The design of cleaning and maintenance (CaM) robots is generally limited by their fixed morphologies, resulting in limited functions and modes of operation. Contrary to fixed shape robots, the design of reconfigurable robots presents unique challenges in designing their system, subsystems, and functionalities with the scope for innovative operational scenarios and achieving high performance in multiple modalities without compromise. This paper proposes a heuristic framework using three layers, namely input, formulation, and output layer, for designing reconfigurable robots with the aid of established transformation principles including expand/collapse, expose/cover, and fuse/divide observed in several products, services, and systems. We apply this heuristic framework approach to the novel design of a pavement CaM robotic system and subsystems, namely, (i) varying footprint, (ii) transmission, (iii) outer skin or cover, (iv) storage bin, (v) surface cleaning, and (vi) vacuum/suction and blowing. The advances in the design method using the heuristic approach are demonstrated by developing an innovative reconfigurable design for the CaM task. Kinematic analysis and control architecture enables the unique locomotion behavior and gaits, namely, (a) static reconfiguration and (b) reconfiguration while locomotion, supported by the control architecture. Experiments were conducted, and outcomes were discussed along with the failure mode analysis to support the design robustness and limitations through the observations made from the development to testing phase over one year. A detailed video demonstrating the design capabilities is linked.

Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of “How clean is clean” by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the “touch and inspect” analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.

Reconfigurable robots design based on polyominos or n-Omino is increasingly being explored in cleaning and maintenance (CnM) tasks due to their ability to change shape using intra- and inter-reconfiguration, resulting in various footprints of the robot. On one hand, reconfiguration during a CnM task in a given environment or map results in enhanced area coverage over fixed-form robots. However, it also consumes more energy due to the additional effort required to continuously change shape while covering a given map, leading to a deterioration in overall performance. This paper proposes a new strategy for n-Omino-based robots to select a range of optimal morphologies that maximizes area coverage and minimizes energy consumption. The optimal “morphology” is based on two factors: the shape or footprint obtained by varying the angles between the n-Omino blocks and the number of n-Omino blocks, i.e., “n”. The proposed approach combines a Footprint-Based Complete coverage Path planner (FBCP) with a metaheuristic optimization algorithm to identify an n-Omino-based reconfigurable robot’s optimal configuration, assuming energy consumption is proportional to the path length taken by the robot. The proposed approach is demonstrated using an n-Omino-based robot named Smorphi, which has square-shaped omino blocks with holonomic locomotion and the ability to change from monomino to tetromino. Three different simulated environments are used to find the optimal morphologies of $Smorphi$ using three metaheuristic optimization techniques, namely, MOEA/D, OMOPSO, and HypE. The results of the study show that the morphology produced by this approach is energy efficient, minimizing energy consumption and maximizing area coverage. Furthermore, the HypE algorithm is identified as more efficient for generating optimal morphology as it took less time to converge than the other two algorithms.

Reconfigurable robots have the potential to perform complex tasks by adapting their morphology to different environments. However, designing optimal morphologies for these robots is challenging due to the large design space and the complex interactions between the robot and the environment. An in-house robot named $Smorphi$, having four holonomic mobile units connected with three hinge joints, is designed to maximize area coverage with its shape-changing features using transformation design principles (TDP). The reinforcement learning (RL) approach is used to identify the optimal morphologies out of a vast combination of hinge angles for a given task by maximizing a reward signal that reflects the robot’s performance. The proposed approach involves three steps: (i) Modeling the Smorphi design space with a Markov decision process (MDP) for sequential decision-making; (ii) a footprint-based complete coverage path planner to compute coverage and path length metrics for various Smorphi morphologies; and (iii) pptimizing policies through proximal policy optimization (PPO) and asynchronous advantage actor–critic (A3C) reinforcement learning techniques, resulting in the generation of energy-efficient, optimal Smorphi robot configurations by maximizing rewards. The proposed approach is applied and validated using two different environment maps, and the results are also compared with the suboptimal random shapes along with the Pareto front solutions using NSGA-II. The study contributes to the field of reconfigurable robots by providing a systematic approach for generating optimal morphologies that can improve the performance of reconfigurable robots in a variety of tasks.