A key aspect of decentralized multirobot coordination is communication. However, beyond simple signaling, there are only few reports in the literature on the successful evolution of communication, with successes largely dependent on specific tasks and evolutionary setups. Thus, there is a lack of standardized communication frameworks that can be applied to different tasks without the need to redesign, rebuild, or re-evolve the entire system for every new task. In this article, we propose a novel communication module that does not need to be modified for its use in different tasks. Each robot has a coordinate (state) in a virtual communication space. The communication space is partitioned into virtual regions, and each region is linked to a physical behavior, such as seeking resources, phototaxis, or recharging the battery. A robot's individual behavior is determined by the region to which its current communication state belongs. Since robots can navigate the communication space and continually broadcast their coordinates to neighbors within range, robot swarms can effectively coordinate their behavior in a self-organized manner. We demonstrate that the same evolved communication module is effective in three swarm robotics tasks: 1) the physical aggregation of the robots into groups of a desired size; 2) the formation of desired swarm geometries; and 3) a foraging task based on temporal role allocation. The results show that the communication module provides good and scalable performance in all tasks, representing a significant step toward a task-agnostic communication framework for robot swarms.

Recent advancements in gait rehabilitation have led to the development of innovative approaches that complement traditional therapeutic methods. These include intense, task-specific exercise strategies, non-invasive treatments, surgical interventions, and advanced robotic technologies. While robotic systems for adult gait rehabilitation are well-established, there is a notable scarcity of such devices for pediatric patients, especially toddlers, due to their unique developmental and biomechanical needs. This work introduces Discover2Walk (D2W), a novel robotic platform designed specifically for pediatric gait rehabilitation in small children. The D2W platform features a multi-module, cable-driven architecture guided by an omnidirectional traction module, addressing the limitations of current rehabilitation devices for younger populations. The platform's modular design consists of three actuated modules—pelvic, ankle, and traction—synchronized by a personalised gait pattern generator. This configuration allows for simultaneous control and monitoring of pelvic and ankle motion using partial body weight support and Assistance As Needed (AAN) approaches. Preliminary evaluations were conducted with pediatric patients with Cerebral Palsy, involving two ambulatory six-year-olds and one non-ambulatory four-year-old, over a series of 10 gait rehabilitation sessions. Data analysis from the ambulatory children showed a decrease in the robotic effort required to assist limb movements along healthy trajectories throughout the sessions, accompanied by an increase in walking speeds. Further work will include expanding the patient cohort to include a broader range of ages, sizes and GMFCS levels to validate the system's effectiveness across a wider spectrum of pediatric gait disabilities and validating the traction effectiveness.

Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long short-term memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexion-extension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32--56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.

The coordination of multiagent systems in real environments receives considerable attention from research and industry. The design of coordination mechanisms should take into account the nature of the environment where the system is embedded. In this paper, the multiagent system is in an environment that features periodic properties. This environment is approached from a signal processing point of view to extract such properties. The coordination is performed by the proposed multifrequency-coupled oscillators (MuFCO) algorithm. It addresses the coordination of the multiagent system as a distributed collective synchronization mechanism. An operation example of MuFCO algorithm is shown, where it is used to coordinate consumptions in a smart grid. Thanks to the MuFCO algorithm, the multiagent system can be used to smooth the aggregated consumption of an electrical grid in a distributed way.

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

We have designed and built a new open hardware/software board that lets miniaturized robots communicate and at the same time obtain the range and bearing of the source of emission. The open E-puck Range & Bearing board improves an existing infrared relative localization/communication software library (libIrcom) developed for the e-puck robot and based on its on-board infrared sensors. The board allows the robots to have an embodied, decentralized and scalable communication system. Its use and capabilities are demonstrated via an alignment experiment.