MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

Abstract

This paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.

Fast Kinodynamic Planning on the Constraint Manifold With Deep Neural Networks

“…To avoid problems stemming from linearization, authors of [38] proposed to use a gradient-based method to solve the Nonlinear Programming (NLP) problem of connecting any two configurations. A similar idea was recently presented in [39], where Model Predictive Control (MPC) was used to perform a tree extension. One of the issues of sampling-based kinodynamic planning is the necessity of sampling high-dimensional state space (due to the inclusion of the velocities in the state).…”

Recent advances in Rapidly-exploring random tree: A review

“…[ 160 ], the nominal mean value of the stochastic control distribution in the model predictive path integral is provided by RRT, leading to satisfactory control performance in both static and dynamic environments without any parameter fine-tuning. Other applications include manipulator dynamic obstacle avoidance [ 161 ], hybrid assembly path planning for complex products [ 162 ], 10-DOF rover traversing over 3D uneven terrains [ 163 ], UAV path planning [ 77 , 151 , 164 ], electric inspection robot navigation [ 165 ], cobot in dynamic environment [ 166 ], underground vehicles [ 167 ], automated guided vehicle [ 145 ], mining truck [ 143 ], redundant robots [ 168 ].…”

Real‐time collision‐free landing path planning for drone deliveries in urban environments

“…Extant studies have examined local path planning obstacle avoidance methods in which candidate paths are generated in advance, and the path of maximum cost is selected [20]. Other methods have applied RRT schemes [21,22].…”