Behavior-based robot navigation on challenging terrain: A fuzzy logic approach

By H. Seraji, Ayanna M. Howard. IEEE Trans. Robotics Autom., 2002

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Sampling-Based Robot Motion Planning: A Review (2014, methodology): “Methods such as Fuzzy Logic Control [33]–[35], Neural Networks [36], Genetic Algorithms [37], [38], Ant ColonyOptimization [39] and SimulatedAnnealing [20] have all been applied in robot path planning.”
Multigait soft robot (2011, background): “R developed to increase the range of motions and functions open to machines, and to build into them some of the characteristics [including autonomous motion (1–3), adaptability to the environment (4–7), and capability of decision making (8, 9)] of animals, particularly animals with skeletons.”
Path Planning for Autonomous Mobile Robots: A Review (2021, background): “Seraji and Howard [140] demonstrate the use of fuzzy logic on an experimental mobile platform to navigate through unstructured terrain.”
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain (2010, background): “Labels can be gathered either offline (Seraji and Howard 2002; Howard et al. 2007) or online (Thrun et al.”