15 research outputs found
Experimental Validation of the Reliability-Aware Multi-UAV Coverage Path Planning Problem
Unmanned aerial vehicles (UAVs) have become crucial for various applications, necessitating reliable and time-constrained performance. Multi-UAV solutions offer advantages but require effective coordination. Traditional coverage path planning methods overlook uncertainties and individual UAV failures. To address this, reliability-aware multi-UAV coverage path planning methods optimise task allocation to maximise mission completion probabilities given a failure model. This paper presents an experimental validation of the reliability-aware approach, specifically an approach using a Greedy Genetic Algorithm (GGA). We evaluate the GGA performance in real-world environments, comparing mission reliability to computed reliability and comparing it against a traditional multi-UAV methods. The experimental validation demonstrates the practical viability and effectiveness of the reliability-aware approach, showing significant improvement in mission reliability despite the inevitable mismatch between real and assumed failure models
When Patrolmen Become Corrupted: Monitoring a Graph Using Faulty Mobile Robots
A team of k mobile robots is deployed on a weighted graph whose edge weights represent distances. The robots move perpetually along the domain, represented by all points belonging to the graph edges, without exceeding their maximum speed. The robots need to patrol the graph by regularly visiting all points of the domain. In this paper, we consider a team of robots (patrolmen), at most f of which may be unreliable, i.e., they fail to comply with their patrolling duties. What algorithm should be followed so as to minimize the maximum time between successive visits of every edge point by a reliable patrolman? The corresponding measure of efficiency of patrolling called idleness has been widely accepted in the robotics literature. We extend it to the case of untrusted patrolmen; we denote by Ifk(G) the maximum time that a point of the domain may remain unvisited by reliable patrolmen. The objective is to find patrolling strategies minimizing Ifk(G). We investigate this problem for various classes of graphs. We design optimal algorithms for line segments, which turn out to be surprisingly different from strategies for related patrolling problems proposed in the literature. We then use these results to study general graphs. For Eulerian graphs G, we give an optimal patrolling strategy with idleness Ifk(G)=(f+1)|E|/k, where |E| is the sum of the lengths of the edges of G. Further, we show the hardness of the problem of computing the idle time for three robots, at most one of which is faulty, by reduction from 3-edge-coloring of cubic graphsâa known NP-hard problem. A byproduct of our proof is the investigation of classes of graphs minimizing idle time (with respect to the total length of edges); an example of such a class is known in the literature under the name of Kotzig graphs
The Fagnano Triangle Patrolling Problem
We investigate a combinatorial optimization problem that involves patrolling
the edges of an acute triangle using a unit-speed agent. The goal is to
minimize the maximum (1-gap) idle time of any edge, which is defined as the
time gap between consecutive visits to that edge. This problem has roots in a
centuries-old optimization problem posed by Fagnano in 1775, who sought to
determine the inscribed triangle of an acute triangle with the minimum
perimeter. It is well-known that the orthic triangle, giving rise to a periodic
and cyclic trajectory obeying the laws of geometric optics, is the optimal
solution to Fagnano's problem. Such trajectories are known as Fagnano orbits,
or more generally as billiard trajectories. We demonstrate that the orthic
triangle is also an optimal solution to the patrolling problem.
Our main contributions pertain to new connections between billiard
trajectories and optimal patrolling schedules in combinatorial optimization. In
particular, as an artifact of our arguments, we introduce a novel 2-gap
patrolling problem that seeks to minimize the visitation time of objects every
three visits. We prove that there exist infinitely many well-structured
billiard-type optimal trajectories for this problem, including the orthic
trajectory, which has the special property of minimizing the visitation time
gap between any two consecutively visited edges. Complementary to that, we also
examine the cost of dynamic, sub-optimal trajectories to the 1-gap patrolling
optimization problem. These trajectories result from a greedy algorithm and can
be implemented by a computationally primitive mobile agent
Distributed approach for coverage and patrolling missions with a team of heterogeneous aerial robots under communication constraints
Using aerial robots in area coverage applications
is an emerging topic. These applications need a coverage
path planning algorithm and a coordinated patrolling
plan. This paper proposes a distributed approach to
coordinate a team of heterogeneous UAVs cooperating
efficiently in patrolling missions around irregular areas,
with low communication ranges and memory storage
requirements. Hence it can be used with smallâscale
UAVs with limited and different capabilities. The
presented system uses a modular architecture and solves
the problem by dividing the area between all the robots
according to their capabilities. Each aerial robot performs
a decomposition based algorithm to create covering paths
and a âoneâtoâoneâ coordination strategy to decide the
path segment to patrol. The system is decentralized and
faultâtolerant. It ensures a finite time to share
information between all the robots and guarantees
convergence to the desired steady state, based on the
maximal minimum frequency criteria. A set of
simulations with a team of quadârotors is used to
validate the approach
MULTI-ROBOT COVERAGE WITH DYNAMIC COVERAGE INFORMATION COMPRESSION
This work considers the problem of coverage of an initially unknown environment by a set of autonomous robots. A crucial aspect in multi-robot coverage involves robots sharing information about the regions they have already covered at certain intervals, so that multiple robots can avoid repeated coverage of the same area. However, sharing the coverage information between robots imposes considerable communication and computation overhead on each robot, which increases the robotsâ battery usage and overall coverage time. To address this problem, we explore a novel coverage technique where robots use an information compression algorithm before sharing their coverage maps with each other. Specifically, we use a polygonal approximation algorithm to represent any arbitrary region covered by a robot as a polygon with a fixed, small number of vertices. At certain intervals, each robot then sends this small set of vertices to other robots in its communication range as its covered area, and each receiving robot records this information in a local map of covered regions so that it can avoid repeat coverage. The coverage information in the map is then utilized by a technique called spanning tree coverage (STC) by each robot to perform area coverage. We have verified the performance of our algorithm on simulated Coroware Corobot robots within the Webots robot simulator with different sizes of environments and different types of obstacles in the environments, while modelling sensor noise from the robotsâ sensors. Our results show that using the polygonal compression technique is an effective way to considerably reduce data transfer between robots in a multi-robot team without sacrificing the performance and efficiency gains that communication provides to such a system
When Patrolmen Become Corrupted: Monitoring a Graph using Faulty Mobile Robots
International audienceA team of k mobile robots is deployed on a weighted graph whose edge weights represent distances. The robots perpetually move along the domain, represented by all points belonging to the graph edges, not exceeding their maximal speed. The robots need to patrol the graph by regularly visiting all points of the domain. In this paper, we consider a team of robots (patrolmen), at most f of which may be unreliable, i.e. they fail to comply with their patrolling duties. What algorithm should be followed so as to minimize the maximum time between successive visits of every edge point by a reliable patrolmen? The corresponding measure of efficiency of patrolling called idleness has been widely accepted in the robotics literature. We extend it to the case of untrusted patrolmen; we denote by Ifk (G) the maximum time that a point of the domain may remain unvisited by reliable patrolmen. The objective is to find patrolling strategies minimizing Ifk (G). We investigate this problem for various classes of graphs. We design optimal algorithms for line segments, which turn out to be surprisingly different from strategies for related patrolling problems proposed in the literature. We then use these results to study the case of general graphs. For Eulerian graphs G, we give an optimal patrolling strategy with idleness Ifk (G) = (f + 1)|E|/k, where |E| is the sum of the lengths of the edges of G. Further, we show the hardness of the problem of computing the idle time for three robots, at most one of which is faulty, by reduction from 3-edge-coloring of cubic graphs â a known NP-hard problem. A byproduct of our proof is the investigation of classes of graphs minimizing idle time (with respect to the total length of edges); an example of such a class is known in the literature under the name of Kotzig graphs
Hopscotch: Robust Multi-agent Search
The task of searching a space is critical to a wide range of diverse applications such as land mine clearing and planetary exploration. Because applications frequently require searching remote or hazardous locations, and because the task is easily divisible, it is natural to consider the use of multi-robot teams to accomplish the search task. An important topic of research in this area is the division of the task among robot agents. Interrelated with subtask assignment is failure handling, in the sense that, when an agent fails, its part of the task must then be performed by other agents. This thesis describes Hopscotch, a multi-agent search strategy that divides the search area into a grid of lots. Each agent is assigned responsibility to search one lot at a time, and upon completing the search of that lot the agent is assigned a new lot. Assignment occurs in real time using a simple contract net. Because lots that have been previously searched are skipped, the order of search from the point of view of a particular agent is reminiscent of the progression of steps in the playground game of Hopscotch. Decomposition of the search area is a common approach to multi-agent search, and auction-based contract net strategies have appeared in recent literature as a method of task allocation in multi-agent systems. The Hopscotch strategy combines the two, with a strong focus on robust tolerance of agent failures. Contract nets typically divide all known tasks among available resources. In contrast, Hopscotch limits each agent to one assigned lot at a time, so that failure of an agent compels re-allocation of only one lot search task. Furthermore, the contract net is implemented in an unconventional manner that empowers each agent with responsibility for contract management. This novel combination of real-time assignment and decentralized management allows Hopscotch to resiliently cope with agent failures. The Hopscotch strategy was modeled and compared to other multi-agent strate- gies that tackle the search task in a variety of ways. Simulation results show that Hopscotch is failure-tolerant and very effective in comparison to the other approaches in terms of both search time and search efficiency. Although the search task modeled here is a basic one, results from simulations show the promise of using this strategy for more complicated scenarios, and with actual robot agents
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Multi-Robot Complete Coverage Using Directional Constraints
Complete coverage relies on a path planning algorithm that will move one or more robots, including the actuator, sensor, or body of the robot, over the entire environment. Complete coverage of an unknown environment is used in applications like automated vacuum cleaning, carpet cleaning, lawn mowing, chemical or radioactive spill detection and cleanup, and humanitarian de-mining.
The environment is typically decomposed into smaller areas and then assigned to individual robots to cover. The robots typically use the Boustrophedon motion to cover the cells. The location and size of obstacles in the environment are unknown beforehand. An online algorithm using sensor-based coverage with unlimited communication is typically used to plan the path for the robots.
For certain applications, like robotic lawn mowing, a pattern might be desirable over a random irregular pattern for the coverage operation. Assigning directional constraints to the cells can help achieve the desired pattern if the path planning part of the algorithm takes the directional constraints into account.
The goal of this dissertation is to adapt the distributed coverage algorithm with unrestricted communication developed by Rekleitis et al. (2008) so that it can be used to solve the complete coverage problem with directional constraints in unknown environments while minimizing repeat coverage. It is a sensor-based approach that constructs a cellular decomposition while covering the unknown environment.
The new algorithm takes directional constraints into account during the path planning phase. An implementation of the algorithm was evaluated in simulation software and the results from these experiments were compared against experiments conducted by Rekleitis et al. (2008) and with an implementation of their distributed coverage algorithm.
The results of this study confirm that directional constraints can be added to the complete coverage algorithm using multiple robots without any significant impact on performance. The high-level goals of complete coverage were still achieved. The work was evenly distributed between the robots to reduce the time required to cover the cells