Multi-horizon reactive and deliberative path planning for autonomous cross-country navigation

摘要

As part of the Raptor system developed for DARPA's PerceptOR program, three path planning methods have been integrated together in the framework of a command-arbitration based architecture. These methods combine reactive and deliberative elements, performing path planning within different planning horizons. Short range path planning (< 10 m) is done by a module called OAradials. OAradials is purely reactive, evaluating arcs corresponding to possible steering commands for the proximity of discrete obstacles, abrupt elevation changes, and unsafe slope conditions. Medium range path planning ( < 30 m) is performed by a module called Biased Random Trees - Follow Path (BRT-FP). Based on LaValle and Kuffner's rapidly exploring random trees planning algorithm, BRT-FP continuously evaluates the local terrain map in order to generate a good path that advances the robot towards the next intermediate waypoint in a user-specified plan. A pure-pursuit control algorithm generates candidate steering commands intended to keep the robot on the generated path. Long range path planning is done by the Dynamic Planner (DPlanner) using Stentz' D~* algorithm. Use of D~* allows efficient exploitation of prior terrain data and dynamic replanning as terrain is explored. Outputs from DPlanner generate intermediate goal points that are fed to the BRT-FP planner. A command-level arbitration scheme selects steering commands based on the weighted sum of the steering preferences generated by the OAradials and BRT-FP path planning behaviors. This system has been implemented on an ATV platform that has been actuated for autonomous operation, and tested on realistic crosscountry terrain in the context of the PerceptOR program.