2,587 research outputs found
Efficient Path Interpolation and Speed Profile Computation for Nonholonomic Mobile Robots
This paper studies path synthesis for nonholonomic mobile robots moving in
two-dimensional space. We first address the problem of interpolating paths
expressed as sequences of straight line segments, such as those produced by
some planning algorithms, into smooth curves that can be followed without
stopping. Our solution has the advantage of being simpler than other existing
approaches, and has a low computational cost that allows a real-time
implementation. It produces discretized paths on which curvature and variation
of curvature are bounded at all points, and preserves obstacle clearance. Then,
we consider the problem of computing a time-optimal speed profile for such
paths. We introduce an algorithm that solves this problem in linear time, and
that is able to take into account a broader class of physical constraints than
other solutions. Our contributions have been implemented and evaluated in the
framework of the Eurobot contest
A Dynamic Localized Adjustable Force Field Method for Real-time Assistive Non-holonomic Mobile Robotics
Providing an assistive navigation system that augments
rather than usurps user control of a powered wheelchair
represents a significant technical challenge. This paper
evaluates an assistive collision avoidance method for a
powered wheelchair that allows the user to navigate safely
whilst maintaining their overall governance of the platform
motion. The paper shows that by shaping, switching and
adjusting localized potential fields we are able to negotiate
different obstacles by generating a more intuitively natural
trajectory, one that does not deviate significantly from the
operator in the loop desired-trajectory. It can also be seen
that this method does not suffer from the local minima
problem, or narrow corridor and proximity oscillation,
which are common problems that occur when using
potential fields. Furthermore this localized method enables
the robotic platform to pass very close to obstacles, such as
when negotiating a narrow passage or doorway
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
Asymptotically Optimal Sampling-Based Motion Planning Methods
Motion planning is a fundamental problem in autonomous robotics that requires
finding a path to a specified goal that avoids obstacles and takes into account
a robot's limitations and constraints. It is often desirable for this path to
also optimize a cost function, such as path length.
Formal path-quality guarantees for continuously valued search spaces are an
active area of research interest. Recent results have proven that some
sampling-based planning methods probabilistically converge toward the optimal
solution as computational effort approaches infinity. This survey summarizes
the assumptions behind these popular asymptotically optimal techniques and
provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics,
and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews,
https://www.annualreviews.org/. 25 pages. 2 figure
Rest-to-Rest Trajectory Planning for Underactuated Cable-Driven Parallel Robots
This article studies the trajectory planning for underactuated cable-driven parallel robots (CDPRs) in the case of rest-to-rest motions, when both the motion time and the path geometry are prescribed. For underactuated manipulators, it is possible to prescribe a control law only for a subset of the generalized coordinates of the system. However, if an arbitrary trajectory is prescribed for a suitable subset of these coordinates, the constraint deficiency on the end-effector leads to the impossibility of bringing the system at rest in a prescribed time. In addition, the behavior of the system may not be stable, that is, unbounded oscillatory motions of the end-effector may arise. In this article, we propose a novel trajectory-planning technique that allows the end effector to track a constrained geometric path in a specified time, and allows it to transition between stable static poses. The design of such a motion is based on the solution of a boundary value problem, aimed at a finding solution to the differential equations of motion with constraints on position and velocity at start and end times. To prove the effectiveness of such a method, the trajectory planning of a six-degrees-of-freedom spatial CDPR suspended by three cables is investigated. Trajectories of a reference point on the moving platform are designed so as to ensure that the assigned path is tracked accurately, and the system is brought to a static condition in a prescribed time. Experimental validation is presented and discussed
Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems
We present a light-weight body-terrain clearance evaluation algorithm for the
automated path planning of NASA's Mars 2020 rover. Extraterrestrial path
planning is challenging due to the combination of terrain roughness and severe
limitation in computational resources. Path planning on cluttered and/or uneven
terrains requires repeated safety checks on all the candidate paths at a small
interval. Predicting the future rover state requires simulating the vehicle
settling on the terrain, which involves an inverse-kinematics problem with
iterative nonlinear optimization under geometric constraints. However, such
expensive computation is intractable for slow spacecraft computers, such as
RAD750, which is used by the Curiosity Mars rover and upcoming Mars 2020 rover.
We propose the Approximate Clearance Evaluation (ACE) algorithm, which obtains
conservative bounds on vehicle clearance, attitude, and suspension angles
without iterative computation. It obtains those bounds by estimating the lowest
and highest heights that each wheel may reach given the underlying terrain, and
calculating the worst-case vehicle configuration associated with those extreme
wheel heights. The bounds are guaranteed to be conservative, hence ensuring
vehicle safety during autonomous navigation. ACE is planned to be used as part
of the new onboard path planner of the Mars 2020 rover. This paper describes
the algorithm in detail and validates our claim of conservatism and fast
computation through experiments
Autonomous golf ball picking robot design and development
The golf ball picking task is a daily that requires human intensive labor. This document presents the hardware developing process of an autonomous golf ball picking robot which aims to efficiently perform this task. It has a maintenance capacity of a 25,000 m2 practice field. Compared to a similar device in the market this robot has twice the maximum speed and three times more container capacity.QREN GOLFmINHO nº1583, FCt - Fundação Ciência e Tecnologia
Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics
In this paper, we address the problem of real-time motion planning for
multiple robotic manipulators that operate in close proximity. We build upon
the concept of dynamic fabrics and extend them to multi-robot systems, referred
to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very
high planning frequency for high-dimensional systems at the expense of being
reactive and prone to deadlocks. To detect and resolve deadlocks, we propose
Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We
validate the methods in simulated close-proximity pick-and-place scenarios with
multiple manipulators, showing high success rates and real-time performance.Comment: 6 pages + 1 page references, 2 tables, 4 figures, preprint version to
accepted paper to IEEE International Symposium on Multi-Robot & Multi-Agent
Systems, Boston, 202
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