Heuristics and Rescheduling in Prioritised Multi-Robot Path Planning: A Literature Review

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings

CRH*: A Deadlock Free Framework for Scalable Prioritised Path Planning in Multi-Robot Systems

Multi-robot system is an ever growing tool which is able to be applied to a wide range of industries to improve productivity and robustness, especially when tasks are distributed in space, time and functionality. Recent works have shown the benefits of multi-robot systems in fields such as warehouse automation, entertainment and agriculture. The work presented in this paper tackles the deadlock problem in multi-robot navigation, in which robots within a common work-space, are caught in situations where they are unable to navigate to their targets, being blocked by one another. This problem can be mitigated by efficient multi-robot path planning. Our work focused around the development of a scalable rescheduling algorithm named Conflict Resolution Heuristic A* (CRH*) for decoupled prioritised planning. Extensive experimental evaluation of CRH* was carried out in discrete event simulations of a fleet of autonomous agricultural robots. The results from these experiments proved that the algorithm was both scalable and deadlock-free. Additionally, novel customisation options were included to test further optimisations in system performance. Continuous Assignment and Dynamic Scoring showed to reduce the make-span of the routing whilst Combinatorial Heuristics showed to reduce the impact of outliers on priority orderings

Autonomous Topological Optimisation for Multi-robot Systems in Logistics

Multi-robot systems (MRS) are currently being introduced in many in-field logistics operations in large environments such as warehouses and commercial soft-fruit production. Collision avoidance is a critical problem in MRS as it may introduce deadlocks during the motion planning. In this work, a discretised topological map representation is used for low-cost route planning of individual robots as well as to easily switch the navigation actions depending on the constraints in the environment. However, this topological map could also have bottlenecks which leads to deadlocks and low transportation efficiency when used for an MRS. In this paper, we propose a resource container based Request-Release-Interrupt (RRI) algorithm that constrains each topological node with a capacity of one entity and therefore helps to avoid collisions and detect deadlocks. Furthermore, we integrate a Genetic Algorithm (GA) with Discrete Event Simulation (DES) for optimising the topological map to reduce deadlocks and improve transportation efficiency in logistics tasks. Performance analysis of the proposed algorithms are conducted after running a set of simulations with multiple robots and different maps. The results validate the effectiveness of our algorithms

Towards an Abstract Lightweight Multi-robot ROS Simulator for Rapid Experimentation

Modern robot simulators are commonly highly complex, offering 3D graphics, and simulation of physics, sensors, and actuators. The computational complexity of simulating large multi-robot systems in these simulators can be prohibitively high. To achieve faster-than-realtime simulation of a multi-robot system for rapid experimentation, we present `move_base_abstract', a ROS package providing a high-level abstraction of robot navigation as a ``drop-in'' replacement for the standard `move_base' navigation, and a bespoke integrated minimal simulator. This bespoke simulator is compatible with ROS and strips the simulation of robots down to the representation of robot poses in 2D space, control of robots via navigation goals, and control of simulation time over ROS topic messages. Replication of an existing MRS simulated study using `move_base_abstract' executed 2.87 times faster than the real-time that was simulated in the study, and analysis of the results of this replication shows room for further optimisations

Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques

The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within RGCs can be derived by quantitatively fitting the sets of physiological data using an input–output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input–output responses are modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear–nonlinear approaches

Maximising availability of transportation robots through intelligent allocation of parking spaces

Autonomous agricultural robots increasingly have an important role in tasks such as transportation, crop monitoring, weed detection etc. These tasks require the robots to travel to different locations in the field. Reducing time for this travel can greatly reduce the global task completion time and improve the availability of the robot to perform more number of tasks. Looking at in-field logistics robots for supporting human fruit pickers as a relevant scenario, this research deals with the design of various algorithms for automated allocation of parking spaces for the on-field robots, so as to make them most accessible to preferred areas of the field. These parking space allocation algorithms are tested for their performance by varying initial parameters like the size of the field, number of farm workers in the field, position of the farm workers etc. Various experiments are conducted for this purpose on a simulated environment. Their results are studied and discussed for better understanding about the contribution of intelligent parking space allocation towards improving the overall time efficiency of task completion

Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments

Global navigation satellite system (GNSS) has been considered as a panacea for positioning and tracking since the last decade. However, it suffers from severe limitations in terms of accuracy, particularly in highly cluttered and indoor environments. Though real-time kinematics (RTK) supported GNSS promises extremely accurate localisation, employing such services are expensive, fail in occluded environments and are unavailable in areas where cellular base stations are not accessible. It is, therefore, necessary that the GNSS data is to be filtered if high accuracy is required. Thus, this article presents a GNSS-based particle filter that exploits the spatial constraints imposed by the environment. In the proposed setup, the state prediction of the sample set follows a restricted motion according to the topological map of the environment. This results in the transition of the samples getting confined between specific discrete points, called the topological nodes, defined by a topological map. This is followed by a refinement stage where the full set of predicted samples goes through weighting and resampling, where the weight is proportional to the predicted particle’s proximity with the GNSS measurement. Thus, a discrete space continuous-time Bayesian filter is proposed, called the Topological Particle Filter (TPF). The proposed TPF is put to test by localising and tracking fruit pickers inside polytunnels. Fruit pickers inside polytunnels can only follow specific paths according to the topology of the tunnel. These paths are defined in the topological map of the polytunnels and are fed to TPF to tracks fruit pickers. Extensive datasets are collected to demonstrate the improved discrete tracking of strawberry pickers inside polytunnels thanks to the exploitation of the environmental constraints

Discrete Event Simulations for Scalability Analysis of Robotic In-Field Logistics in Agriculture – A Case Study

Agriculture lends itself to automation due to its labour-intensive processes and the strain posed on workers in the domain. This paper presents a discrete event simulation (DES) framework allowing to rapidly assess different processes and layouts for in-field logistics operations employing a fleet of autonomous transportation robots supporting soft-fruit pickers. The proposed framework can help to answer pressing questions regarding the economic viability and scalability of such fleet operations, which we illustrate and discuss in the context of a specific case study considering strawberry picking operations. In particular, this paper looks into the effect of a robotic fleet in scenarios with different transportation requirements, as well as on the effect of allocation algorithms, all without requiring resource demanding field trials. The presented framework demonstrates a great potential for future development and optimisation of the efficient robotic fleet operations in agriculture

An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies

Harvesting in soft-fruit farms is labor intensive, time consuming and is severely affected by scarcity of skilled labors. Among several activities during soft-fruit harvesting, human pickers take 20–30% of overall operation time into the logistics activities. Such an unproductive time, for example, can be reduced by optimally deploying a fleet of agricultural robots and schedule them by anticipating the human activity behaviour (state) during harvesting. In this paper, we propose a framework for spatio-temporal prediction of human pickers’ activities while they are picking fruits in agriculture fields. Here we exploit temporal patterns of picking operation and 2D discrete points, called topological nodes, as spatial constraints imposed by the agricultural environment. Both information are used in the prediction framework in combination with a variant of the Hidden Markov Model (HMM) algorithm to create two modules. The proposed methodology is validated with two test cases. In Test Case 1, the first module selects an optimal temporal model called as picking_state_progression model that uses temporal features of a picker state (event) to statistically evaluate an adequate number of intra-states also called sub-states. In Test Case 2, the second module uses the outcome from the optimal temporal model in the subsequent spatial model called node_transition model and performs “spatio-temporal predictions” of the picker’s movement while the picker is in a particular state. The Discrete Event Simulation (DES) framework, a proven agricultural multi-robot logistics model, is used to simulate the different picking operation scenarios with and without our proposed prediction framework and the results are then statistically compared to each other. Our prediction framework can reduce the so-called unproductive logistics time in a fully manual harvesting process by about 80 percent in the overall picking operation. This research also indicates that the different rates of picking operations involve different numbers of sub-states, and these sub-states are associated with different trends considered in spatio-temporal predictions