18,342 research outputs found
Robot motion planning using real-time heuristic search
Autonomous mobile robots must be able to plan quickly and stay reactive to the world around them. Currently, navigating in the presence of dynamic obstacles is a problem that modern techniques struggle to handle in a real-time manner, even when the environment is known. The solutions range from using: 1) sampling-based algorithms which cut down on the shear size of these state spaces, 2) algorithms which quickly try to plan complete paths to the goal (to avoid local minima) and 3) using real-time search techniques designed for static worlds. Each of these methods have fundamental flaws that prevent it from being used in practice.
In this thesis I offer three proposed techniques to help improve planning among dynamic obstacles. First, I present a new partitioned learning technique for splitting the costs estimates used by heuristic search techniques into those caused by the static environment and those caused by the dynamic obstacles in the world. This allows for much more accurate learning. Second, I introduce a novel decaying heuristic technique for generalizing cost-to-go over states of the same pose (x. y.theta.v) in the world. Third, I show a garbage collection mechanism for removing useless states from our search to cut down on the overall memory usage. Finally, I present a new algorithm called Partitioned Learning Real-time A*. PLRTA* uses all three of these new enhancements to navigate through worlds with dynamic obstacles in a real-time manner while handling the complex situations in which other algorithms fail.
I empirically compare our algorithm to other competing algorithms in a number of random instances as well as hand crafted scenarios designed to highlight desirable behavior in specific situations. I show that PLRTA* outperforms the current state-of-the-art algorithms in terms of minimizing cost over a large number of robot motion planning problems, even when planning in fairly confined environments with up to ten dynamic obstacles
Real-time sampling-based motion planning with dynamic obstacles
Autonomous robots are increasingly becoming incorporated in everyday human activities, and this trend does not show any signs of slowing down. One task that autonomous robots will need to reliably perform among humans and other dynamic objects is motion planning. That is, to reliably navigate a robot to a desired pose as quickly as possible while minimizing the probability of colliding with other objects. This involves not only planning around the predicted future trajectories of dynamic obstacles, but doing so in a real-time manner so that the robot can remain reactive to its surroundings. Current methods do not directly address this problem. This thesis proposes a new real-time planning algorithm called real-time R* (RTR*). RTR* is based on the R* search algorithm that couples random sampling with heuristic search and has been shown to work well in several different robotics domains. Several modifications needed to transform R* into a real-time algorithm are described. Additional modifications that were developed specifically for this problem domain are also detailed. An empirical evaluation is given comparing RTR* with several state-of-the-art motion planning and real-time search algorithms. RTR* shows promising performance and improves on R*, however it underperforms the current state-of-the-art. Several enhancements are discussed that could improve the behavior of RTR*
Search-based 3D Planning and Trajectory Optimization for Safe Micro Aerial Vehicle Flight Under Sensor Visibility Constraints
Safe navigation of Micro Aerial Vehicles (MAVs) requires not only
obstacle-free flight paths according to a static environment map, but also the
perception of and reaction to previously unknown and dynamic objects. This
implies that the onboard sensors cover the current flight direction. Due to the
limited payload of MAVs, full sensor coverage of the environment has to be
traded off with flight time. Thus, often only a part of the environment is
covered.
We present a combined allocentric complete planning and trajectory
optimization approach taking these sensor visibility constraints into account.
The optimized trajectories yield flight paths within the apex angle of a
Velodyne Puck Lite 3D laser scanner enabling low-level collision avoidance to
perceive obstacles in the flight direction. Furthermore, the optimized
trajectories take the flight dynamics into account and contain the velocities
and accelerations along the path.
We evaluate our approach with a DJI Matrice 600 MAV and in simulation
employing hardware-in-the-loop.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Montreal, Canada, May 201
Dynamic Motion Planning for Aerial Surveillance on a Fixed-Wing UAV
We present an efficient path planning algorithm for an Unmanned Aerial
Vehicle surveying a cluttered urban landscape. A special emphasis is on
maximizing area surveyed while adhering to constraints of the UAV and partially
known and updating environment. A Voronoi bias is introduced in the
probabilistic roadmap building phase to identify certain critical milestones
for maximal surveillance of the search space. A kinematically feasible but
coarse tour connecting these milestones is generated by the global path
planner. A local path planner then generates smooth motion primitives between
consecutive nodes of the global path based on UAV as a Dubins vehicle and
taking into account any impending obstacles. A Markov Decision Process (MDP)
models the control policy for the UAV and determines the optimal action to be
undertaken for evading the obstacles in the vicinity with minimal deviation
from current path. The efficacy of the proposed algorithm is evaluated in an
updating simulation environment with dynamic and static obstacles.Comment: Accepted at International Conference on Unmanned Aircraft Systems
201
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