Optimal Trajectory Planning for Design of a Crawling Gait in a Robot Using Genetic Algorithm

Abstract

This paper describes a new locomotion mode to use in a crawling robot, inspired of real inchworm. The crawling device is modelled as a mobile manipulator, and for each step of its motion, the associated dynamics relations are derived using Euler-Lagrange equations. Next, the Genetic Algorithm (GA) is utilized to optimize the trajectory of the free joints (active actuators) in order to minimize the consumed effort (e.g. integral of square of torques over the step time). In this way, the results show a reduction of 5 to 37 percent in torque consumption in comparison with the gradient based method. Finally, numerical simulation for each step motion is presented to validate the proposed algorithm.

An Analytic Method for the Kinematics and Dynamics of a Multiple-Backbone Continuum Robot

“…Compared to traditional rigid link robots, continuum robots feature a continuous backbone without joints [1], redundant degrees of freedom and exhibit significant compliance that provides exceptional operational capacities during environmental interaction and object manipulation. Due to inherent flexibility, a continuum robot has great potential in applications that include operation inside complex, unstructured environments [2][3], such as collapsed buildings in search and rescue operations [4][5][6] or minimally invasive surgery (MIS) in medical applications [7][8][9][10].…”

Artificial intelligence and machine learning for autonomous military vehicles

“…The simplest control calculates a desired steering angle based on the car's orientation and distance relative to a desired trajectory, as used by Stanley (Thrun et al, 2006). A widely used alternative is the PID controller, notably the adaptive PID controller, where the gains are tuned offline for various states and updated accordingly or adjusted in real time using neural networks, genetic algorithms, or fuzzy logic (Puntunan and Parnichkun, 2006, Scott et al, 1992, Wai, 2003, Ghanbari and Noorani, 2011. The last dominant control method used in the industry is Model Predictive Control (MPC), which uses a model of the steering input effect on the vehicle trajectory and attempts to find an optimal path (Lenain et al, 2005, Borrelli et al, 2005, Falcone et al, 2007a, Falcone et al, 2007b.…”

Cloud Model Approach for Lateral Control of Intelligent Vehicle Systems

“…Current self-adaptive control methods of intelligent vehicles modify parameters of PID on the basis of changes in intelligent vehicle states and object properties, thereby improving control. They mainly include adaptive control reference models [11], adaptive control fuzzy models [12,13], adaptive control neural networks models [14,15], and adaptive control evolutionary models [16].…”