Optimal Task Planning of Humanoid in Cluttered Environment

Due to high degrees of freedom of humanoids and induced redundancy, there are multiple ways of performing a given manipulation task. Finding optimal ways of performing tasks is one desirable property of any motion planning framework. This includes optimizing the path with respect to a certain objective function. Additionally, a variety of constraints need to be satisfied such as stability, self-collision and collision with objects in the environment and also kinematic closed-loop chains formed during the task. Time requirements of the planner is another important aspect that drives us to use sampling based methods. In this paper, we present an asymptotically optimal sampling based approach for generating statically stable motion plans. We use RRT*-connect algorithm which we obtained by modifying RRT-Connect. Moreover, we use a gradient based inverse kinematics solver to generate goal configurations. We evaluate the efficacy of our approach in the results section in a simulation environment on Hubo+ robot model. The results show a significant improvement in path costs as well as overall optimality of the given task.