Autonomous Mobile Robots (AMRs) need sophisticated path planning algorithms to navigate complex environments, avoiding collisions while minimizing travel distance and computation time. Traditional algorithms often struggle in dynamic scenarios. Nature provides rich inspiration for developing robust and adaptive algorithms that mimic collective intelligence and adaptability, offering clear advantages over conventional methods. This chapter compares several biologically inspired algorithms categorized by their biological groups: microorganism-based Invasive Weed Optimization (IWO), the aquatic Whale Optimization Algorithm (WOA), insect-based Moth Flame Optimization (MFO), mammal-based Grey Wolf Optimization (GWO), and bird-based Harris Hawks Optimization (HHO). For example, the HHO algorithm emulates the cooperative movement patterns of birds, where individuals share information and adapt their movements for efficient pathfinding. Foraging-based algorithms, inspired by insect and animal foraging behaviors, balance exploration and exploitation to find the optimal path efficiently. These algorithms were tested in simulated environments that mirror real-world AMR navigation challenges, with a focus on static environments with predefined maps and obstacles. The study evaluated the algorithms on the basis of path length, computation time, and ability to quickly converge to the optimal path. This pioneering research compares algorithms by their biological inspirations (swarm vs. foraging) and assesses their performance in realistic simulations.