Autonomous Navigation for a Dynamical Hexapod Robot Using Fuzzy Logic Controller

Mobile Robot Path Planning Using Polyclonal-Based Artificial Immune Network

“…Reinforcement learning algorithm [1,5,6] has simple and complete theory, but it is mostly used in static environment because its infinite state in complex environment. Fuzzy logic control (FLC) [7][8][9] has the capacity to handle uncertain and imprecise information obtained from sensors using linguistic rules. The advantages of fuzzy system are that it does not require precise analytical models of environment and it has better real-time performance, but its computational complexity increases with the geometric progression growth of rules' number.…”

“…Experimental environment sets are as follows: the initial position of robot (9,8), the goal position (8,16), and the velocity of robot is 0.1 m/s. In Figures 2(a) and 2(b), artificial immune network is utilized for mobile robot path planning.…”

“…Experimental environment sets are as follows: the initial position of robot (5,4), the goal position (10,16), the number of obstacles are 5, the obstacles position (9, 10), (7,6), (5,8), (8,13), and (6, 12), respectively. The velocity is 0.1 m/s.…”

“…A series of experiments are performed to compare the behavior of the proposed algorithm with reactive immune network [16] in solving the problem of local minima. Experimental environment sets are as follows: robot's initial position (3,8), goal's initial position (12,11), and obstacles' position (9, 10), (7,11), (5,8), and (12,8). The velocity is 0.1 m/s and = 1 s.…”

Design of a high-speed line following robot that smoothly follows tight curves

“…Autonomous line following robot are increasingly being used to perform object transport tasks in industrial environments, in surveillance applications and in construction and mining industries [1][2][3][4]. However not much published work exists that discuss possible control strategies and implementations.…”