Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots.

Swarm robotics focuses on decentralised control of large numbers of simple robots with limited capabilities. Decentralised control in a swarm system requires a reliable communication link between the individuals that is able to provide linear and angular distances between the individuals-Range & Bearing. This study presents the development of an open-source, low-cost communication module which can be attached to miniature sized robots; e.g., Mona. In this study, we only focused on bearing estimation to mathematically model the bearings of neighbouring robots through systematic experiments using real robots. In addition, the model parameters were optimised using a genetic algorithm to provide a reliable and precise model that can be applied for all robots in a swarm. For further investigation and improvement of the system, an additional layer of optimisation on the hardware layout was implemented. The results from the optimisation suggested a new arrangement of the sensors with slight angular displacements on the developed board. The precision of bearing was significantly improved by optimising in both software level and re-arrangement of the sensors' positions on the hardware layout.

Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System.

The piezoelectric actuator is indispensable for driving the micro-manipulator. In this paper, a simplified interval type-2 (IT2) fuzzy system is proposed for hysteresis modelling and feedforward control of a piezoelectric actuator. The partial derivative of the output of IT2 fuzzy system with respect to the modelling parameters can be analytically computed with the antecedent part of IT2 fuzzy rule specifically designed. In the experiments, gradient based optimization was used to identify the IT2 fuzzy hysteresis model. Results showed that the maximum error of model identification is 0.42% with only 3 developed IT2 fuzzy rules. Moreover, the model validation was conducted to demonstrate the generalization performance of the identified model. Based on the analytic inverse of the developed model, feedforward control experiment for tracking sinusoidal trajectory of 20 Hz was carried out. As a result, the hysteresis effect of the piezoelectric actuator was reduced with the maximum tracking error being 4.6%. Experimental results indicated an improved performance of the proposed IT2 fuzzy system for hysteresis modelling and feedforward control of the piezoelectric actuator.

Evolutionary optimization for risk-aware heterogeneous multi-agent path planning in uncertain environments.

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size.

Autonomous tracking of honey bee behaviors over long-term periods with cooperating robots.

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are transformative technologies in behavioral science and biology. The central element of the most important pollinator species-honey bees-is the colony's queen. Because honey bee self-regulation is complex and studying queens in their natural colony context is difficult, the behavioral strategies of these organisms have not been widely studied. We created an autonomous robotic observation and behavioral analysis system aimed at continuous observation of the queen and her interactions with worker bees and comb cells, generating behavioral datasets of exceptional length and quality. Key behavioral metrics of the queen and her social embedding within the colony were gathered using our robotic system. Data were collected continuously for 24 hours a day over a period of 30 days, demonstrating our system's capability to extract key behavioral metrics at microscopic, mesoscopic, and macroscopic system levels. Additionally, interactions among the queen, worker bees, and brood were observed and quantified. Long-term continuous observations performed by the robot yielded large amounts of high-definition video data that are beyond the observation capabilities of humans or stationary cameras. Our robotic system can enable a deeper understanding of the innermost mechanisms of honey bees' swarm-intelligent self-regulation. Moreover, it offers the possibility to study other social insect colonies, biocoenoses, and ecosystems in an automated manner. Social insects are keystone species in all terrestrial ecosystems; thus, developing a better understanding of their behaviors will be invaluable for the protection and even the restoration of our fragile ecosystems globally.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring.

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as 'biohybrids', designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra musselDreissena polymorphaand water fleaDaphniaspp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.

Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment.

This article is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot. This approach is built upon a recently proposed idea of using deep neural networks (DNNs) to approximate the optimal motion trajectories, which has been validated that a fast approximation performance can be achieved. To further enhance the network prediction performance, a recurrent network model capable of fully exploiting the inherent relationship between preoptimized system state and control pairs is advocated. In the lower level, a deep reinforcement learning (DRL)-based collision-free control algorithm is established to achieve the waypoint tracking task in an uncertain environment (e.g., the existence of unexpected obstacles). Since this approach allows the control policy to directly learn from human demonstration data, the time required by the training process can be significantly reduced. Moreover, a noisy prioritized experience replay (PER) algorithm is proposed to improve the exploring rate of control policy. The effectiveness of applying the proposed deep learning-based control is validated by executing a number of simulation and experimental case studies. The simulation result shows that the proposed DRL method outperforms the vanilla PER algorithm in terms of training speed. Experimental videos are also uploaded, and the corresponding results confirm that the proposed strategy is able to fulfill the autonomous exploration mission with improved motion planning performance, enhanced collision avoidance ability, and less training time.

A Minimally Invasive Approach Towards "Ecosystem Hacking" With Honeybees.

Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen bee, a central colony element, has the potential to change the collective behavior of the entire colony and thus also improve its interaction with the surrounding ecosystem. Such a system can be used to study and understand many elements of bee behavior within hives that have not been adequately researched. We discuss here the applicability of this technology for ecosystem protection: A novel paradigm of a minimally invasive form of conservation through "Ecosystem Hacking". We discuss the necessary requirements for such technology and show experimental data on the dynamics of the natural queen's court, initial designs of biomimetic robotic surrogates of court bees, and a multi-agent model of the queen bee court system. Our model is intended to serve as an AI-enhanceable coordination software for future robotic court bee surrogates and as a hardware controller for generating nature-like behavior patterns for such a robotic ensemble. It is the first step towards a team of robots working in a bio-compatible way to study honey bees and to increase their pollination performance, thus achieving a stabilizing effect at the ecosystem level.

Frequency shifting approach towards textual transcription of heartbeat sounds.

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription.