Co-designing Robot Dogs with and for Neurodivergent Individuals: Opportunities and Challenges

Purpose Gait rehabilitation is a fundamental requirement to avoid disuse syndrome for the long-term hospitalized person such as cerebral infarction patient or patient with dementia. In general gait rehabilitation is monotonous and dull, so some entertainment or a sense of achievement is helpful to maintain motivation during the daily exercise. This paper proposes an effective gait-rehabilitation system using a robotic dog supporting individual initiatives of the patient. Method Walking with a living dog is one way to achieve this; however as an intervention it is limited to mildly symptomatic patients because a real dog risks sudden lunging. We used a robotic dog instead of a living dog for relatively critical patients. We have studied robot-assisted activity and therapy using robotic animals in the elderly nursing home in order to improve residents’ quality of life (QOL). In the case of a man-made robot, aforementioned risks can be ignored. Moreover, it is an advantage that an in-house walking exercise with the dog is possible. The basic concept of this proposal is co-creation of steps between the elderly and dog. That is, the real-time stabilometric signal of the elderly was measured and analyzed to extract a single step and forwarded to the robotic dog to make a step. Since commercially available amusement products pass through the safety review of public administration, the proposed system was composed of those products as a hardware tool. More specifically, entertainment robot AIBO of Sony ERC was adopted as the robotic dog and Nintendo’s Wii-balance-board is used as the stabilometric human interface devices. All signals are transmitted by conventional Bluetooth and LAN systems. Results & Discussion Figure 2 shows the block diagram and enforcement scenes of gaiting exercise at standing and sitting positions, respectively. The patients both suffered from dementia and made their own steps in a positive manner shown by arrows in the figure. Such positive attitude was uncommon in the conventional daily exercise. Functions for walking back and forth walk, and turning right and left are installed. It is noteworthy that the standing patient tempts the dog to move toward another patient and the patient calls the robot to her. This means there a co-creation field is created through the robotic dog. This was followed by a chat between the patients. In the case of the sitting patient, although for her it is very hard exercise to raise her foot from the floor, she made several steps on her own initiative successively after the physical therapist guided her how to walk by patting and lifting her right and left knees alternately. The position of the center of gravity and command signal were logged every 0.1 sec in the control PC and available to be analyzed afterward. For example, the change of exhaustion during exercise was estimated from the magnitude and regularity of steps. Since all hardware tools can be packed in a suitcase the proposed gait exercise can be easily done everywhere.

ABSTRACTThe inclusion of animals and robots in therapeutic interventions for Autism Spectrum Disorder (ASD) has become more common. This study provides a first comparison between the potential of living versus robotic dogs to elicit social communication behavior and regulated emotional responses in individuals with ASD. Ten children and thirteen adults with ASD and severe lan- guage delay were tested for appropriate social communication behavior and cardiac autonomic functioning during a planned, structured interaction with an experimenter alone (no-stimulus condition), an experimenter accompanied by a living dog (dog condition), and an experimenter accompanied by a robotic dog (robot condition). A within-subjects design was followed to expose all participants to all three experimental conditions. Overall, participants (children and adults) showed a higher percentage of appropriate social behaviors in Living and Robotic Dogs as Elicitors of Social Communication Behavior and Regulated Emotional … the dog and the robot conditions than in the no-stimulus condition. In children, the living dog was more effective than the robotic dog in promoting social communication behavior. In adults, no such difference was found between the dog and the robot condition. Only the dog appeared to elicit a positive effect in cardiac autonomic functioning by increasing heart rate variability (HRV) and buffer- ing the decrease in parasympathetic activity due to interaction with the experimenter. The data are preliminary but relevant and warrant replication in larger-scale studies.

Adaptation to technological advancements and intelligent digital tools can enable healthcare providers to overcome the challenges of their patient-oriented care systems and processes. One such intelligent tool is automated assistive robots, which can improve patient care and safety in the health sector. This paper presents an invariant set of continuous nonlinear control laws for an assistive robot and a rehabilitation wheelchair robot modeled as a new autonomous robotic dog and rehabilitation wheelchair system for navigating a highly constrained environment. The control laws are derived from the Lyapunov-based control scheme classified under the umbrella of artificial potential field (APF) methods, and inherently proved stability of the new heterogeneous system. The robotic dog guides the wheelchair robot during the navigation process in a cluttered environment where the avoidances are from the robotic dog and the integrated dynamic protective polygon. The wheelchair traverses the obstacle-free path traced by the dynamic polygon. The leash is flexible, and its length is bounded, which invariably provides the protective polygon to change its intrinsic dimension. Thus, the dual-robot system has increased mobility for obstacle avoidance and passing through narrow passageways. The solution proffered herein is only feasible in a highly constrained and isolated human environment where nothing else appears to be moving in the direction of the robotic dog and wheelchair. The computer simulations and associated convergence graphs present the efficacy of the unique control laws for the new heterogeneous robotic system. Adoption of such control laws and their suitable variants can make a big impact in the healthcare industry.

In the era of rapid development in computer science, artificial intelligence (AI), and the Internet of Things (IoT), robotics has undergone continuous updates and iterations. People have been exploring various application areas for robots, with robotic dogs standing out prominently. These robotic dogs are highly anticipated due to their remarkable attributes of high flexibility, adaptability, and stability. They have become a powerful magnet, attracting numerous technology companies worldwide. The market prospects for robotic dogs are extremely broad, as they can be applied in areas such as companionship, rescue operations, reconnaissance, and logistics. Furthermore, they also generate significant social discourse, making research on robotic dogs highly valuable. This paper provides a brief overview of the application scenarios of robotic dogs in the domains of home companionship and natural disaster rescue. It summarizes the relevant technologies and current developments utilized in these two fields and offers a glimpse into the future applications of robotic dogs.

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot's stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot's own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog's own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains.

In this study, we investigated the enhancement of social-emotional literacy of preschool children in dog-assisted learning situations, in which we involved a robotic dog and a living dog. The paper first reports the results of a short research intervention conducted in spring 2020, which explored how the assistance of a commercial robotic dog and a living dog contribute to the development of social-emotional skills as part of playful learning. Our experimental research intervention involved (n=16) 5-7-year old Finnish children who were divided into two randomly drawn groups. With the guidance of two early childhood educators and the help of the two dogs, preschool children performed tasks related to social-emotional skills designed, implemented, observed and documented by the teachers. Based on the findings of the study, a Model for Enhancing Emotional Literacy with a Robot Dog was developed. With the model, the contribution of our research is in offering new perspectives on how a robotic dog alongside a living dog can be effectively used in early education to support enhancement of emotional literacy.

This paper presents a simple design and construction process of a robot that acts like a sniffer dog, named robotic dog, at the detection of any metals which may be harmful for human lives like bombs or valuable things like keys. The robotic dog (RD) consists of a metal detector system and an autonomous car embedded with an electronic buzzer system so that it acts as a real sniffer dog. The robotic vehicle can be controlled from a safe distance using Bluetooth technology. Based on our experimental studies, it was found that the RD can move freely in any direction. At the detection of metal anywhere, the RD locks itself on that position, the buzzer system starts barking like a dog, and it does not move forward until the metal is removed. This RD may have a wide range of uses because of its simplicity and low cost in construction. Moreover, it has the design flexibility to modify its shape to meet the changes of needs.

This paper proposes an embedded intelligent robot model based on Multi-agent, and implements it on a robot dog platform with embedded, modular components. In order to make the software structure flexible and does not depend on specific hardware, we design and divide robot into few agents by the idea, which is “thinking and learning, perception and interaction, action and control”. What's more, the model takes account of both intelligence and response capability for emergency by deliberative module and reflex module. The mechanism of Case-based Reasoning is applied to realize the deliberation of robot dog. In the simulation and actual integration test, the robot dog is able to walk, avoid obstacles, perceive and process external signals such as voice input and infrared. The results proved that preliminary success was achieved.

Deep learning-based 3D reconstruction of scaffolds using a robot dog

Once upon a time, in a land far, far away, there were three families who each owned a robotic dog. The robotic dogs were a great source of entertainment for their owners: the family members enjoyed playing with them the same way as one would with a real dog. Next to their roles as loyal canine companions, the dogs were also supposed to patrol around the house of the families and protect them from ‘suspect types’,1 such as potential burglars, child molesters and any other unwelcome intruders. The eyes of these robotic dogs registered all the passers-by and stored their image and gait. Whenever a dog spotted a ‘suspect type’ around the family house it would bark, set off the alarm system, contact all family members and call the police. If the ‘suspect type’ continued to approach the house, the dog could set off tear gas. The instruction manual opened with:Congratulations! You have made a great choice. No real dog could ever provide the same level of security as your new robotic pet. No burglar can distract your robotic dog with a simple piece of sausage. The robotic dog will never waggle its tail at an unwelcome guest, because you instruct it exactly about who should be kept away from your family. Robo-dog has an extremely user-friendly interface: just talk to the dog as you would to a child. 2 And which real dog would be able to call the police if they spot a suspicious type? Precisely. But your robotic dog will.

To increase the flexibility performance of robot dog, dynamic performance matching of power equipment in four-footed robot dog is made. Demand for power and configure in robot dog power equipment is briefly analyzed, improved backward power match between gasoline and pump is presented, power and flow rate match between gasoline and pump is calculated, control strategy based on pump pressure feedback is applied into gasoline engine control in order to save energy. With experimental bench test, experimental results showed that energy saving matching of power equipment can meet the requirement of fast dynamic meditation for robot dog .

Soft robots are compliant, impact resistant, and relatively safe in comparison to hard robots. However, the development of untethered soft robots is still a major challenge because soft legs cannot effectively support the power and control systems. Most untethered soft robots apply a crawling or walking gait, which limits their locomotion speed and mobility. This paper presents an untethered soft robot that can move with a bioinspired dynamic trotting gait. The robot is driven by inflatable soft legs designed on the basis of the pre-charged pneumatic (PCP) actuation principle. Experimental results demonstrate that the developed robot can trot stably with the fastest speed of 23 cm/s (0.97 body length per second) and can trot over different terrains (slope, step, rough terrain, and natural terrains). The robotic dog can hold up to a 5.5 kg load in the static state and can carry up to 1.5 kg in the trotting state. Without any rigid components inside the legs, the developed robotic dog exhibits resistance to large impacts, i.e., after withstanding a 73 kg adult (46 times its body mass), the robotic dog can stand up and continue its trotting gait. This innovative robotic system has great potential in equipment inspection, field exploration, and disaster rescue.

The study proposes a multi-sensor localization and real-timeble mapping method based on the fusion of 3D LiDAR point clouds and visual-inertial data, which addresses the issue of decreased localization accuracy and mapping in complex environments that affect the autonomous navigation of robot dogs. Through the experiments conducted, the proposed method improved the overall localization accuracy by 42.85% compared to the tightly coupled LiDAR-inertial odometry method using smoothing and mapping. In addition, the method achieved lower mean absolute trajectory errors and root mean square errors compared to other algorithms evaluated on the urban navigation dataset. The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. When applied to a real robot dog, the rotational error was reduced to 1.86°, and the localization error in GPS environments was 0.89m. Furthermore, the proposed approach closely followed the theoretical path, with the smallest average error not exceeding 0.12 m. Overall, the proposed technique effectively improves both autonomous navigation and mapping for robot dogs, significantly increasing their stability.

To meet the control demand of power equipment in robot dog, development on control system for robot dog is made. Based on the brief analysis of demand in power and configure in power assembly of robot dog, whole design sketch of control system for power is presented, electronic control system based on MC9S12DP512 microprocessor and neural control algorithm for network structure PID is found. Control algorithm and control effect of control system is verified in experimental bench, experimental results showed that designed control system control effect is stable and it can meet the design requirement in transient speed adjusting and stable speed fixing.

In the United States, approximately one-fourth of elderly individuals are at risk of social isolation. Additionally, an average of 32,000 deaths occurs every year due to accidental falls for people over the age of sixty-five. Companion robots can address loneliness and safety concerns among the elderly. Modifying a robot dog with canine attributes fosters connection, warmth, and trust, enhancing relational dynamics. These assistive robots can improve the quality of life for the elderly by supporting mobility, communication, and daily activities. This paper proposes an improved scheme for the Boston Dynamics robot dog, Spot Explorer, designed for the elderly. Spot is controlled using a Raspberry Pi 4 Model B with a customized PyAutoGUI algorithm in Python, enabling wireless interaction and featuring a user-friendly fail-safe component via a keyboard. To test the potential use of obstacle avoidance and human-robot interaction, the preliminary results, analysis of variance, and linear regression of the ultrasound sensor showcased the experimental data on par with the prescribed range. This paper also proposes a user-friendly payload for Spot that was simulated through COMSOL Multiphysics and validated with software innovation and hardware integration for the elderly population. Future work will involve the integration and validation of all sensors with Spot to create a friendly robot dog that could improve the quality of life for senior citizens.