Abstract
This paper describes an algorithm for robotic motion planning that is capable of optimising several cost functions simultaneously to provide optimised, feasible and collision-free paths. The algorithm is based on the best-first graph search algorithm using a Pareto frontier to evaluate costs at each node. Additionally, we include a calculation of the distribution of robot trajectories when the path is realised using a LQR based controller. This ensures that the possibility of collisions is greatly reduced. Results are provided that show multi-cost robotic path planning under position uncertainty and control constraints whilst simultaneously optimising distance travelled and fuel spent.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on |
| Pages | 240-245 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 1 Sept 2014 |
Keywords
- Pareto optimisation
- collision avoidance
- graph theory
- linear quadratic control
- mobile robots
- multi-robot systems
- search problems
- trajectory control
- uncertain systems
- LQR based controller
- Pareto frontier
- best-first graph search algorithm
- collision-free path
- control constraint
- cost function
- distance travel
- fuel spent
- multicost robotic motion planning
- multicost robotic path planning
- position uncertainty
- robot trajectory
- Cost function
- Covariance matrices
- Heuristic algorithms
- Robots
- Trajectory
- Uncertainty
- Vectors
Fingerprint
Dive into the research topics of 'Multi-cost robotic motion planning under uncertainty'. Together they form a unique fingerprint.Profiles
-
Professor Arthur G Richards
- School of Engineering Mathematics and Technology - Professor of Robotics and Control
- Dynamics and Control
Person: Academic , Member