1,051 research outputs found
Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance
Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a
reliable and robust collision avoidance technique. In this paper we address the
problem of multi-MAV reactive collision avoidance. A model-based controller is
employed to achieve simultaneously reference trajectory tracking and collision
avoidance. Moreover, we also account for the uncertainty of the state estimator
and the other agents position and velocity uncertainties to achieve a higher
degree of robustness. The proposed approach is decentralized, does not require
collision-free reference trajectory and accounts for the full MAV dynamics. We
validated our approach in simulation and experimentally.Comment: Video available on: https://www.youtube.com/watch?v=Ot76i9p2ZZo&t=40
Statistically Distinct Plans for Multi-Objective Task Assignment
We study the problem of finding statistically distinct plans for stochastic
planning and task assignment problems such as online multi-robot pickup and
delivery (MRPD) when facing multiple competing objectives. In many real-world
settings robot fleets do not only need to fulfil delivery requests, but also
have to consider auxiliary objectives such as energy efficiency or avoiding
human-centered work spaces. We pose MRPD as a multi-objective optimization
problem where the goal is to find MRPD policies that yield different trade-offs
between given objectives. There are two main challenges: 1) MRPD is
computationally hard, which limits the number of trade-offs that can reasonably
be computed, and 2) due to the random task arrivals, one needs to consider
statistical variance of the objective values in addition to the average. We
present an adaptive sampling algorithm that finds a set of policies which i)
are approximately optimal, ii) approximate the set of all optimal solutions,
and iii) are statistically distinguishable. We prove completeness and adapt a
state-of-the-art MRPD solver to the multi-objective setting for three example
objectives. In a series of simulation experiments we demonstrate the advantages
of the proposed method compared to baseline approaches and show its robustness
in a sensitivity analysis. The approach is general and could be adapted to
other multi-objective task assignment and planning problems under uncertainty
Designing Heterogeneous Robot Fleets for Task Allocation and Sequencing
We study the problem of selecting a fleet of robots to service spatially
distributed tasks with diverse requirements within time-windows. The problem of
allocating tasks to a fleet of potentially heterogeneous robots and finding an
optimal sequence for each robot is known as multi-robot task assignment (MRTA).
Most state-of-the-art methods focus on the problem when the fleet of robots is
fixed. In contrast, we consider that we are given a set of available robot
types and requested tasks, and need to assemble a fleet that optimally services
the tasks while the cost of the fleet remains under a budget limit. We
characterize the complexity of the problem and provide a Mixed-Integer Linear
Program (MILP) formulation. Due to poor scalability of the MILP, we propose a
heuristic solution based on a Large Neighbourhood Search (LNS). In simulations,
we demonstrate that the proposed method requires substantially lower budgets
than a greedy algorithm to service all tasks
TrajFlow: Learning the Distribution over Trajectories
Predicting the future behaviour of people remains an open challenge for the
development of risk-aware autonomous vehicles. An important aspect of this
challenge is effectively capturing the uncertainty which is inherent to human
behaviour. This paper studies an approach for probabilistic motion forecasting
with improved accuracy in the predicted sample likelihoods. We are able to
learn multi-modal distributions over the motions of an agent solely from data,
while also being able to provide predictions in real-time. Our approach
achieves state-of-the-art results on the inD dataset when evaluated with the
standard metrics employed for motion forecasting. Furthermore, our approach
also achieves state-of-the-art results when evaluated with respect to the
likelihoods it assigns to its generated trajectories. Evaluations on artificial
datasets indicate that the distributions learned by our model closely
correspond to the true distributions observed in data and are not as prone
towards being over-confident in a single outcome in the face of uncertainty
Scalarizing Multi-Objective Robot Planning Problems using Weighted Maximization
When designing a motion planner for autonomous robots there are usually
multiple objectives to be considered. However, a cost function that yields the
desired trade-off between objectives is not easily obtainable. A common
technique across many applications is to use a weighted sum of relevant
objective functions and then carefully adapt the weights. However, this
approach may not find all relevant trade-offs even in simple planning problems.
Thus, we study an alternative method based on a weighted maximum of objectives.
Such a cost function is more expressive than the weighted sum, and we show how
it can be deployed in both continuous- and discrete-space motion planning
problems. We propose a novel path planning algorithm for the proposed cost
function and establish its correctness, and present heuristic adaptations that
yield a practical runtime. In extensive simulation experiments, we demonstrate
that the proposed cost function and algorithm are able to find a wider range of
trade-offs between objectives (i.e., Pareto-optimal solutions) for various
planning problems, showcasing its advantages in practice
Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians
Prediction of human motions is key for safe navigation of autonomous robots
among humans. In cluttered environments, several motion hypotheses may exist
for a pedestrian, due to its interactions with the environment and other
pedestrians.
Previous works for estimating multiple motion hypotheses require a large
number of samples which limits their applicability in real-time motion
planning. In this paper, we present a variational learning approach for
interaction-aware and multi-modal trajectory prediction based on deep
generative neural networks.
Our approach can achieve faster convergence and requires significantly fewer
samples comparing to state-of-the-art methods. Experimental results on real and
simulation data show that our model can effectively learn to infer different
trajectories. We compare our method with three baseline approaches and present
performance results demonstrating that our generative model can achieve higher
accuracy for trajectory prediction by producing diverse trajectories.Comment: Accepted, 12 pages, 4 figure
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