Robust trajectory execution is an extension of cooperative collision avoidance that takes pre-planned trajectories directly into account. We propose an algorithm for robust trajectory execution that compensates for a variety of dynamic changes, including newly appearing obstacles, robots breaking down, imperfect motion execution, and external disturbances. Robots do not communicate with each other and only sense other robots’ positions and the obstacles around them. At the high-level we use a hybrid planning strategy employing both discrete planning and trajectory optimization with a dynamic receding horizon approach. The discrete planner helps to avoid local minima, adjusts the planning horizon, and provides good initial guesses for the optimization stage. Trajectory optimization uses a quadratic programming formulation, where all safety-critical parts are formulated as hard constraints. At the low-level, we use buffered Voronoi cells as a multi-robot collision avoidance strategy. Compared to ORCA, our approach supports higher-order dynamic limits and avoids deadlocks better. We demonstrate our approach in simulation and on physical robots, showing that it can operate in real time.
Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation
Cooperatively avoiding collision is a critical functionality
for robots navigating in dense human crowds, failure
of which could lead to either overaggressive or overcautious
behavior. A necessary condition for cooperative collision avoidance
is to couple the prediction of the agents’ trajectories with
the planning of the robot’s trajectory. However, it is unclear
that trajectory based cooperative collision avoidance captures the
correct agent attributes. In this work we migrate from trajectory
based coupling to a formalism that couples agent preference
distributions. In particular, we show that preference distributions
(probability density functions representing agents’ intentions)
can capture higher order statistics of agent behaviors, such as
willingness to cooperate. Thus, coupling in distribution space
exploits more information about inter-agent cooperation than
coupling in trajectory space. We thus introduce a general objective
for coupled prediction and planning in distribution space,
and propose an iterative best response optimization method
based on variational analysis with guaranteed sufficient decrease.
Based on this analysis, we develop a sampling-based motion
planning framework called DistNav1 that runs in real time on a
laptop CPU. We evaluate our approach on challenging scenarios
from both real world datasets and simulation environments, and
benchmark against a wide variety of model based and machine
learning based approaches. The safety and efficiency statistics
of our approach outperform all other models. Finally, we find
that DistNav is competitive with human safety more »
- Award ID(s):
- 1837515
- Publication Date:
- NSF-PAR ID:
- 10283235
- Journal Name:
- Robotics: Science and Systems
- Sponsoring Org:
- National Science Foundation
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Robust trajectory execution is an extension of cooperative collision avoidance that takes pre-planned trajectories directly into account. We propose an algorithm for robust trajectory execution that compensates for a variety of dynamic changes, including newly appearing obstacles, robots breaking down, imperfect motion execution, and external disturbances. Robots do not communicate with each other and only sense other robots’ positions and the obstacles around them. At the high-level we use a hybrid planning strategy employing both discrete planning and trajectory optimization with a dynamic receding horizon approach. The discrete planner helps to avoid local minima, adjusts the planning horizon, and provides good initial guesses for the optimization stage. Trajectory optimization uses a quadratic programming formulation, where all safety-critical parts are formulated as hard constraints. At the low-level, we use buffered Voronoi cells as a multi-robot collision avoidance strategy. Compared to ORCA, our approach supports higher-order dynamic limits and avoids deadlocks better. We demonstrate our approach in simulation and on physical robots, showing that it can operate in real time.
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.15607/RSS.2021.XVII.053
