was used to conduct velocity sampling, trajectory simulation, and evaluation. ...
... The brain-driven mobile robot is a typical and important BCI system that provides a flexible and extensible approach to daily assistance. In many studies, brain-driven mobile platforms-including wheelchairs [4,33,41], mobile robots [7,10,16,42], and high-speed vehicles [43]-have been explored and investigated. These studies have shown great promise in coordination and shared autonomy between humans and robots. ...
Although the electroencephalography (EEG) based brain–computer interface (BCI) has been successfully developed for rehabilitation and assistance, it is still challenging to achieve continuous control of a brain-actuated mobile robot system. In this study, we propose a continuous shared control strategy combining continuous BCI and autonomous navigation for a mobile robot system. The weight of shared control is designed to dynamically adjust the fusion of continuous BCI control and autonomous navigation. During this process, the system uses the visual-based simultaneous localization and mapping (SLAM) method to construct environmental maps. After obtaining the global optimal path, the system utilizes the brain-based shared control dynamic window approach (BSC-DWA) to evaluate safe and reachable trajectories while considering shared control velocity. Eight subjects participated in two-stage training, and six of these eight subjects participated in online shared control experiments. The training results demonstrated that naïve subjects could achieve continuous control performance with an average percent valid correct rate of approximately 97 % and an average total correct rate of over 80 %. The results of online shared control experiments showed that all of the subjects could complete navigation tasks in an unknown corridor with continuous shared control. Therefore, our experiments verified the feasibility and effectiveness of the proposed system combining continuous BCI, shared control, autonomous navigation, and visual SLAM. The proposed continuous shared control framework shows great promise in BCI-driven tasks, especially navigation tasks for brain-driven assistive mobile robots and wheelchairs in daily applications.
... Moreover, within the research area of assisted mobility and robotic wheelchairs, incorporating autonomous navigation behaviors is a fundamental topic to be solved by the research community, as it is considered essential for applications in actual environments [10,11,26,27]. ...
Under the umbrella of assistive technologies research, a lot of different platforms have appeared since the 1980s, trying to improve the independence of people with severe mobility problems. Those works followed the same path coming from the field of robotics trying to reach users’ needs. Nevertheless, those approaches rarely arrived on the market, due to their specificity and price. This paper presents a new prototype of an intelligent wheelchair (IW) that tries to fill the gap between research labs and market. In order to achieve such a goal, the proposed solution balances the criteria of performance and cost by using low-cost hardware and open software standards in mobile robots combined together within a modular architecture, which can be easily adapted to different profiles of a wide range of potential users. The basic building block consists of a mechanical chassis with two electric motors and a low-level electronic control system; driven by a joystick, this platform behaves similar to a standard electrical wheelchair. However, the underlying structure of the system includes several independent but connected nodes that form a distributed and scalable architecture that allows its adaptability, by adding new modules, to tackle autonomous navigation. The communication among the system nodes is based on the controller area network (CAN) specification, an extended standard in industrial fields that have a wide range of low-cost devices and tools. The system was tested and evaluated in indoor environments and by final users in order to ensure its usability, robustness, and reliability; it also demonstrated its functionality when navigating through buildings, corridors, and offices. The portability of the solution proposed is also shown by presenting the results on two different platforms: one for kids and another one for adults, based on different commercial mechanical platforms.
... On the basis of the global path, the DWA algorithm is used to calculate the local path in real time for execution according to the information captured using the sensor. The DWA [16] can correct the difference between the real scene information and the global map information to some extent. However, today's diverse scenarios may include several dynamic obstacles in the environment, such as cats, dogs, people not paying attention to the robot, and those with disabilities. ...
With the continuous development of robotics and artificial intelligence, robots are being increasingly used in various applications. For traditional navigation algorithms, such as Dijkstra and A*, many dynamic scenarios in life are difficult to cope with. To solve the navigation problem of complex dynamic scenes, we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present. The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output. The algorithm enhances the robots' ability to actively avoid obstacles while retaining the adaptability of traditional methods.
... In the brain-machine interface (BMI) community there are inference systems that leverage knowledge to discover and exploit the user goal while executing a manipulation task [22], [23], and planning systems where the user inputs a goal that the robot plans and later executes by itself [24]. In a similar direction as us, albeit in the domain of wheelchair navigation, Lopes et al. [25] use a hybridplanning framework to create a grid the user can traverse with a BMI. In the teleoperation literature there has been research in adaptive movement constraints (virtual fixtures) [26] and on-the-fly goal-oriented pedagogical task demonstrators for a space robot [27]. ...
From robotic space assistance to healthcare robotics, there is increasing interest in robots that offer adaptable levels of autonomy. In this paper, we propose an action representation and planning framework that is able to generate plans that can be executed with both shared control and supervised autonomy, even switching between them during task execution. The action representation -- Constraint Action Templates (CATs) -- combine the advantages of Action Templates (Leidner, 2019) and Shared Control Templates (Quere, 2020). We demonstrate that CATs enable our planning framework to generate goal-directed plans for variations of a typical task of daily living, and that users can execute them on the wheelchair-robot EDAN in shared control or in autonomous mode.
... Two RWs were able to detect the step/stairs and adjust in both the pitch and roll direction [221,229]. Four RW primarily used for indoor obstacle and navigation abilities in controlled environments [230,231], and two RWs with step climbing capabilities met autonomy level 3 [229,232]. The interaction and communication were limited to visual feedback to the user for navigation and path planning. ...
Robotic wheelchair research and development is a growing sector. This article introduces a robotic wheelchair taxonomy, and a readiness model supported by a mini-review. The taxonomy is constructed by power wheelchair and, mobile robot standards, the ICF and, PHAATE models. The mini-review of 2797 articles spanning 7 databases produced 205 articles and 4 review articles that matched inclusion/exclusion criteria. The review and analysis illuminate how innovations in robotic wheelchair research progressed and have been slow to translate into the marketplace.
... Due to several types of impairments, there are a significant number of people unable to perform daily tasks. Hence, a particular type of assistive mobile robot, robotic wheelchair platforms, has been researched aiming to increase the autonomy and mobility of such users [21,22]. Brain-actuated wheelchairs [21,23,24] have also received particular focus in research, with several promising techniques for severely motor disabled people who are unable to control a robotic platform by the conventional interfaces, such as joystick [21,25]. ...
... Hence, a particular type of assistive mobile robot, robotic wheelchair platforms, has been researched aiming to increase the autonomy and mobility of such users [21,22]. Brain-actuated wheelchairs [21,23,24] have also received particular focus in research, with several promising techniques for severely motor disabled people who are unable to control a robotic platform by the conventional interfaces, such as joystick [21,25]. With the advances in Brain-Computer Interfaces (BCI) and shared control methods, new paradigms of the brain-computer interaction that allow the user to choose his navigation target have been proposed. ...
... Hence, a particular type of assistive mobile robot, robotic wheelchair platforms, has been researched aiming to increase the autonomy and mobility of such users [21,22]. Brain-actuated wheelchairs [21,23,24] have also received particular focus in research, with several promising techniques for severely motor disabled people who are unable to control a robotic platform by the conventional interfaces, such as joystick [21,25]. With the advances in Brain-Computer Interfaces (BCI) and shared control methods, new paradigms of the brain-computer interaction that allow the user to choose his navigation target have been proposed. ...
Multi-Object Tracking (MOT) techniques have been under continuous research and increasingly applied in a diverse range of tasks. One area in particular concerns its application in navigation tasks of assistive mobile robots, with the aim to increase the mobility and autonomy of people suffering from mobility decay, or severe motor impairments, due to muscular, neurological, or osteoarticular decay. Therefore, in this work, having in view navigation tasks for assistive mobile robots, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, is presented. To improve the data association of both methods, which are solved as a linear assignment problem with a generated cost matrix, a set of new object tracking data association cost matrices based on intersection over union, Euclidean distances, and bounding box metrics is proposed. For the evaluation of the MOT by detection in a real-time pipeline, the YOLOv3 is used to detect and classify the objects available on images. In addition, to perform the proposed evaluation aiming at assistive platforms, the ISR Tracking dataset, which represents the object conditions under which real robotic platforms may navigate, is presented. Experimental evaluations were also carried out on the MOT17 dataset. Promising results were achieved by the proposed object tracking data association cost matrices, showing an improvement in the majority of the MOT evaluation metrics compared to the default data association cost matrix. In addition, promising frame rate values were attained by the pipeline composed of the detector and the tracking module.
... However, many of them become unable to use conventional interfaces, as a result of impairment severity or physical ability deterioration [2]. For those with severe motor impairments, brain-computer interfaces (BCIs) may be an alternative solution as it is possible to send commands through brain signals without requiring muscle activity [3], [4], [5], [6]. Yet, using a BCI to control a robotic wheelchair is a very challenging task because BCI has low transfer rates and limited accuracy [7]. ...
... For this reason, when compared to other BCI applications such as spellers or games, BCWs require much higher reliability and general usability, which is only possible if they integrate an assistive navigation system (ANS) that perceives the wheelchair's surroundings and performs suitable and smooth trajectories, considering the user intents. This can be accomplished by combining user and machine outputs in a so-called collaborative controller [9], [5], [10], [6], [11], allowing BCI commands, which encode highlevel goals, to be provided at sparse intervals without the need for precise, low-level continuous steering. ...
... Self-paced control (also known as asynchronous control) provides the possibility for users to send BCI commands only when they wish to, at their own pace. This is therefore a very desirable feature, which can lead to less mental effort and more natural driving interaction [14], [5], [15], [6], [16], [17], [18]. To implement a self-paced BCI, the system must automatically recognize control and noncontrol states. ...
Brain-controlled wheelchairs (BCWs) are a promising solution for severely motor-disabled people who cannot use conventional interfaces. Compared to other brain-computer interfaces (BCIs), e.g., spellers and games, BCWs are more complex, combining BCI and assistive navigation systems (ANS), and require higher levels of reliability and usability to ensure safety and natural human-machine interaction. This paper proposes a P300-based BCW that combines collaborative-control, self-paced control, and dynamic-time commands, supported by an ANS, to achieve natural and minimumeffort driving. The proposed approach was tested by 7 ablebodied participants and 6 participants with motor disabilities, steering a robotic wheelchair (RobChair) in real office-like
environments. These 2 groups controlled the self-paced BCI with an accuracy of 96.7% and 93.5%, respectively. Combining the BCI and the collaborative controller, BCW’s final driving accuracy has increased to over 99% in both groups. The selfpaced approach decreased the number of required commands from 73 to 10. This drastic reduction allowed users to be 86.3% of the time relaxed in a non-control state. The dynamic-time
commands approach was effective showing the possibility of adjusting the BCI speed versus performance. Subjective results assessed through questionnaires showed that the proposed selfpaced control significantly decreased the mental demand and effort. Participants reported a high level of satisfaction regarding pleasantness, naturalness, difficultness, corroborating the quantitative results. We conclude that the proposed approach has demonstrated high feasibility for able-bodied and motor-disabled participants, and represents a relevant contribution to improve the usability
of BCWs, moving toward their potential use by target users in home settings.
... The results of the tests showed an improvement in navigation efficiency of both methods when compared to unassisted mobile platforms. Still in an assistive context, a Double DWA (D-DWA) approach was proposed [18] that provides smooth movements and effective traversal of tight spaces. In [8], an approach for assisted navigation of visually impaired individuals through a Simulated Passivity approach of the FriWalk is presented. ...
This work proposes a robot-assisted navigation approach based on user intent adjustment, in the context of robotic walkers. Walkers are prescribed to users with gait disorders so that they can support their body weight on the upper limbs, however, the manipulation of such devices can be cumbersome for some users. Common problems for the users are lack of dexterous upper limb control and visual impairments. These problems can render walkers’ users helpless, making them unable to operate these devices effectively and efficiently. We present a new approach to robot-assisted navigation using a utility decision and safety analysis procedure with user intent adjustments learned by reinforcement learning (RL) and supported on a rapidly-exploring random tree inspired algorithm. The proposed approach offers full control of the assistive platform to the user until obstacles are detected. In dangerous scenarios, corrections are computed in order that the assistive platform avoids collisions and follows social norms, effectively guiding the user through the environment while enforcing safer routes. The experimental validation was carried out in a virtual environment and in a real world scenario using a robotic walker built in our lab (ISR-AIWALKER). Experimental results have shown that the proposed approach provides a reliable solution to the robot-assisted navigation of a robotic walker, in particular the use of utility theory to evaluate candidate motions together with a RL model increases the safety of the user’s navigation.
