Bio-inspired artificial pheromone system for swarm robotics applications

From SAGE Publishing via Jisc Publications RouterHistory: epub 2020-06-03Publication status: PublishedFunder: grantová agentura české republiky; FundRef: https://doi.org/10.13039/501100001824; Grant(s): 17-27006YFunder: Engineering and Physical Sciences Research Council; FundRef: https://doi.org/10.13039/501100000266; Grant(s): EP/P01366X/1Funder: Engineering and Physical Sciences Research Council; FundRef: https://doi.org/10.13039/501100000266; Grant(s): EP/R026084/1Funder: OP VVV funded project, Research Center for Informatics; Grant(s): CZ.02.101/0.0/0.0/16_019/0000765Pheromones are chemical substances released into the environment by an individual animal, which elicit stereotyped behaviours widely found across the animal kingdom. Inspired by the effective use of pheromones in social insects, pheromonal communication has been adopted to swarm robotics domain using diverse approaches such as alcohol, RFID tags and light. COSΦ is one of the light-based artificial pheromone systems which can emulate realistic pheromones and environment properties through the system. This article provides a significant improvement to the state-of-the-art by proposing a novel artificial pheromone system that simulates pheromones with environmental effects by adopting a model of spatio-temporal development of pheromone derived from a flow of fluid in nature. Using the proposed system, we investigated the collective behaviour of a robot swarm in a bio-inspired aggregation scenario, where robots aggregated on a circular pheromone cue with different environmental factors, that is, diffusion and pheromone shift. The results demonstrated the feasibility of the proposed pheromone system for use in swarm robotic applications

Highly sensitive active pixel image sensor array driven by large-area bilayer MoS2 transistor circuitry

Various large-area growth methods for two-dimensional transition metal dichalcogenides have been developed recently for future electronic and photonic applications. However, they have not yet been employed for synthesizing active pixel image sensors. Here, we report on an active pixel image sensor array with a bilayer MoS2 film prepared via a two-step large-area growth method. The active pixel of image sensor is composed of 2D MoS2 switching transistors and 2D MoS2 phototransistors. The maximum photoresponsivity (Rph) of the bilayer MoS2 phototransistors in an 8 7 8 active pixel image sensor array is statistically measured as high as 119.16 AW 121. With the aid of computational modeling, we find that the main mechanism for the high Rph of the bilayer MoS2 phototransistor is a photo-gating effect by the holes trapped at subgap states. The image-sensing characteristics of the bilayer MoS2 active pixel image sensor array are successfully investigated using light stencil projection

Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning

Autonomous vehicles have been highlighted as a major growth area for future transportation systems and the deployment of large numbers of these vehicles is expected when safety and legal challenges are overcome. To meet the necessary safety standards, effective collision avoidance technologies are required to ensure that the number of accidents are kept to a minimum. As large numbers of autonomous vehicles, operating together on roads, can be regarded as a swarm system, we propose a bio-inspired collision avoidance strategy using virtual pheromones; an approach that has evolved effectively in nature over many millions of years. Previous research using virtual pheromones showed the potential of pheromone-based systems to maneuver a swarm of robots. However, designing an individual controller to maximise the performance of the entire swarm is a major challenge. In this paper, we propose a novel deep reinforcement learning (DRL) based approach that is able to train a controller that introduces collision avoidance behaviour. To accelerate training, we propose a novel sampling strategy called Highlight Experience Replay and integrate it with a Deep Deterministic Policy Gradient algorithm with noise added to the weights and biases of the artificial neural network to improve exploration. To evaluate the performance of the proposed DRL-based controller, we applied it to navigation and collision avoidance tasks in three different traffic scenarios. The experimental results showed that the proposed DRL-based controller outperformed the manually-tuned controller in terms of stability, effectiveness, robustness and ease of tuning process. Furthermore, the proposed Highlight Experience Replay method outperformed than the popular Prioritized Experience Replay sampling strategy by taking 27% of training time average over three stages

Extended Artificial Pheromone System for Swarm Robotic Applications

This paper proposes an artificial pheromone communication system inspired by social insects. The proposed model is an extension of the previously developed pheromone communication system, COS-Phi. The new model increases COS-Phi flexibility by adding two new features, namely, diffusion and advection. The proposed system consists of an LCD flat screen that is placed horizontally, overhead digital camera to track mobile robots, which move on the screen, and a computer, which simulates the pheromone behaviour and visualises its spatial distribution on the LCD. To investigate the feasibility of the proposed pheromone system, real micro-robots, Colias, were deployed which mimicked insects' role in tracking the pheromone sources. The results showed that, unlike the COS-Phi, the proposed system can simulate the impact of environmental characteristics, such as temperature, atmospheric pressure or wind, on the spatio-temporal distribution of the pheromone. Thus, the system allows studying behaviours of pheromone-based robotic swarms in various real-world conditions

Federated Reinforcement Learning for Collective Navigation of Robotic Swarms

The recent advancement of Deep Reinforcement Learning (DRL) contributed to robotics by allowing automatic controller design. The automatic controller design is a crucial approach for designing swarm robotic systems, which require more complex controllers than a single robot system to lead a desired collective behaviour. Although the DRL-based controller design method showed its effectiveness for swarm robotic systems, the reliance on the central training server is a critical problem in real-world environments where robot-server communication is unstable or limited. We propose a novel Federated Learning (FL) based DRL training strategy (FLDDPG) for use in swarm robotic applications. Through the comparison with baseline strategies under a limited communication bandwidth scenario, it is shown that the FLDDPG method resulted in higher robustness and generalisation ability into a different environment and real robots, while the baseline strategies suffer from the limitation of communication bandwidth. This result suggests that the proposed method can benefit swarm robotic systems operating in environments with limited communication bandwidth, e.g., in high-radiation, underwater, or subterranean environments

Bio-inspired artificial pheromone system for swarm robotics applications

Pheromones are chemical substances released into the environment by an individual animal, which elicit stereotyped behaviours widely found across the animal kingdom. Inspired by the effective use of pheromones in social insects, pheromonal communication has been adopted to swarm robotics domain using diverse approaches such as alcohol, RFID tags and light. COSΦ is one of the light-based artificial pheromone systems which can emulate realistic pheromones and environment properties through the system. This article provides a significant improvement to the state-of-the-art by proposing a novel artificial pheromone system that simulates pheromones with environmental effects by adopting a model of spatio-temporal development of pheromone derived from a flow of fluid in nature. Using the proposed system, we investigated the collective behaviour of a robot swarm in a bio-inspired aggregation scenario, where robots aggregated on a circular pheromone cue with different environmental factors, that is, diffusion and pheromone shift. The results demonstrated the feasibility of the proposed pheromone system for use in swarm robotic applications