UAV SLAM and Motion Planning

We address the task of enable unmanned vehicles to explore in cluttered environment with Sampling Based Algorithm, especially Rapidly-exploring Random Tree. First, we proposed a gauassian distribution based ellipse (2D, or ellipsoid 3D) modeling approach to perform environment representation. The gaussian-ellipse model enable a virtual-force like vectorized information to guid the sampling fo random exploration. Second, we introduced a bidirectional potential field (BPF) to redistribute each newly sampled state, such that the in-collision samples can be redistributed for extension. The common potential field is a combination of goal biasing and obstacle respulsion, which does not help with a higher efficiency over narrow corridor. The proposed BPF is able to re-generate sampling around obstacle if collision occurred, where the distance toward obstacle using gaussian model acts a inverse factor for redistribution.

Illustration of gaussian-ellipse model around obstacle surface, where the gaussian model parameters are decided by the sensor model:

This is an image

To enable the using of obstacle information, especially the narrow corridor region in cluttered environment, BPF introduces internal repulsion and external attraction only for obstacle. The BPF performs re-generation around collision region, and deploys re-disbution to enable large safety envelop.

This is an image

We show some comparisons with currently successful algorithms and one real test.

This is an image This is an image This is an image

If you interest, please check the following papers:

  • [1] L. Yang, J. Xiao, J. Qi, L. Yang, L. Wang, and J. Han. GART: An environment-guided path planner for robots in crowded environments under kinodynamic constraints. International Journal of Advanced Robotic Systems, vol.13, no. 6, pp:1 ~ 12, 2016.
  • [2]L. Yang, D. Song, J. Xiao, J. Han, L. Yang, Y. Cao. Generation of Dynamically Feasible and Collision Free Trajectory by Applying Six-Order Bezier Curve and Local Optimal Reshaping. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015).
  • [3]L. Yang, J. Qi, Z. Jiang, D. Song, J. Han, and J. Xiao, “Guiding Attraction based Random Tree Path Planning under Uncertainty: Dedicate for UAV”, IEEE Int. Conf. on Mechatronics and Automation (ICMA2014), Tianjin, China, Aug.3~6, 2014. (Best Student Paper Award).

Our latest research has been invited to be published in Kinematics, InTech Press. We proposed a multi-paths planner for online obstacle avoidance, which introduces a “Extending-forbidden” method to shift the extending resource for multiple paths generation.

Illustration of multi-path generation:

This is an image

Also, we proposed a online visible path connection for switching approach for agile obstacle avoidance:

This is an image

The 3D demo is illustrated:

This is an image