Sign in to your account

Richard Fisher

Introduction

DRM-connect is an algorithm for motion planning and replanning, and is a combination of dynamic reachability maps (DRM) with lazy collision checking and a fallback strategy based on the RRT-connect algorithm, which is used to repair the roadmap through further exploration.

Trajectory planning and replanning in complex environments often reuses very little information from previous solutions. This is particularly evident when the motion is repeated multiple times with only a limited amount of variation between each run. Graph-based planning offers fast replanning at the cost of significant pre-computation, while probabilistic planning requires no pre-computation at the cost of slow replanning.

We attempt to offer the best of both by proposing the DRM-connect algorithm.

Algorithm

Offline, an approximate Reeb graph is constructed from the trajectories of prior tasks in the same or similar environments.

For a new planning or replanning query, DRM-connect searches this Reeb graph for a trajectory to complete the task (checking collisions lazily). If no path is found, DRM-connect iterates between attempting to repair the disconnected subgraphs through a process similar to RRT-connect (operating on multiple graphs, rather than trees) and searching for paths through the graph. Since DRM-connect is probabilistically complete, the likelihood of a successful trajectory being returned approaches one as time tends to infinity.

Further work will incorporate online updates...

RAIL Lab

We are co-organising the 2nd annual Deep Learning Indaba which will be held at Stellenbosch University from 9-14 September 2018.

The Deep Learning Indaba exists to celebrate and strengthen machine learning in Africa through state-of-the-art teaching, networking, policy debate, and through our support programmes, such as the IndabaX and the Kambule and Maathai awards. The Indaba works towards the vision of Africans becoming critical contributors, owners, and shapers of the coming advances in artificial intelligence and machine learning. The report on the outcomes of the first Indaba 2017 can be read here.