3,343 research outputs found
Path evaluation for a mobile robot based on a risk of collision
An odometry system that mobile robot uses for positioning has cumulative error because of wheels' slippage and uneven ground. It causes a risk of collision of obstacles. Therefore, we propose a path evaluation method for a mobile robot based on a risk of collision. To evaluate a robot's path, we define an evaluation value as an integral of a risk of collision along the path. To evaluate the risk of collision at each point, we use an estimated positioning error generated in the odometry system. Using the evaluation method, the robot can plan a path based on a risk of collision, not the shortest path. We also consider sensing points planning for position adjustment of the mobile robot, based on the same approach. Some examples of path evaluation results support a validity of the proposed method.</p
Collision Free Navigation of a Multi-Robot Team for Intruder Interception
In this report, we propose a decentralised motion control algorithm for the
mobile robots to intercept an intruder entering (k-intercepting) or escaping
(e-intercepting) a protected region. In continuation, we propose a
decentralized navigation strategy (dynamic-intercepting) for a multi-robot team
known as predators to intercept the intruders or in the other words, preys,
from escaping a siege ring which is created by the predators. A necessary and
sufficient condition for the existence of a solution of this problem is
obtained. Furthermore, we propose an intelligent game-based decision-making
algorithm (IGD) for a fleet of mobile robots to maximize the probability of
detection in a bounded region. We prove that the proposed decentralised
cooperative and non-cooperative game-based decision-making algorithm enables
each robot to make the best decision to choose the shortest path with minimum
local information. Then we propose a leader-follower based collision-free
navigation control method for a fleet of mobile robots to traverse an unknown
cluttered environment where is occupied by multiple obstacles to trap a target.
We prove that each individual team member is able to traverse safely in the
region, which is cluttered by many obstacles with any shapes to trap the target
while using the sensors in some indefinite switching points and not
continuously, which leads to saving energy consumption and increasing the
battery life of the robots consequently. And finally, we propose a novel
navigation strategy for a unicycle mobile robot in a cluttered area with moving
obstacles based on virtual field force algorithm. The mathematical proof of the
navigation laws and the computer simulations are provided to confirm the
validity, robustness, and reliability of the proposed methods
A snake-based scheme for path planning and control with constraints by distributed visual sensors
YesThis paper proposes a robot navigation scheme using wireless visual sensors deployed in an environment.
Different from the conventional autonomous robot approaches, the scheme intends to relieve massive on-board
information processing required by a robot to its environment so that a robot or a vehicle with less intelligence can
exhibit sophisticated mobility. A three-state snake mechanism is developed for coordinating a series of sensors to
form a reference path. Wireless visual sensors communicate internal forces with each other along the reference snake
for dynamic adjustment, react to repulsive forces from obstacles, and activate a state change in the snake body from a
flexible state to a rigid or even to a broken state due to kinematic or environmental constraints. A control snake is
further proposed as a tracker of the reference path, taking into account the robot’s non-holonomic constraint and
limited steering power. A predictive control algorithm is developed to have an optimal velocity profile under robot
dynamic constraints for the snake tracking. They together form a unified solution for robot navigation by distributed
sensors to deal with the kinematic and dynamic constraints of a robot and to react to dynamic changes in advance.
Simulations and experiments demonstrate the capability of a wireless sensor network to carry out low-level control
activities for a vehicle.Royal Society, Natural Science Funding Council (China
Behavioural strategy for indoor mobile robot navigation in dynamic environments
PhD ThesisDevelopment of behavioural strategies for indoor mobile navigation has become a challenging
and practical issue in a cluttered indoor environment, such as a hospital or factory, where
there are many static and moving objects, including humans and other robots, all of which
trying to complete their own specific tasks; some objects may be moving in a similar direction
to the robot, whereas others may be moving in the opposite direction. The key requirement
for any mobile robot is to avoid colliding with any object which may prevent it from reaching
its goal, or as a consequence bring harm to any individual within its workspace. This challenge
is further complicated by unobserved objects suddenly appearing in the robots path,
particularly when the robot crosses a corridor or an open doorway. Therefore the mobile
robot must be able to anticipate such scenarios and manoeuvre quickly to avoid collisions.
In this project, a hybrid control architecture has been designed to navigate within dynamic
environments. The control system includes three levels namely: deliberative, intermediate
and reactive, which work together to achieve short, fast and safe navigation. The deliberative
level creates a short and safe path from the current position of the mobile robot to its goal
using the wavefront algorithm, estimates the current location of the mobile robot, and extracts
the region from which unobserved objects may appear. The intermediate level links the
deliberative level and the reactive level, that includes several behaviours for implementing
the global path in such a way to avoid any collision.
In avoiding dynamic obstacles, the controller has to identify and extract obstacles from the
sensor data, estimate their speeds, and then regular its speed and direction to minimize the
collision risk and maximize the speed to the goal. The velocity obstacle approach (VO) is
considered an easy and simple method for avoiding dynamic obstacles, whilst the collision
cone principle is used to detect the collision situation between two circular-shaped objects.
However the VO approach has two challenges when applied in indoor environments. The
first challenge is extraction of collision cones of non-circular objects from sensor data, in
which applying fitting circle methods generally produces large and inaccurate collision cones
especially for line-shaped obstacle such as walls. The second challenge is that the mobile
robot cannot sometimes move to its goal because all its velocities to the goal are located
within collision cones. In this project, a method has been demonstrated to extract the colliii
sion cones of circular and non-circular objects using a laser sensor, where the obstacle size
and the collision time are considered to weigh the robot velocities. In addition the principle
of the virtual obstacle was proposed to minimize the collision risk with unobserved moving
obstacles. The simulation and experiments using the proposed control system on a Pioneer
mobile robot showed that the mobile robot can successfully avoid static and dynamic obstacles.
Furthermore the mobile robot was able to reach its target within an indoor environment
without causing any collision or missing the target
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
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