Modular Autonomous Swarm Architecture for Biomedical Nanorobots
A modular biomedical nanorobot architecture based on biochemical communication, adaptive role specialization and distributed consensus.
Abstract
Biomedical nanorobots could support targeted drug delivery, biosensing and tissue engineering but limited coordination restricts operation in complex physiological environments. This study explores a modular autonomous swarm architecture where different nanorobot designs operate through the same cooperative mechanism. Each robot has a biocompatible surface with affinity receptors or stimulus responsive elements for sensing local biomarkers and an interchangeable therapeutic or diagnostic payload. Robots travel by propulsion or fluid transport while receptor binding provides local biochemical input. Detection activates the release of diffusible signaling molecules which form local concentration gradients and recruit nearby robots. Recruited robots resample the biochemical environment and signaling thresholds assign them to verification, relay or payload roles. Relay robots regenerate the signaling cue to propagate verified information through the swarm, while repeated receptor responses from independent robots contribute to a distributed consensus threshold before action is permitted. The architecture was tested in simulated healthy, inflamed, damaged and target tissue with overlapping biomarker signals, sensor noise, communication loss, relay failure and robot failure. The complete swarm achieved 79.94% successful target identification and task completion, 94.88% specificity, 88.81% site classification accuracy and 69.27% payload efficiency, while reducing active swarm utilization from 65.43% to 8.08% and communication overhead by 37.7% compared with consensus only swarms. Performance remained 69.21% to 80.21% under individual disturbances and fell to 51.36% under combined severe stress. These results support biochemical communication and adaptive specialization as a basis for autonomous biomedical nanorobot swarms.
Overview
This work examines a reusable cooperative mechanism that can operate across different biomedical nanorobot designs. Physical robot configuration and payload can change while communication, role assignment and consensus follow the same swarm architecture.
Architecture
Local biomarker detection releases diffusible signaling molecules that recruit nearby robots. Recruited agents resample the environment and take on verification, relay or payload roles according to local signaling thresholds. Relay agents extend verified information through the swarm while repeated independent responses contribute to a distributed consensus decision.
Computational evaluation
The architecture was evaluated in simulated healthy, inflamed, damaged and target tissue. The tests included overlapping biomarkers, sensor noise, communication loss, relay failure and robot failure. Comparisons measured classification, specificity, payload efficiency, communication overhead, swarm utilization and performance under individual and combined disturbances.
Results
The complete computational swarm achieved 79.94% successful target identification and task completion, 94.88% specificity, 88.81% site classification accuracy and 69.27% payload efficiency. Active swarm utilization fell from 65.43% to 8.08% and communication overhead was 37.7% lower than consensus only swarms. Performance remained between 69.21% and 80.21% under individual disturbances and fell to 51.36% under combined severe stress. These are simulation results.