Enabling methodologies for optimal coverage by multiple autonomous industrial robots

University of Technology Sydney. Faculty of Engineering and Information Technology.Unlike traditional industrial robots which are purpose-built for a particular repetitive application, Autonomous Industrial Robots (AIRs) are adaptable to new operating conditions or environments. An AIR is an industrial robot, with or without a mobile platform, that has the intelligence needed to operate autonomously in a complex and unstructured environment. This intelligence includes aspects such as self-awareness, environmental awareness, and collision avoidance. In this thesis, research is focused on developing methodologies that enable multiple AIRs to perform complete coverage tasks on objects that can have complex geometric shapes while aiming to achieve optimal team objectives. For the AIRs to achieve optimal complete coverage for tasks such as grit-blasting and spray painting several problems need to be addressed. One problem is to partition and allocate the surface areas that multiple AIRs can reach. Another problem is to find a set of appropriate base placements for each AIR and to determine the visiting sequence of the base placements such that complete coverage is obtained. Uncertainties in base placements, due to sensing and localization errors, need to be accounted for if necessary. Coverage path planning, i.e. generating the AIRs’ end-effector path, is another problem that needs to be addressed. Coverage path planning needs to be adaptable with respect to dynamic obstacles and unexpected changes. In solving these problems, it is vital for the AIRs to optimize the team's objectives while accounting for relevant constraints. This research develops new methodologies to address the above problems, including (1) a Voronoi partitioning based approach for simultaneous area partitioning and allocation utilizing Voronoi partitioning and multi-objective optimization; (2) optimization-based methods for multi-AIR base placements with uncertainties; and (3) a prey-predator behaviour-based algorithm for adaptive and efficient real-time coverage path planning, which accounts for stationary or dynamic obstacles and unexpected changes in the coverage area. Real-world and simulated experiments have been carried out to verify the proposed methodologies. Various comparative studies are presented against existing methods. The results show that the proposed methodologies enable effective and efficient complete coverage by the AIRs

Flip Task Allocation for Robot Path Coverage

The usage of multi-robot systems to complete monotonous yet complex tasks has become increasingly popular. One such category is tasks that require the complete coverage of an area, such as the task of vacuuming. The undertaking of a complete coverage task by a singular mobile floor cleaning robot requires a minimum of path planning capabilities to prevent the recleaning of previously cleaned areas. When more than one robot is utilized to complete the same coverage task, there must be some form of global strategy implemented that can aid the multi-robot system in reducing the amount of coverage overlap, idle time, and overall time required to complete the vacuuming task. Such global strategies often utilize a method of decomposing the larger task into smaller subtasks which are then allocated among the number of robots within the system. However, many of these strategies are either static in their task allocation or are based on a singular robot system to accomplish the complete coverage task. The algorithm for global strategy proposed in this thesis presents a methodology for utilizing the techniques of triangular mesh decomposition, Traveling Salesman Problem optimization, and dynamic flip task allocation for multiple floor cleaning robots

Efficient Multi-Robot Coverage of a Known Environment

This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Discrete Path Planing Strategies for Coverage and Multi-Robot Rendezvous

This thesis addresses the problem of motion planning for autonomous robots, given a map and an estimate of the robot pose within it. The motion planning problem for a mobile robot can be defined as computing a trajectory in an environment from one pose to another while avoiding obstacles and optimizing some objective such as path length or travel time, subject to constraints like vehicle dynamics limitations. More complex planning problems such as multi-robot planning or complete coverage of an area can also be defined within a similar optimization structure. The computational complexity of path planning presents a considerable challenge for real-time execution with limited resources and various methods of simplifying the problem formulation by discretizing the solution space are grouped under the class of discrete planning methods. The approach suggests representing the environment as a roadmap graph and formulating shortest path problems to compute optimal robot trajectories on it. This thesis presents two main contributions under the framework of discrete planning. The first contribution addresses complete coverage of an unknown environment by a single omnidirectional ground rover. The 2D occupancy grid map of the environment is first converted into a polygonal representation and decomposed into a set of convex sectors. Second, a coverage path is computed through the sectors using a hierarchical inter-sector and intra-sector optimization structure. It should be noted that both convex decomposition and optimal sector ordering are known NP-hard problems, which are solved using a greedy cut approximation algorithm and Travelling Salesman Problem (TSP) heuristics, respectively. The second contribution presents multi-robot path-planning strategies for recharging autonomous robots performing a persistent task. The work considers the case of surveillance missions performed by a team of Unmanned Aerial Vehicles (UAVs). The goal is to plan minimum cost paths for a separate team of dedicated charging robots such that they rendezvous with and recharge all the UAVs as needed. To this end, planar UAV trajectories are discretized into sets of charging locations and a partitioned directed acyclic graph subject to timing constraints is defined over them. Solutions consist of paths through the graph for each of the charging robots. The rendezvous planning problem for a single recharge cycle is formulated as a Mixed Integer Linear Program (MILP), and an algorithmic approach, using a transformation to the TSP, is presented as a scalable heuristic alternative to the MILP. The solution is then extended to longer planning horizons using both a receding horizon and an optimal fixed horizon strategy. Simulation results are presented for both contributions, which demonstrate solution quality and performance of the presented algorithms

Discrete Path Planing Strategies for Coverage and Multi-Robot Rendezvous

This thesis addresses the problem of motion planning for autonomous robots, given a map and an estimate of the robot pose within it. The motion planning problem for a mobile robot can be defined as computing a trajectory in an environment from one pose to another while avoiding obstacles and optimizing some objective such as path length or travel time, subject to constraints like vehicle dynamics limitations. More complex planning problems such as multi-robot planning or complete coverage of an area can also be defined within a similar optimization structure. The computational complexity of path planning presents a considerable challenge for real-time execution with limited resources and various methods of simplifying the problem formulation by discretizing the solution space are grouped under the class of discrete planning methods. The approach suggests representing the environment as a roadmap graph and formulating shortest path problems to compute optimal robot trajectories on it. This thesis presents two main contributions under the framework of discrete planning. The first contribution addresses complete coverage of an unknown environment by a single omnidirectional ground rover. The 2D occupancy grid map of the environment is first converted into a polygonal representation and decomposed into a set of convex sectors. Second, a coverage path is computed through the sectors using a hierarchical inter-sector and intra-sector optimization structure. It should be noted that both convex decomposition and optimal sector ordering are known NP-hard problems, which are solved using a greedy cut approximation algorithm and Travelling Salesman Problem (TSP) heuristics, respectively. The second contribution presents multi-robot path-planning strategies for recharging autonomous robots performing a persistent task. The work considers the case of surveillance missions performed by a team of Unmanned Aerial Vehicles (UAVs). The goal is to plan minimum cost paths for a separate team of dedicated charging robots such that they rendezvous with and recharge all the UAVs as needed. To this end, planar UAV trajectories are discretized into sets of charging locations and a partitioned directed acyclic graph subject to timing constraints is defined over them. Solutions consist of paths through the graph for each of the charging robots. The rendezvous planning problem for a single recharge cycle is formulated as a Mixed Integer Linear Program (MILP), and an algorithmic approach, using a transformation to the TSP, is presented as a scalable heuristic alternative to the MILP. The solution is then extended to longer planning horizons using both a receding horizon and an optimal fixed horizon strategy. Simulation results are presented for both contributions, which demonstrate solution quality and performance of the presented algorithms