93 research outputs found
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths
in high-dimensional state spaces while accounting for physical constraints
related to motors and joints, generating smooth and stable motions, avoiding
obstacles, and preventing collisions. A motion planning algorithm must
therefore balance competing demands, and should ideally incorporate uncertainty
to handle noise, model errors, and facilitate deployment in complex
environments. To address these issues, we introduce a framework for robot
motion planning based on variational Gaussian Processes, which unifies and
generalizes various probabilistic-inference-based motion planning algorithms.
Our framework provides a principled and flexible way to incorporate
equality-based, inequality-based, and soft motion-planning constraints during
end-to-end training, is straightforward to implement, and provides both
interval-based and Monte-Carlo-based uncertainty estimates. We conduct
experiments using different environments and robots, comparing against baseline
approaches based on the feasibility of the planned paths, and obstacle
avoidance quality. Results show that our proposed approach yields a good
balance between success rates and path quality
Optimized Path Planning for USVs under Ocean Currents
The proposed work focuses on the path planning for Unmanned Surface Vehicles
(USVs) in the ocean enviroment, taking into account various spatiotemporal
factors such as ocean currents and other energy consumption factors. The paper
proposes the use of Gaussian Process Motion Planning (GPMP2), a Bayesian
optimization method that has shown promising results in continuous and
nonlinear path planning algorithms. The proposed work improves GPMP2 by
incorporating a new spatiotemporal factor for tracking and predicting ocean
currents using a spatiotemporal Bayesian inference. The algorithm is applied to
the USV path planning and is shown to optimize for smoothness, obstacle
avoidance, and ocean currents in a challenging environment. The work is
relevant for practical applications in ocean scenarios where an optimal path
planning for USVs is essential for minimizing costs and optimizing performance.Comment: 9 pages and 7 figures, submitted for IEEE Transactions on Man,
systems ,and Cybernetic
Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization
A significant challenge in manipulation motion planning is to ensure agility
in the face of unpredictable changes during task execution. This requires the
identification and possible modification of suitable joint-space trajectories,
since the joint velocities required to achieve a specific endeffector motion
vary with manipulator configuration. For a given manipulator configuration, the
joint space-to-task space velocity mapping is characterized by a quantity known
as the manipulability index. In contrast to previous control-based approaches,
we examine the maximization of manipulability during planning as a way of
achieving adaptable and safe joint space-to-task space motion mappings in
various scenarios. By representing the manipulator trajectory as a
continuous-time Gaussian process (GP), we are able to leverage recent advances
in trajectory optimization to maximize the manipulability index during
trajectory generation. Moreover, the sparsity of our chosen representation
reduces the typically large computational cost associated with maximizing
manipulability when additional constraints exist. Results from simulation
studies and experiments with a real manipulator demonstrate increases in
manipulability, while maintaining smooth trajectories with more dexterous (and
therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent
Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201
Role Engine Implementation for a Continuous and Collaborative Multi-Robot System
In situations involving teams of diverse robots, assigning appropriate roles
to each robot and evaluating their performance is crucial. These roles define
the specific characteristics of a robot within a given context. The stream
actions exhibited by a robot based on its assigned role are referred to as the
process role. Our research addresses the depiction of process roles using a
multivariate probabilistic function. The main aim of this study is to develop a
role engine for collaborative multi-robot systems and optimize the behavior of
the robots. The role engine is designed to assign suitable roles to each robot,
generate approximately optimal process roles, update them on time, and identify
instances of robot malfunction or trigger replanning when necessary. The
environment considered is dynamic, involving obstacles and other agents. The
role engine operates hybrid, with central initiation and decentralized action,
and assigns unlabeled roles to agents. We employ the Gaussian Process (GP)
inference method to optimize process roles based on local constraints and
constraints related to other agents. Furthermore, we propose an innovative
approach that utilizes the environment's skeleton to address initialization and
feasibility evaluation challenges. We successfully demonstrated the proposed
approach's feasibility, and efficiency through simulation studies and
real-world experiments involving diverse mobile robots.Comment: 10 pages, 18 figures, summited in IEEE Transactions on Systems, Man
and Cybernetics(T-SMC
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