Develops a new gait synthesizer for a snake-like robot in an unstructured changing environment similar to earthquake rubbles. The gait synthesizer, inspired from the GARIC Architecture, is implemented on a simulated navigation of a snake robot. We consider 6 prototypical gaits classified into vertical undulation, lateral undulation, heading changing right/left, flapping right/left. The gait synthesizer is composed of a gait selection network that decides on a combination of prototypical gaits based on a fuzzy controller which is further tuned by a gait evaluation module based on an ANN that learns from prior performances of robot locomotion and acts as an arbiter unit on gait performance. The third module is a stochastic gait modifier, which takes feedback information from the dynamic robotic system and changes the gait based on reinforcement learning.

This paper presents the multisensor-based online path planning of a serpentine robot in the unstructured changing environment of earthquake rubble during search for survivors. The robot has 6 links and 16 controllable degrees of freedom and is equipped with an ultrasound sensor and a camera on its head and infrared sensors along the links of the robot for obstacle avoidance. The serpentine robot locomotion is achieved as a combination of basic snakelike and wormlike gaits which are that of lateral undulation, rectilinear motion, flapping motion (right, left), and of torsional rotation (which has the pivot point at the center of the robot). The path planning algorithm is adopted from distance transform based exploratory path planning and modified to our serpentine robotic configuration. The adopted method is used for calculating the cost of the possible next configuration states so that the snake/worm like robot selects its next gait accordingly. Environmental data is obtained from the multiple sensors located along the body of the robot. The sensors are located in the following configuration: one ultrasound sensor for detecting the obstacles in front of the robot and constructing the map for free space, and 12 infrared sensors (6 pairs) which are located on the left and right of the joints of the robot. Simulation results are presented in the paper as an illustration of the efficiency of the method in complex unstructured environments.