While wearable robots that utilize intrinsically soft materials for actuation offer enhanced safety and biological compatibility, the challenges of sensing and control significantly affect their performance. The control problem in such systems is inherently complex, and the inclusion of 'softness' introduces additional nonlinearities, hysteresis, and uncertainties. Furthermore, the effectiveness of control strategies is highly dependent on sensor selection and integration, which presents its own challenges. Most robotic systems require separate sensors for control purposes. In this study, a new sensing and control scheme are introduced for soft wearable robots, leveraging the intrinsic soft-sensing capability of fluidic filament actuators without adding computational complexity. This method enables simultaneous sensing and actuation with$\mathbf{9 6 \%}$position accuracy, even under physical disturbances. This approach is demonstrated with a soft assistive device for elbow flexion/extension, achieving 70.5% tracking accuracy and a 0.09s response delay to human intention, ensuring the system provides minimal resistance when assistance is not needed, while delivering the required support when necessary.

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

This paper proposes a human approaching robot navigation framework that enables a mobile service robot to (i) estimate a socially optimal approaching pose, and (ii) navigate safely and socially to the estimated approaching pose. In the first stage, the robot estimates potential approaching poses of a human or a human group, which the robot can safely and socially approach, using the dynamic social zone model. In the second stage, the proposed framework selects a socially optimal approaching pose, then estimate a socially optimal trajectory of the robot using the proposed goal-oriented timed elastic band (GTEB) model. The developed GTEB model takes into account the current robot’s states, robot dynamics, dynamic social zone, regular obstacles and potential approaching poses to generate the socially optimal robot trajectory from the robot’s current pose to the selected optimal approaching pose. The motion control command extracted from the socially optimal trajectory is then utilized to drive the mobile robot to approach the individual humans or human groups, while safely and socially avoiding regular obstacles, human and human groups during the navigation process. The proposed approaching human framework is verified in the both simulation and real robots. The results illustrate that, the mobile robot equipped with our developed GTEB model is able to safely and socially approach and avoid individual humans and human groups, while guaranteeing the comfortable safety for the humans and socially acceptable behaviors for the robot. Note to Practitioners—Although our proposed GTEB model is capable of estimating a socially optimal approaching pose and social robot trajectory, driving the robot to approach a human and a human group, and providing the safety and comfort for humans and socially acceptable behaviors of the robot, there exists a few drawbacks if we wish to apply the proposed approaching human framework in dynamic social environments. First, the optimizer for the GTEB model should be improved in terms of computational time and accuracy to avoid generating unpredictable robot trajectories, especially in dynamic social environments. Second, the socio-spatio-temporal characteristics of the humans including human position, motion and orientation, and human group and human–object interactions play an important role in the proposed GTEB model. However, the existing techniques are only suitable in quasi-dynamic social environments. Hence, highly accurate, robust and real-time algorithms for human detection and tracking, and social interaction detection are necessary. Third, social interactive intentions such as human–robot, human–human and human–object interactive intentions should be predicted and incorporated into the proposed framework to improve the performance of the developed framework in the dynamic social environments. Last but not least, human and human group identification algorithms should be proposed to enable the robot to identify the humans, whose the mobile robot is requested to approach. In the future, the social interactive intentions, the human’s future states and its trajectories and will be predicted using deep learning algorithms and incorporated into the approaching human framework to improve the performance of the proposed framework.

This paper presents a simple but compact design of a bicycle-like robot for inspecting complex-shaped ferromagnetic structures. The design concept for versatile locomotion relies on two independently steered magnetic wheels formed in a bicycle-like configuration, allowing the robot to possess multi-directional mobility. The key feature of a reciprocating mechanism enables the robot to change its shape when passing obstacles. A dynamic joint of the robot configuration makes it naturally adapt to uneven and complex surfaces of steel structures. We demonstrate the usability and practical deployment of the robot for steel thickness measurement using an ultrasonic sensor.

In this paper we propose a new method of encoding social norms and rules into sampling-based motion planners. Inspired from the social force model (SFM), we modify and use it as a social intention model (SIM) to reshape the motion primitives (MP) of the rapidly-exploring random tree (RRT) motion planner for the socially aware robot navigation. We also introduce a new benchmark for evaluating social planning performance, so called as the social effort index (SEI). The experimental results show that the socially-guided motion primitives-based RRT increases safe and social interactions between the robot and human agents about 50% compared to the typical RRT-embedded MP (RRT-MP) in human populated environments.

In this paper, we address a novel hierarchical distributed control (HDC) strategy for networked multirobot systems (MRSs). This strategy is developed on a geometric approach without requiring estimation of algebraic connectivity. It is originally based upon behavioral control, but upgraded by distributed node control with a mobility constraint for global network integrity preservation and distributed connectivity control with a local connectivity minimization strategy for network coverage expansion. Thanks to properties of HDC, a networked MRS is capable of achieving high performance with cooperative tasks. We have examined and evaluated our proposed method in both simulations with up to 100 simulated robots and real-world experiments with up to 14 real robots.

This paper addresses the circular formation and concentric formation stabilization problem of kinematic unicycles. We design distributed control laws driving unicycles to converge to a common circle at the first stage and a velocity control law enabling unicycles to achieve a specific formation on the circle at the second stage. We also achieve the concentric formation by dividing unicycles into groups and design distributed control laws reinforcing each group at a desired circular orbit from the stationary center. We provide analysis to show that both the circular and concentric formations are asymptotically stable using set stabilization theory. Typical examples are selected to demonstrate and support the theoretical results.

Safe and social navigation is the key to deploying a mobile service robot in a human-centered environment. Widespread acceptability of mobile service robots in daily life is hindered by robot's inability to navigate in crowded and dynamic human environments in a socially acceptable way that would guarantee human safety and comfort. In this paper, we propose an effective proactive social motion model (PSMM) that enables a mobile service robot to navigate safely and socially in crowded and dynamic environments. The proposed method considers not only human states (position, orientation, motion, field of view, and hand poses) relative to the robot but also social interactive information about human-object and human group interactions. This allows development of the PSMM that consists of elements of an extended social force model and a hybrid reciprocal velocity obstacle technique. The PSMM is then combined with a path planning technique to generate a motion planning system that drives a mobile robot in a socially acceptable manner and produces respectful and polite behaviors akin to human movements. Note to Practitioners-In this paper, we validated the effectiveness and feasibility of the proposed proactive social motion model (PSMM) through both simulation and real-world experiments under the newly proposed human comfortable safety indices. To do that, we first implemented the entire navigation system using the open-source robot operating system. We then installed it in a simulated robot model and conducted experiments in a simulated shopping mall-like environment to verify its effectiveness. We also installed the proposed algorithm on our mobile robot platform and conducted experiments in our office-like laboratory environment. Our results show that the developed socially aware navigation framework allows a mobile robot to navigate safely, socially, and proactively while guaranteeing human safety and comfort in crowded and dynamic environments. In this paper, we examined the proposed PSMM with a set of predefined parameters selected based on our empirical experiences about the robot mechanism and selected social environment. However, in fact a mobile robot might need to adapt to various contextual and cultural situations in different social environments. Thus, it should be equipped with an online adaptive interactive learning mechanism allowing the robot to learn to auto-adjust their parameters according to such embedded environments. Using machine learning techniques, e.g., inverse reinforcement learning [1] to optimize the parameter set for the PSMM could be a promising research direction to improve adaptability of mobile service robots in different social environments. In the future, we will evaluate the proposed framework based on a wider variety of scenarios, particularly those with different social interaction situations and dynamic environments. Furthermore, various kinds of social cues and signals introduced in [2] and [3] will be applied to extend the proposed framework in more complicated social situations and contexts. Last but not least, we will investigate different machine learning techniques and incorporate them in the PSMM in order to allow the robot to automatically adapt to diverse social environments.

This paper presents a novel approach of rendezvous trajectory generation for mobile robots. Rendezvous is a special study of two robots or more moving to meet at a previously agreed location. In multi-robot systems, rendezvous plays a significant role in carrying out different missions such as formation performance, collective tasks, swarming behaviours, self-assembly, self-sharing energy, self-recharging energy, etc. In this paper, we investigate a method of trajectory generation, which makes robot performing energy trophallaxis. The proposed method is a kinematic-based model that fully satisfies dynamic constraints of mobile robots as well as desirability of the final robot postures at the rendezvous point. Simulation results of the generated trajectory are provided to demonstrate the possibility of this approach.

The paper considers trophallaxis in robot swarms, which is presented as a concept for resource sharing among individuals inspired by altruistic behaviour in natural populations. A number of elementary problems are identified, such as energy containment, robot morphology, rendezvous motion control and individual resource exchange behaviour. The CISSBot is presented as a design study as well as a proof of concept illustrating how a trophallactic exchange mechanism may be implemented based on batteries in commercially available form factor. A collison free proximity motion control is presented and illustrated by numerical simulation results. A probabilistic Markovian model including relevant effects; mobility, charging, battery exchange and energy consumption is presented and illustrated with numerical examples.

Trophallaxis is a natural phenomenon, biologically observed from social insects or vertebrate animals, to exchange food between colony members. This paper describes a new robotic concept, “Randomized Robot Trophallaxis”, based on a group of autonomous mobile robots with capabilities of self-refueling energy and self-sharing energy. We firstly clarify the concept “Randomized Robot Trophallaxis” by given examples of some natural animal societies. We continuously put emphases on mechatronics implementation of the trophallactic robots. Simulation results are described to point out considerable advantages of trophallactic features when deploying multiple mobile robots latter. The paper is ended with discussion on the concept as well as future vision.

Research of autonomous mobile robots has mostly emphasized interaction and coordination that are naturally inspired from biological behavior of birds, insects, and fish: flocking, foraging, collecting, and sharing. However, most research has been only focused on autonomous behaviors in order to perform robots like animals, whereas it is lacked of determinant to those behaviours: energy. Approaching to cluster animal and the higher, collective and sharing food among individuals are major activity to keep society being. This paper issues an approach to sociable robots using self-maintained energy in cooperative mobile robots, which is dominantly inspired from swarm behavior of collecting and sharing food of honey-bee and ant. Autonomous mobile robots are usually equipped with a finite energy, thus they can operate in a finite time. To overcome the finitude, we describe practical deployment of mobile robots that are capable of carrying and exchanging fuel to other robots. Mechanism implementation including modular hardware and control architecture to demonstrate the capabilities of the approach is presented. Subsequently, the battery exchange algorithm basically based on probabilistic modeling of total energy on each robot located in its local vicinity is described. The paper is concluded with challenging works of chain of mobile robots, rescue, repair, and relation of heterogeneous robots

Research of sociable robots has emphasized interaction and coordination of mobile robots with inspiration from natural behavior of birds, insects, and fish: flocking, foraging, collecting, sharing and so forth. However, the animal behaviors are looking for food towards survival. In an animal society, collecting and sharing are experimentally recognized as the highest property. This paper issues an approach to sociable robots using self-maintained energy in robot society, which is naturally inspired from swarm behavior of honey-bee and ant. Typically, autonomous mobile robots are usually equipped with a finite energy, thus they can operate in a finite time. To overcome the limitation, we describe practical deployment of a group of mobile robot with the possibility of carrying and exchanging fuel, e.g. battery to other robots. Early implementation that includes modular hardware and control architecture to demonstrate the possibility of the approach is presented. Subsequently, the battery exchange algorithm basically based on probabilistic modeling of total energy on each robot located in local vicinity is described.