Efficient fire drill evacuation in high-rise buildings demands real-time perception and optimized path planning. This study presents an autonomous evacuation framework that integrates deep learning-based object detection with multi-objective trajectory planning. Using RGB-D vision, the system detects human traffic and staircases, projecting them into the robot’s workspace. The evacuation task is modeled as a Moving Target Traveling Salesman Problem (MT-TSP) and solved via a modified NSGA-II algorithm, balancing travel time, path length, safety, and accessibility. Experiments show over 90% detection accuracy and significant improvements in navigation efficiency, demonstrating the effectiveness of combining deep learning with evolutionary optimization for robotic fire drill planning.

Cleaning robots are one of the market dominators in the commercialized robot space. So far, numerous robots have been introduced that can perform cleaning tasks in various settings, including floor, pavement, pool, lawn, windows, etc. However, none of the existing commercial cleaning robots targets the staircase, commonly found in multi-story buildings. Even though few works in the literature introduced robotic solutions for staircase cleaning, they primarily focused on cleaning the ascending staircase often, with a loose connection to access the descending staircase. In this paper, we propose a novel autonomous reconfigurable robotic platform called sTetro-D that can autonomously detect the descending staircase, approach the step, and perform area coverage in an unknown environment. The developed autonomy framework consists of two modes which are search mode and clean mode. In search mode, we implemented an RGB-D camera-based fusion technique wherein we combined the image bounding box from DCNN (Deep Convolution Neural Network) with the depth information to find the 3D first step pose that assists the robot in approaching it precisely. After the successful stair approach, the cleaning mode enables the staircase area coverage process. We described all these aspects and concluded with an experimental analysis of the proposed robotic system in a real-world scenario. The results demonstrate that the robot has a significant performance in detecting the descending staircase, staircase approach, and area coverage.

Planning the energy-efficient and collision-free paths for reconfigurable robots in complex environments is more challenging than conventional fixed-shaped robots due to their flexible degrees of freedom while navigating through tight spaces. This article presents a novel algorithm, energy-efficient batch informed trees* (BIT*) for reconfigurable robots, which incorporates BIT*, an informed, anytime sampling-based planner, with the energy-based objectives that consider the energy cost for robot’s each reconfigurable action. Moreover, it proposes to improve the direct sampling technique of informed RRT* by defining an$L^2$greedy informed setthat shrinks as a function of the state with the maximum admissible estimated cost instead of shrinking as a function of the current solution, thereby improving the convergence rate of the algorithm. Experiments were conducted on a tetromino hinged-based reconfigurable robot as a case study to validate our proposed path planning technique. The outcome of our trials shows that the proposed approach produces energy-efficient solution paths, and outperforms existing techniques on simulated and real-world experiments.

Being as a robotic companion is an extensive application of on-body robots; yet, as an emerging type of robots, few previous works focus on the design of on-body companion robots from the users' perspective, remaining users' expectations towards this type of robots unclear. To assist designers in the design process of on-body companion robots, we surveyed users' expectations towards on-body companion robots (n=215) by a questionnaire constituting of questions on factors that may affect robot acceptance, including robot functionality, robot appearance, and robot social ability. Based on the survey results, we stated design guidelines for the design of on-body companion robots supporting designers with insights into users. To demonstrate how to design on-body companion robots based on our findings, we organized a workshop with experienced designers to develop a conceptual on-body companion robot, and they proposed Bubo, an example prototype of on-body companion robot.

Research focuses on robots related to area coverage applications such as cleaning, painting, demining, lawn moving, and inspection is gaining significant momentum in recent years. The majority of such research platforms faces significant performance issues while accessing narrow and constrained spaces due to their fixed morphology. To this end, we have developed a novel self-reconfigurable robot platform named as “hTetran” inspired from Polyabolo which can change its morphology to any of the six tetrabolo shapes with an objective of maximizing the area coverage. This paper presents the system design, reconfiguration theory, and locomotion modules of the developed robot, including the adaptation of Polyabolo tiling theory as a coverage path planning strategy for autonomous navigation. The paper concludes with a set of experiments in a mocked office room setup that validates the efficiency of the proposed robot in terms of area coverage. Our experimental trials indicate that the “hTetran” brings out a higher area coverage performance in all considered experimental cases.

This research work presents the development of a novel tetris inspired reconfigurable floor cleaning robot - hTetro, utilizing the hinged dissection theory of polyominoes. The developed robot platform is capable of transforming between any of the seven set of one-sided tetromino morphologies according to the perceived environment with an objective of maximizing the coverage area. Experiments were performed across two different settings to systematically compare the coverage area performance of the developed hTetro robot and a commercially available fixed morphology robot platform. Results indicate significantly higher coverage area performance in the case of hTetro due to its ability to assume optimal morphology in relation to navigating environment.