Evolutionary Wall-Following Hexapod Robot Using Advanced Multiobjective Continuous Ant Colony Optimized Fuzzy Controller

This paper proposes an evolutionary wall-following hexapod robot, where a new multiobjective evolutionary fuzzy control approach is proposed to control both walking orientation and speed of a hexapod robot for a wall-following task. According to the measurements of four distance sensors, a fuzzy controller (FC) controls the walking speed of the robot by changing the common swing angles of its six legs. At the same time, the FC controls the orientation of the robot by applying additional changes to the swing angles of the three legs in each side. In addition to the basic requirement of walking along the wall in an unknown environment, the control objectives are that the robot should maintain a proper robot-to-wall distance and walk at a high speed. This paper formulates the control problem as a constrained multiobjective FC optimization problem. A data-driven advanced multi-objective front-guided continuous ant colony optimization (AMO-FCACO) is proposed to address the problem and find a Pareto set of optimal solutions of different FCs. The performance of the AMO-FCACO-based fuzzy wall-following control approach is verified through simulations and comparisons with various multiobjective optimization algorithms. Experiments on controlling a real robot in an unknown environment using two software-designed FCs are performed to view the control performance in practice.