38,998 research outputs found
Multi-layer approach to motion planning in obstacle rich environment
A widespread use of robotic technology in civilian and military applications has
generated a need for advanced motion planning algorithms that are real-time implementable.
These algorithms are required to navigate autonomous vehicles through
obstacle-rich environments. This research has led to the development of the multilayer
trajectory generation approach. It is built on the principle of separation of
concerns, which partitions a given problem into multiple independent layers, and addresses
complexity that is inherent at each level. We partition the motion planning
algorithm into a roadmap layer and an optimal control layer. At the roadmap layer,
elements of computational geometry are used to process the obstacle rich environment
and generate feasible sets. These are used by the optimal control layer to generate
trajectories while satisfying dynamics of the vehicle. The roadmap layer ignores the
dynamics of the system, and the optimal control layer ignores the complexity of the
environment, thus achieving a separation of concern. This decomposition enables
computationally tractable methods to be developed for addressing motion planning
in complex environments. The approach is applied in known and unknown environments.
The methodology developed in this thesis has been successfully applied to a 6
DOF planar robotic testbed. Simulation results suggest that the planner can generate
trajectories that navigate through obstacles while satisfying dynamical constraints
Multi-layer approach to motion planning in obstacle rich environment
A widespread use of robotic technology in civilian and military applications has
generated a need for advanced motion planning algorithms that are real-time implementable.
These algorithms are required to navigate autonomous vehicles through
obstacle-rich environments. This research has led to the development of the multilayer
trajectory generation approach. It is built on the principle of separation of
concerns, which partitions a given problem into multiple independent layers, and addresses
complexity that is inherent at each level. We partition the motion planning
algorithm into a roadmap layer and an optimal control layer. At the roadmap layer,
elements of computational geometry are used to process the obstacle rich environment
and generate feasible sets. These are used by the optimal control layer to generate
trajectories while satisfying dynamics of the vehicle. The roadmap layer ignores the
dynamics of the system, and the optimal control layer ignores the complexity of the
environment, thus achieving a separation of concern. This decomposition enables
computationally tractable methods to be developed for addressing motion planning
in complex environments. The approach is applied in known and unknown environments.
The methodology developed in this thesis has been successfully applied to a 6
DOF planar robotic testbed. Simulation results suggest that the planner can generate
trajectories that navigate through obstacles while satisfying dynamical constraints
AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space
Quadrotor motion planning in complex environments leverage the concept of
safe flight corridor (SFC) to facilitate static obstacle avoidance. Typically,
SFCs are constructed through convex decomposition of the environment's free
space into cuboids, convex polyhedra, or spheres. However, when dealing with a
quadrotor swarm, such SFCs can be overly conservative, substantially limiting
the available free space for quadrotors to coordinate. This paper presents an
Alternating Minimization-based approach that does not require building a
conservative free-space approximation. Instead, both static and dynamic
collision constraints are treated in a unified manner. Dynamic collisions are
handled based on shared position trajectories of the quadrotors. Static
obstacle avoidance is coupled with distance queries from the Octomap, providing
an implicit non-convex decomposition of free space. As a result, our approach
is scalable to arbitrary complex environments. Through extensive comparisons in
simulation, we demonstrate a improvement in success rate, an average
reduction in mission completion time, and an average
reduction in per-agent computation time compared to SFC-based approaches. We
also experimentally validated our approach using a Crazyflie quadrotor swarm of
up to 12 quadrotors in obstacle-rich environments. The code, supplementary
materials, and videos are released for reference.Comment: Submitted to ICRA 202
Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective
Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage.
In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient.
The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions.
Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems
Minimum-time trajectory generation for quadrotors in constrained environments
In this paper, we present a novel strategy to compute minimum-time
trajectories for quadrotors in constrained environments. In particular, we
consider the motion in a given flying region with obstacles and take into
account the physical limitations of the vehicle. Instead of approaching the
optimization problem in its standard time-parameterized formulation, the
proposed strategy is based on an appealing re-formulation. Transverse
coordinates, expressing the distance from a frame path, are used to
parameterise the vehicle position and a spatial parameter is used as
independent variable. This re-formulation allows us to (i) obtain a fixed
horizon problem and (ii) easily formulate (fairly complex) position
constraints. The effectiveness of the proposed strategy is proven by numerical
computations on two different illustrative scenarios. Moreover, the optimal
trajectory generated in the second scenario is experimentally executed with a
real nano-quadrotor in order to show its feasibility.Comment: arXiv admin note: text overlap with arXiv:1702.0427
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