Stochastic mobility-based path planning in uncertain environments

Abstract

The ability of mobile robots to generate feasible trajectories online is an important requirement for their autonomous operation in unstructured environments. Many path generation techniques focus on generation of time-or distance-optimal paths while obeying dynamic constraints, and often assume precise knowledge of robot and/or environmental (i.e. terrain) properties. In uneven terrain, it is essential that the robot mobility over the terrain be explicitly considered in the planning process. Further, since significant uncertainty is often associated with robot and/or terrain parameter knowledge, this should also be accounted for in a path generation algorithm. Here, extensions to the rapidly exploring random tree (RRT) algorithm are presented that explicitly consider robot mobility and robot parameter uncertainty based on the stochastic response surface method (SRSM). Simulation results suggest that the proposed approach can be used for generating safe paths on uncertain, uneven terrain.

Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees

“…Particle RRT 23 uses particles to sample uncertain motion, while Kewlani et al use a finite-series approximation of the uncertainty propagation. 24 Pepy et al outer-approximate uncertainty sets to guarantee robust feasibility for nonlinear systems subject to bounded internal uncertainty. 25 The LQG-MP algorithm 26 linearizes nonlinear dynamics subject to motion and sensing uncertainty, applying LQG to connect states within an RRT.…”

Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty

“…A recently-proposed algorithm identifies a finite-series approximation of the uncertainty propagation, in order to reduce model complexity and the resulting number of simulations needed per node. 21 In contrast, the proposed approach in this paper requires only one simulation per node, with optional additional calculations to compute collision probability at each time step.…”

Multi-agent motion planning for nonlinear Gaussian systems

“…However, the RRT does not explicitly incorporate uncertainty. Recently, efforts have been made to extend the RRT algorithm to an uncertain environment (Fulgenzi, Tay, Spalanzani, & Laugier, 2008;Kewlani, Ishigami, & Iagnemma, 2009;Melchior & Simmons, 2007). In this paper, we also propose an extension of the RRT algorithm to handle uncertainty in dynamic environments.…”