12 research outputs found
Homotopic Path Planning on Manifolds for Cabled Mobile Robots
We present two path planning algorithms for mobile robots that are connected
by cable to a fixed base. Our algorithms efficiently compute the shortest path
and control strategy that lead the robot to the target location considering cable length
and obstacle interactions. First, we focus on cable-obstacle collisions. We introduce
and formally analyze algorithms that build and search an overlapped configuration
space manifold. Next, we present an extension that considers cable-robot collisions.
All algorithms are experimentally validated using a real robot
Entanglement Definitions for Tethered Robots: Exploration and Analysis
In this article we consider the problem of tether entanglement for tethered
robots. In many applications, such as maintenance of underwater structures,
aerial inspection, and underground exploration, tethered robots are often used
in place of standalone (i.e., untethered) ones. However, the presence of a
tether also introduces the risk for it to get entangled with obstacles present
in the environment or with itself. To avoid these situations, a
non-entanglement constraint can be considered in the motion planning problem
for tethered robots. This constraint can be expressed either as a set of
specific tether configurations that must be avoided, or as a quantitative
measure of a `level of entanglement' that can be minimized. However, the
literature lacks a generally accepted definition of entanglement, with existing
definitions being limited and partial. Namely, the existing entanglement
definitions either require a taut tether to come into contact with an obstacle
or with another tether, or they require for the tether to do a full loop around
an obstacle. In practice, this means that the existing definitions do not
effectively cover all instances of tether entanglement. Our goal in this
article is to bridge this gap and provide new definitions of entanglement,
which, together with the existing ones, can be effectively used to qualify the
entanglement state of a tethered robot in diverse situations. The new
definitions find application mainly in motion planning for tethered robot
systems, where they can be used to obtain more safe and robust
entanglement-free trajectories. The present article focuses exclusively on the
presentation and analysis of the entanglement definitions. The application of
the definitions to the motion planning problem is left for future work.Comment: 30 pages, 19 figure
NEPTUNE: Non-Entangling Planning for Multiple Tethered Unmanned Vehicles
Despite recent progress on trajectory planning of multiple robots and path
planning of a single tethered robot, planning of multiple tethered robots to
reach their individual targets without entanglements remains a challenging
problem. In this paper, we present a complete approach to address this problem.
Firstly, we propose a multi-robot tether-aware representation of homotopy,
using which we can efficiently evaluate the feasibility and safety of a
potential path in terms of (1) the cable length required to reach a target
following the path, and (2) the risk of entanglements with the cables of other
robots. Then, the proposed representation is applied in a decentralized and
online planning framework that includes a graph-based kinodynamic trajectory
finder and an optimization-based trajectory refinement, to generate
entanglement-free, collision-free and dynamically feasible trajectories. The
efficiency of the proposed homotopy representation is compared against existing
single and multiple tethered robot planning approaches. Simulations with up to
8 UAVs show the effectiveness of the approach in entanglement prevention and
its real-time capabilities. Flight experiments using 3 tethered UAVs verify the
practicality of the presented approach.Comment: Accepted for publication in IEEE Transaction on Robotic
Tangle-Free Exploration with a Tethered Mobile Robot
Exploration and remote sensing with mobile robots is a well known field of research, but current solutions cannot be directly applied for tethered robots. In some applications, tethers may be very important to provide power or allow communication with the robot. This paper presents an exploration algorithm that guarantees complete exploration of arbitrary environments within the length constraint of the tether, while keeping the tether tangle-free at all times. While we also propose a generalized algorithm that can be used with several exploration strategies, our implementation uses a modified frontier-based exploration approach, where the robot chooses its next goal in the frontier between explored and unexplored regions of the environment. The basic idea of the algorithm is to keep an estimate of the tether configuration, including length and homotopy, and decide the next robot path based on the difference between the current tether length and the shortest tether length at the next goal position. Our algorithm is provable correct and was tested and evaluated using both simulations and real-world experiments
Path planning for a tethered robot using Multi-Heuristic A* with topology-based heuristics
Abstract — In this paper, we solve the path planning problem for a tethered mobile robot, which is connected to a fixed base by a cable of length L. The reachable space of the robot is restricted by the length of the cable and obstacles. The reachable space of the tethered robot can be computed by considering the topology class of the cable. However, it is computationally too expensive to compute this space a-priori. Instead, in this paper, we show how we can plan using a recently-developed variant of A * search, called Multi-Heuristic A*. Normally, the Multi-Heuristic A * algorithm takes in a fixed set of heuristic functions. In our problem, however, the heuristics represent length of paths to the goal along different topology classes, and there can be too many of them and not all the topology classes are useful. To deal with this, we adapt Multi-Heuristic A * to work with a dynamically generated set of heuristic functions. It starts out as a normal weighted A*. Whenever the search gets trapped in a local minimum, we find the proper topology class of the path to escape from it and add the corresponding new heuristic function into the set of heuristic functions considered by the search. We present experimental analysis comparing our approach with weighted A * on planning for a tethered robot in simulation. I
Exploration of Unknown Environments Using a Tethered Mobile Robot
Exploration with mobile robots is a well known field of research, but current solutions cannot be directly applied for tethered robots. In some applications, tethers may be very important to provide power or allow communication with the robot. This thesis presents an exploration algorithm that guarantees complete exploration of arbitrary environments within the length constraint of the tether, while keeping the tether tangle-free at all times. While a generalized algorithm that can be used with several exploration strategies is also proposed, the presented implementation uses a modified frontier-based exploration approach, where the robot chooses its next goal in the frontier between explored and unexplored regions of the environment. The main modification from standard frontier-based method is the use of a cost function to sort multiple goals in one iteration and pick the cheapest one to go to, minimizing global path length in the process. The cost is calculated in terms of path length with tether constraints accounted for. The basic idea of the algorithm is to keep an estimate of the tether configuration, including length and homotopy, and decide the next robot path based on the length difference between the current tether length and the shortest tether length at the next goal position. The length difference is then used to determine whether it is safe for the robot to take the shortest path to the goal, or whether the robot has to take a different path to the goal in the way that would put the tether back into the most optimal configuration. The maximum length difference that would still guarantee global tangle-free paths has been shown to be the circumference of the smallest expected obstacle in the environment. The presented algorithm is provable correct and was tested and evaluated using both simulations and real-world experiments. Navigation of a planar robot is done with the aid of a Simultaneous Localization and Mapping (SLAM) system, with the data being provided by the on-board LiDAR scanner. The results from conducted experiments have demonstrated that the proposed algorithm results in the total path length increase of anywhere from 30% up to to 80% compared to untethered frontier-based approach, with the exact percentage increase dependent on the complexity of the environment. It is also approximately 6 times shorter than the total path length in a conservative approach of backtracking to the base to avoid tangling
Shortest path planning for single manipulator in 2D environment of deformable objects
A heuristic algorithm to perform path planning for single manipulator in 2D environment containing deformable objects is presented. The environment is partitioned into a quadtree hierarchy for both sampling and space navigation use before combination of artificial potential field and heuristic reasoning are applied iteratively to generate feasible path for the manipulator. The algorithm specifically targets for the shortest path without damaging any objects due to deep collision depth between manipulator link and object. Resulting path is in turn to be used in generating micro-instruction controlling the manipulator. Implementation results show feasibility to solve problems involving simple object and manipulator configuration
Online Coverage by a Tethered Autonomous Mobile Robot in Planar Unknown Environments
Abstract—This paper is concerned with online tethered coverage, in which a mobile robot of size D is attached to a fixed point S by a cable of finite length L. Starting at S, the robot has to cover an unknown planar environment containing obstacles, and return to S with the cable fully retracted. The paper first establishes an optimal off-line tethered coverage methodology, then introduces the TC (Tethered Coverage) algorithm that performs online tethered coverage using position and local obstacle detection sensors. The performance of the TC algorithm is measured by its competitiveness, determined by measuring its total online path length, l, relative to the optimal off-line solution lopt. The paper establishes that the TC algorithm has a competitive performance of l ≤ 2 L lopt. Execution example and experiments with a D tethered recoiling mechanism illustrate the usefulness of the TC algorithm. I
Motion Planning for a Tethered Mobile Robot
Recently there has been surge of research in motion planning for tethered robots. In this problem a planar robot is connected via a cable of limited length to a fixed point in R2. The configuration space in this problem is more complicated than the one of a classic motion planning problem as existence of the cable causes additional constraints on the motion of the robot. In this thesis we are interested in finding a concise representation of the configuration space that results in a straightforward planning algorithm. To achieve such a representation we observe that configuration space manifold has a discrete structure that conveniently can be separated from its continuous aspect when it is represented as an atlas of charts. We provide a method for generating either the complete atlas or a subset of its charts based on special cable events. Generating parts of the configuration space on-the-fly enables the following improvements over the state-of-the-art. a) We decompose the environment into cells as needed rather than an off-line global discretization, obtaining competitive time and space complexity for our planner. b) We are able to exploit topological structure to represent robot-cable configurations concisely leading us towards solutions to the more complex problems of interest.
To underscore the potential of this representation, we take further steps to generalize it to two more complicated instances of the tethered robot planning problem that has been widely disregarded in the literature. We will first consider a simplified model of cable-to-cable contacts, giving the robot the option to perform knot-like tying motions. Next, we will address the planning problem for a tethered robot whose cable has a constraint on its curvature. This adds to the realism of the model since most practical cables have some degree of stiffness which limits curvature. In this case we provide a novel technique to relate Dubins' theory of curves with work on planning with topological constraints. Our results show the efficiency of the method and indicate further promise for procedures that represent manifolds via an amalgamation of implicit discrete topological structure and explicit Euclidean cells