The utilization of autonomous intelligent robots in search and rescue (SAR) is a new and challenging field of robotics, dealing with tasks in extremely hazardous and complex disaster environments. Autonomy, high mobility, robustness, and modularity are critical design issues of rescue robotics, requiring dexterous devices equipped with the ability to learn from prior rescue experience, adaptable to variable types of usage with a wide enough functionality under different sensing modules, and compliant to environmental and victim conditions. Intelligent, biologically inspired mobile robots and, in particular, serpentine mechanisms have turned out to be widely used robot effective, immediate, and reliable responses to many SAR operations. This article puts a special emphasis on the challenges of serpentine search robot hardware, sensor-based path planning, and control design.

In this work, our motivation focuses on an energy-efficient data collection problem by a mobile sink, an unmanned aerial vehicle (UAV) with limited battery capacity, in a robot network divided into several robot clusters. In each cluster, a cluster head (CH) robot allocates tasks to the remaining robots and collects data from them. Our contribution is to minimize the UAV total energy consumption coupled to minimum cost data collection from CH robots by visiting optimally a portion of the CH robots. The UAV decides the subset of CH robots to visit by considering not only the locations of all CH robots but also its battery capacity. If the UAV cannot visit all CH robots, then the CH robots not visited by the UAV transmit their data to another CH robot to forward it. The decision of transmission paths of transmitting robots is included in the cost optimization. Our contribution passes beyond the existing paradigms in the literature by considering the constant battery capacity for the UAV. We derive the optimal approach analytically for this problem. For various numbers of clusters, the performance of our strategy is compared with the approach in the close literature in terms of total energy consumed by CH robots, which affects network lifetime. Numerical results demonstrate that our strategy outperforms the approach in the close literature.

We propose a novel approach inspired by compressible fluids as a distributed and scalable solution to the deployment problem of mobile sensor networks in unknown environments. Our approach is based on fluid dynamics through which we model the sensor network as a fluid body and each sensor node as a fluid element. Originating from local neighborhood interactions, the principles of fluid flow are adapted to the deployment of the sensor network. Mimicking the diffusive and self-spreading behavior of compressible fluids in a mobile sensor network, we achieve the desirable properties of effective coverage, scalability, and distributed self-deployment. An adaptive deployment scheme is also devised for urban disaster environments where the number of nodes to be deployed may not be determined a priori. Simulation results show that our approach can guarantee a desired level coverability