Probabilistic Approach to Robot Motion Planning in Dynamic Environments

Abstract

Four major approaches to robot motion planning in dynamic environments are discussed: probabilistic robot, probabilistic collision state (PCS), partially closed-loop receding horizon control (PCLRHC) and gross hidden Markov model (GHMM). A comparison of three mapping techniques, Kalman filter, expectation and maximization algorithm and Markov model, is presented. The PCS method is the probabilistic extension of inevitable collision state, which is found to be the safest motion planning method. The concept of open-loop and partially closed-loop receding horizon control (OLRHC and PCLRHC) is compared critically, and the algorithms are benchmarked. GHMM is the best suited method for environments with limited space and dynamic environment due to human interactions. GHMM parameters and structure are evaluated using an incremental "learn-and-predict" approach. For exploring GHMM, we simulated a cafeteria with eight tables to be served by a robot, considering three different arrangements of tables along with convex and concave obstacles, and obtained the path length and time taken for a Hamiltonian path. During the simulation, it was observed that for a given static or dynamic environment, the concavity of the obstacles is what makes the scenario a complex one.

A Probabilistic Particle Tracking Framework for Guided and Brownian Motion Systems with High Particle Densities

“…Thus, the hyperprameters are currently determined with the aim to maximize the trajectory length. In future investigations, however, the hyperparameters will be optimized regarding the multi-objectives trajectory length, smoothness of the trajectory, and computational requirements like memory and execution time by developing a method for estimating the parameters automatically, e.g., via a Kalman filter [55]-based algorithm like in this example on robot motion planning [33], so that less experts knowledge have to be provided. Classical approaches like grid search or gradient-based approaches would require a ground truth to Considering the presented results, the introduced framework can reconstruct trajectories from both, guided and Brownian motion systems, even under high particle densities.…”

Robotic Mushroom Harvesting by Employing Probabilistic Road Map and Inverse Kinematics

“…The links have an ordered structure in which each link has its own coordinate system, and is positioned relative to the coordinate system of the previous link. The position of the link i in the coordinate system of its ancestor is obtained by computing the joint angle [34,[36][37][38][39][40][41][42][43].…”

Bayesian probabilistic modeling in robosoccer environment for robot path planning

“…The robot is designed to locate the most efficient path for harvesting ripe mushrooms from a field of plants planted at random. In addition, the applications of the biologically motivated meta-heuristic algorithms firefly algorithm (FA) and ant colony optimization (ACO) [8] have been researched and evaluated. An in-depth analysis and critical examination of the important contributions to path planning in dynamic contexts is conducted in [9].…”