Path Planning

Reactive Path Planning

In this work, we develop a reactive guidance strategy for collision avoidance using bearing-only measurements. The guidance strategy can be used to avoid collision from circular obstacles and to follow straight and curved walls at safe distance. The guidance law moves a obstacle in the sensor field-of-view to a desired constant bearing angle, which causes the MAV to maintaina constant distance from the obstacle.

Related Publications

1. Lwowski, Jonathan, Liang Sun, Roberto Mexquitic-Saavedra, Rajnikant Sharma, and Daniel Pack. “A Reactive Bearing Angle Only Obstacle Avoidance Technique for Unmanned Ground Vehicles.” Journal of Automation and Control Research 1, no. 1 (2014).

2. Rajnikant Sharma, Jeffery Saunders, Randal W. Beard, “Reactive path planning for micro air vehicles using bearing only measurements”, Journal of Intelligent and Robotic Systems, Volume 65, Issue 1 (2012), p.409-416.

Related Video

Observability-based path planning

In this work  we develop  an observability-based local path planning and obstacle avoidance technique that utilizes an extended Kalman Filter (EKF) to estimate the time-to-collision (TTC) and bearing to obstacles using bearing-only measurements. To ensure that the error covariance matrix computed by an EKF is bounded, the system should be observable. We perform a nonlinear observability analysis to obtain the necessary conditions for complete observability of the system. These conditions are used to explicitly design a path planning algorithm that enhances observability while simultaneously avoiding collisions with obstacles. We analyze the behavior of the path planning algorithm and specially define the environments where the path planning algorithm will guarantee collision-free paths that lead to a goal configuration.

Related Publication

  1. Huili Yu, Rajnikant Sharma, Randal W. Beard, “Observability-based local path planning and collision avoidance for micro air vehicles using bearing-only measurements”, Robotics and Autonomous Systems, 2013.

Related Video

Quadrotor Path Following



 

 

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