2 research outputs found
Collaborative Planning for Catching and Transporting Objects in Unstructured Environments
Multi-robot teams have attracted attention from industry and academia for
their ability to perform collaborative tasks in unstructured environments, such
as wilderness rescue and collaborative transportation.In this paper, we propose
a trajectory planning method for a non-holonomic robotic team with
collaboration in unstructured environments.For the adaptive state collaboration
of a robot team to catch and transport targets to be rescued using a net, we
model the process of catching the falling target with a net in a continuous and
differentiable form.This enables the robot team to fully exploit the kinematic
potential, thereby adaptively catching the target in an appropriate
state.Furthermore, the size safety and topological safety of the net, resulting
from the collaborative support of the robots, are guaranteed through geometric
constraints.We integrate our algorithm on a car-like robot team and test it in
simulations and real-world experiments to validate our performance.Our method
is compared to state-of-the-art multi-vehicle trajectory planning methods,
demonstrating significant performance in efficiency and trajectory quality
An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments
As a core part of autonomous driving systems, motion planning has received
extensive attention from academia and industry. However, real-time trajectory
planning capable of spatial-temporal joint optimization is challenged by
nonholonomic dynamics, particularly in the presence of unstructured
environments and dynamic obstacles. To bridge the gap, we propose a real-time
trajectory optimization method that can generate a high-quality whole-body
trajectory under arbitrary environmental constraints. By leveraging the
differential flatness property of car-like robots, we simplify the trajectory
representation and analytically formulate the planning problem while
maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve
efficient obstacle avoidance with a safe driving corridor for unmodelled
obstacles and signed distance approximations for dynamic moving objects. We
present comprehensive benchmarks with State-of-the-Art methods, demonstrating
the significance of the proposed method in terms of efficiency and trajectory
quality. Real-world experiments verify the practicality of our algorithm. We
will release our codes for the research communit