This paper presents an Enhanced Particle Swarm Optimisation (EPSO) algorithm to improve multi-robot path planning by integrating a new path planning scheme with a cubic Bezier curve trajectory smoothing algorithm. Traditional PSO algorithms often result in suboptimal paths with numerous turns, necessitating frequent stops and higher energy consumption. The proposed EPSO algorithm addresses these issues by generating smoother paths that reduce the number of turns and enhance the efficiency of multi-robot systems. The proposed algorithm was evaluated through simulations in two scenarios, and its performance was compared against the basic PSO algorithm. The results demonstrated that EPSO consistently produced shorter, smoother paths with fewer directional changes, albeit with slightly longer execution times. This improvement translates to more efficient navigation, reduced energy consumption, and enhanced overall performance of multi-robot systems. The findings underscore the potential of EPSO in applications requiring precise and efficient path planning, highlighting its contribution to advancing the field of robotics.

Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.

This paper proposes a novel multi-step broadcast control (MBC) scheme to deploy a group of autonomous mobile agents for accomplishing coverage tasks in a bounded region. Traditional broadcast control (BC) schemes use a one-to-all communication framework to transmit a uniform signal to all agents, making it cost-effective compared with any all-to-all communication-based scheme for a multi-agent system. However, as BC schemes are based on a single-step view of the environment for decision-making, the environment’s varying distribution density is not known immediately to the agents, resulting in suboptimal performance. To overcome this drawback, this paper proposes an MBC scheme, where agents use a predictive multi-step view and are able to detect the varying densities in the environment ahead of time. The local controller output is estimated using a weighted averaging technique which assigns a higher weight to immediate steps; this feature compensates for any decrease in prediction accuracy as the number of steps increases. We demonstrate the effectiveness of the proposed MBC scheme using a coverage task over a region with uneven population density. Compared to existing BC schemes, the proposed MBC scheme shows superior convergence characteristics in task accomplishment and deployment efficiency.