170 research outputs found
A Tutorial on Linear Least Square Estimation
This is a brief tutorial on the least square estimation technique that is
straightforward yet effective for parameter estimation. The tutorial is focused
on the linear LSEs instead of nonlinear versions, since most nonlinear LSEs can
be approximated non-trivially using its linear counterparts. Linear LSEs can
also provide insight into the study of the nonlinear techniques, e.g.,
Gauss-Newton method and Lavenberg-Marquardt method etc. Linear LSEs are
computationally efficient for most occasions, so they are widely applied in
practice. In this tutorial, both the original batch least square estimation and
its recursive variants are reviewed comprehensively with detailed mathematical
derivations.Comment: 3 Pages, tutoria
Multi-Robot Motion Planning: A Learning-Based Artificial Potential Field Solution
Motion planning is a crucial aspect of robot autonomy as it involves
identifying a feasible motion path to a destination while taking into
consideration various constraints, such as input, safety, and performance
constraints, without violating either system or environment boundaries. This
becomes particularly challenging when multiple robots run without
communication, which compromises their real-time efficiency, safety, and
performance. In this paper, we present a learning-based potential field
algorithm that incorporates deep reinforcement learning into an artificial
potential field (APF). Specifically, we introduce an observation embedding
mechanism that pre-processes dynamic information about the environment and
develop a soft wall-following rule to improve trajectory smoothness. Our
method, while belonging to reactive planning, implicitly encodes environmental
properties. Additionally, our approach can scale up to any number of robots and
has demonstrated superior performance compared to APF and RL through numerical
simulations. Finally, experiments are conducted to highlight the effectiveness
of our proposed method.Comment: 6 page
Distributed Flocking Control of Aerial Vehicles Based on a Markov Random Field
The distributed flocking control of collective aerial vehicles has
extraordinary advantages in scalability and reliability, \emph{etc.} However,
it is still challenging to design a reliable, efficient, and responsive
flocking algorithm. In this paper, a distributed predictive flocking framework
is presented based on a Markov random field (MRF). The MRF is used to
characterize the optimization problem that is eventually resolved by
discretizing the input space. Potential functions are employed to describe the
interactions between aerial vehicles and as indicators of flight performance.
The dynamic constraints are taken into account in the candidate feasible
trajectories which correspond to random variables. Numerical simulation shows
that compared with some existing latest methods, the proposed algorithm has
better-flocking cohesion and control efficiency performances. Experiments are
also conducted to demonstrate the feasibility of the proposed algorithm.Comment: 6 Page
SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion
The Central Pattern Generator (CPG) is adept at generating rhythmic gait
patterns characterized by consistent timing and adequate foot clearance. Yet,
its open-loop configuration often compromises the system's control performance
in response to environmental variations. On the other hand, Reinforcement
Learning (RL), celebrated for its model-free properties, has gained significant
traction in robotics due to its inherent adaptability and robustness. However,
initiating traditional RL approaches from the ground up presents computational
challenges and a heightened risk of converging to suboptimal local minima. In
this paper, we propose an innovative quadruped locomotion framework, SYNLOCO,
by synthesizing CPG and RL that can ingeniously integrate the strengths of both
methods, enabling the development of a locomotion controller that is both
stable and natural. Furthermore, we introduce a set of performance-driven
reward metrics that augment the learning of locomotion control. To optimize the
learning trajectory of SYNLOCO, a two-phased training strategy is presented.
Our empirical evaluation, conducted on a Unitree GO1 robot under varied
conditions--including distinct velocities, terrains, and payload
capacities--showcases SYNLOCO's ability to produce consistent and clear-footed
gaits across diverse scenarios. The developed controller exhibits resilience
against substantial parameter variations, underscoring its potential for robust
real-world applications.Comment: 7 Page
DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention
Integrating rule-based policies into reinforcement learning promises to
improve data efficiency and generalization in cooperative pursuit problems.
However, most implementations do not properly distinguish the influence of
neighboring robots in observation embedding or inter-robot interaction rules,
leading to information loss and inefficient cooperation. This paper proposes a
cooperative pursuit algorithm named Decentralized Adaptive COOperative Pursuit
via Attention (DACOOP-A) by empowering reinforcement learning with artificial
potential field and attention mechanisms. An attention-based framework is
developed to emphasize important neighbors by concurrently integrating the
learned attention scores into observation embedding and inter-robot interaction
rules. A KL divergence regularization is introduced to alleviate the resultant
learning stability issue. Improvements in data efficiency and generalization
are demonstrated through numerical simulations. Extensive quantitative analysis
and ablation studies are performed to illustrate the advantages of the proposed
modules. Real-world experiments are performed to justify the feasibility of
deploying DACOOP-A in physical systems.Comment: 8 Pages; This manuscript has been accepted by IEEE Robotics and
Automation Letter
Formation Control for Moving Target Enclosing via Relative Localization
In this paper, we investigate the problem of controlling multiple unmanned
aerial vehicles (UAVs) to enclose a moving target in a distributed fashion
based on a relative distance and self-displacement measurements. A relative
localization technique is developed based on the recursive least square
estimation (RLSE) technique with a forgetting factor to estimates both the
``UAV-UAV'' and ``UAV-target'' relative positions. The formation enclosing
motion is planned using a coupled oscillator model, which generates desired
motion for UAVs to distribute evenly on a circle. The coupled-oscillator-based
motion can also facilitate the exponential convergence of relative localization
due to its persistent excitation nature. Based on the generation strategy of
desired formation pattern and relative localization estimates, a cooperative
formation tracking control scheme is proposed, which enables the formation
geometric center to asymptotically converge to the moving target. The
asymptotic convergence performance is analyzed theoretically for both the
relative localization technique and the formation control algorithm. Numerical
simulations are provided to show the efficiency of the proposed algorithm.
Experiments with three quadrotors tracking one target are conducted to evaluate
the proposed target enclosing method in real platforms.Comment: 8 Pages, accepted by IEEE CDC 202
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