To avoid risking the lives of victims and rescue team personnel in the event of disasters like earthquakes, volcanic eruptions, hurricanes, etc., search and rescue (SAR) robots are increasingly incorporated into the operation. One of the major challenges in integrating SAR robots into rescue operations is the potentially severely damaged infrastructure within the disaster site. A critical component of this is a functional communication system which is essential for exchanging real-time information between the robots in the field and the base station (BS). Given that there might be either limited coverage or a complete failure of standard communication systems in a severely affected disaster site, a novel disaster management system named Titan disaster management system (DMS), which is based on low-power wide-area networks (LPWANs) LoRa technology is proposed. Its development consists of three solutions, which are Titan coordination, communication, and navigation solutions. The Titan coordination solution based on a robot platoon configuration has been developed to assist the rescue team in establishing a BS within the disaster-affected area. The victims, the BS, and the SAR robots may all communicate with one another using the Titan communication system. For guiding the SAR robot through the devastated disaster scene, the Titan navigation solution named fuzzy inference system vector field histogram (FISVFH) was developed. The different phases of the development of Titan DMS are discussed, and the results obtained from field tests are presented in this article.

The original concept of the artificial potential field in robot path planning has spawned a variety of extensions to address its main weakness, namely the formation of local minima in which the robot may be trapped. In this paper, a smooth navigation function combining the Dijkstra-based discrete static potential field evaluation with bilinear interpolation is proposed. The necessary modifications of the bilinear interpolation method are developed to make it applicable to the path-planning application. The effect is that the strategy makes it possible to solve the problem of the local minima, to generate smooth paths with moderate computational complexity, and at the same time, to largely preserve the product of the computationally intensive static plan. To cope with detected changes in the environment, a simple planning strategy is applied, bypassing the static plan with the solution of the A* algorithm to cope with dynamic discoveries. Results from several test environments are presented to illustrate the advantages of the developed navigation model.

Trajectory planning in the field of mobile robotics involves the generation of a trajectory to navigate a robot from a start state to a goal state. One widely employed technique involves a two-step approach: a path planner generates a path made up of piecewise linear segments with sharp turns, which are then smoothed in the trajectory generation step. In contrast, this work formulates trajectory generation as an optimization problem based on the Bézier curve, denoted as ‘BTP’, to generate the robot’s trajectory in one step. It uses a weighted objective function of trajectory length and navigation time to suit different optimization strategies while considering the robot’s kinematics and dynamics limitations. BTP adopts matrix-based formulations for all mathematical operations to enable dynamic adjustment of the degree of the Bézier curve during the optimization process, if convergence is not obtained with the current degree. Additionally, BTP guarantees that the robot’s trajectory is always within the open space identified by the robot’s sensors. The efficacy of BTP has been evaluated through simulations and real-world experimentation, including soccer games and cluttered environment scenarios. Finally, the performance is benchmarked against some of the existing trajectory planners. BTP reduced the robot’s navigation time by a minimum of 11% up to 55% compared to other tested trajectory planners, ensuring $C^{2}$ continuity rather than just $C^{1}$ continuity. Furthermore, it consistently achieved precise goal configuration, unlike the tested trajectory planners, which exhibited deviations of up to 0.6 meters.

In recent years, multirobot systems (MRSs) have witnessed significant growth because of their capability of solving complex problems involving collaborative missions. A two-phase approach is typically employed in such applications, encompassing task planning and assignment among the robots, followed by motion planning to execute the assigned tasks. This work focuses on the motion planning phase, which facilitates the optimal navigation of multiple robots from their initial to desired goal configurations. A novel multirobot Bézier curve-based trajectory planner (MrBTP) is proposed. MrBTP generates optimal trajectories for multiple robots navigating in an unknown environment, including the polygonal characterization of the available open space. The optimization process is based on using a weighted objective function that combines the navigation time of each robot and the length of its trajectory, allowing for various path optimization strategies. The effectiveness of MrBTP has been evaluated in various scenarios, and its performance is compared with that of a selected trajectory planner based on three performance metrics. Over the three robots used in the test, MrBTP reduced the navigation time by 21%–49% and the trajectory length by 10%–18% compared to the selected benchmark trajectory planner. In addition, it ensures $C^{2}$ rather than just $C^{1}$ continuity.