Speed Daemon: Experience-Based Mobile Robot Speed Scheduling

Abstract

A time-optimal speed schedule results in a mobile robot driving along a planned path at or near the limits of the robots capability. However, deriving models to predict the effect of increased speed can be very difficult. In this paper, we present a speed scheduler that uses previous experience, instead of complex models, to generate time-optimal speed schedules. The algorithm is designed for a vision-based, path-repeating mobile robot and uses experience to ensure reliable localization, low path-tracking errors, and realizable control inputs while maximizing the speed along the path. To our knowledge, this is the first speed scheduler to incorporate experience from previous path traversals in order to address system constraints. The proposed speed scheduler was tested in over 4 km of path traversals in outdoor terrain using a large Ackermann-steered robot travelling between 0.5 m/s and 2.0 m/s. The approach to speed scheduling is shown to generate fast speed schedules while remaining within the limits of the robots capability.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2014
Accession Number
AD1017667

Entities

People

  • Angela P. Schoellig
  • Chris J. Ostafew
  • Jack Collier
  • Timothy D. Barfoot

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Actuators
  • Algorithms
  • Cameras
  • Collision Avoidance
  • Deceleration
  • Engineering
  • Environment
  • Motion Planning
  • Reliability
  • Robots
  • Scheduling (Production)
  • Stereo Cameras
  • Test Vehicles
  • Trajectories
  • Travel Time
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Operations Research
  • Parallel and Distributed Computing.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy