Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

Abstract

Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.

Real-Time Path Planning for Robot Using OP-PRM in Complex Dynamic Environment

“…Hrabar ( 2008 ) proposed a combination of the PRM algorithm and D* Lite for path planning, where a stereo camera embedded in the robot is used to detect obstacles and dynamically update the path in unknown configuration space. Khaksar et al ( 2019 ) also proposed a combination of the D* Lite algorithm and random roadmap algorithm for path planning in complex terrain.…”

A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots

“…In another study, the LTL-A* algorithm was used calculate a globally optimal path specified by linear temporal logic (LTL) and a weighted transition system [20]. On the other hand, neural network and deep learning-based methods have been proposed recently such as radial basis function neural network (RBFNN) applied for trajectory tracking of industrial Manutec-r15 robot [21], while grid-based search on randomized maps has been adopted in [22]. Recently, a number of hybrid and nature-inspired algorithms were suggested such as particle swarm optimization-modified frequency bat (PSO-MFB) algorithm for multi-target path planning [23], firefly algorithm for trajectory planning in highly uncertain environment [24], dragonfly algorithm [25], a hybrid beetle antennae search (BAS) and artificial potential field (APF) algorithm [26].…”