Humanoid robot path planning using memory-based gravity search algorithm and enhanced differential evolution approach in a complex environment

The present work focuses on the optimal path planning of humanoid robots in a rugged terrain using a hybrid-based improved gravitational search algorithm (IGSA) tuned with a differentially perturbed velocity (DV) approach. The primary IGSA suffers from the disadvantage of a lower convergence rate and the risk of getting trapped in optimal local conditions. The drawbacks are eliminated by employing a hybrid IGSA-DV path planning approach, which improves the memory and velocity updating scheme. The algorithm is designed to minimize the overall path length of the humanoid, from source to goal, in the minimal time possible. The humanoids, during their locomotion, coordinate with each other to avoid collisions in their journey. The robots make decisions based on the position of the various obstacles within the search space. So, path smoothness is also considered to ensure stability during the locomotion. The work is further focussed on optimizing the energy efficiency of the different joints of the humanoid while walking on even and uneven surfaces. The path planning is performed in real-world and simulation environments, and the results are then compared with the different existing, individual, and hybrid techniques. The comparison of the above approach revealed that the IGSA-DV algorithm showed an optimal outcome in terms of path length and time taken. Moreover, the percentage deviation in the path length and time in both the simulation and experimental environment was within 6%. The Petri-Net approach is implemented along with the proposed technique to avoid confusion among the robots during multiple robot navigations.