Intelligent Approaches in Locomotion - A Review <s> Transition table <s> We present a search-based method for the generation of a terrain-adaptive optimal gait of a six-legged walking machine. In this, several heuristic rules have been proposed to reduce the search effort. We identify the useful support states of the machine and form a table to indicate for each of these states the list of other states to which a transition can be made. This helps in converging to and maintaining a periodic gait through a limited search while retaining adequate options to deviate from such a gait as and when needed. The criterion for optimization is coded into a function that evaluates the promise of a node in the search graph. We have shown how this function may be designed to generate the common periodic gaits like the wave gait, the equal phase gait, and the follow-the-leader gait. The purpose is to demonstrate that the proposed method is sufficiently general and can cater to a wide range of optimizing requirements. <s> BIB001 </s> Intelligent Approaches in Locomotion - A Review <s> Transition table <s> Abstract This paper reports on experiments involving a hexapod robot. Motivated by neurobiological evidence that control in real hexapod insects is distributed leg-wise, we investigated two approaches to learning distributed controllers: genetic algorithms and reinforcement learning. In the case of reinforcement learning, a new learning algorithm was developed to encourage cooperation between legs. Results from both approaches are presented and compared. <s> BIB002
In BIB002 , a transition table was evolved, in order to specify actions based on the current system state, for a hexapod. Body states were classified into an integer value and this was used to index a list of actions, used to move the legs. For a given transition table, generated using a GA, the hexapod was simulated starting with each possible initial configuration, and the fitness function was the proportion of those starting positions that led to a tripod gait. The best table evolved gave stable tripod gates for 98.4% of the initial states, with the failures arising from initial symmetrical leg positions that could not lead to asymmetrical tripod gaits. A transition table was also used in BIB001 to control a hexapod, but this time, the state value was calculated as a binary string where each bit represented either supporting (down) or not supporting (up) leg. With one leg being raised or lowered at a time, the goal was to move through a terrain that had specified bad patches (pre-known to the algorithm). Using a graph search technique, locomotion was successfully generated and exhibited different gates.