A metaheuristic approach to optimal morphology in reconfigurable tiling robots

Abstract

Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies.

Exploring Embodied Intelligence in Soft Robotics: A Review

“…The principles underlying self-motion, aggregation, and emergent macroscopic functionality can also be demonstrated through artificial systems, particularly swarm robotics. Like amoebae, swarm robots can emit aggregation signals and assemble into artificial organisms [47], as shown in Figure 7b. release signaling molecules called cAMPs.…”

“…The principles underlying self-motion, aggregation, and emergent macroscopic functionality can also be demonstrated through artificial systems, particularly swarm robotics. Like amoebae, swarm robots can emit aggregation signals and assemble into artificial organisms [47], as shown in Figure 7b. (a) Dictyostelium discoideum, commonly referred to as slime mold, capable of a transition from a collection of unicellular amoebae into a multicellular organism [46]; (b) modular reconfigurable robots [47].…”

“…Like amoebae, swarm robots can emit aggregation signals and assemble into artificial organisms [47], as shown in Figure 7b. (a) Dictyostelium discoideum, commonly referred to as slime mold, capable of a transition from a collection of unicellular amoebae into a multicellular organism [46]; (b) modular reconfigurable robots [47]. Reproduced with permission from ref.…”

A Deep Reinforcement Learning Approach to Optimal Morphologies Generation in Reconfigurable Tiling Robots

“…Our previous work propose a footprint-based coverage planner that can perform complete coverage for the different morphologies of a reconfigurable robot [20]. In this work, we adapt that coverage planner to perform complete coverage in a given environment.…”

“…This approach uses dynamic programming to find the optimal path with the fewest reconfigurations, but it may be computationally expensive for real-world scenarios. A major contribution is made in [20] for energy-efficient area coverage by identifying a range of optimal morphologies using a metaheuristic algorithm in a reconfigurable robot. This approach generates morphology with an optimal balance between area coverage and energy consumption.…”