Multi-robot path planning for budgeted active perception with self-organising maps

© 2016 IEEE. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has polynomial-bounded runtime independent of the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Simulations were performed using a 3D point cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for budgeted active perception tasks with continuous sets of candidate viewpoints and long planning horizons

Online planning for multi-robot active perception with self-organising maps

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons

Agent-based approach to illegal maritime behavior modeling

Maritime shipping is a set of complex activities with a large number of actors involved. We focus on a subset of illegal maritime activities, such as armed robberies, maritime piracy or contraband smuggling. To fight against them and minimize their negative impact naval authorities typically introduce a number of countermeasures, such as deployed patrols or surveillance agents. Due to very high costs of countermeasures it is often beneficial to evaluate their impact using a simulation, allowing what-if analysis and evaluation of a range of scenarios before actually deploying the countermeasures. We introduce BANDIT, an agent-based computational platform, which is designed to evaluate scenarios with an accent on the modeling of different types of illegal behavior and on the interaction between agents. The platform consists of an agent behavior modeling system and a multi-agent maritime simulator. The platform allows the definition of a number of scenarios through a simple configuration and it offers the means to run these scenarios in a single or a batch mode and evaluate the results as single or aggregate data sets respectively. We demonstrate the usefulness of the platform on the scenarios of the drug smuggling problem in the seas surrounding Central America. Senario outcomes (e.g., heatmaps of activities, set of trajectories etc.) are subsequently used to help with the design of effective countermeasures, i.e., allocating naval patrols and planning their patrol routes

Bi-objective maritime route planning in pirate-infested waters

Contemporary maritime shipping is subject to a large number of constraints given by tight shipping schedules and very low margins. Additionally, problematic areas with increased security needs dynamically changing in time, combined with seasonal oceanographic and meteorological conditions pose a challenging voyage planning problem. In this work we present a risk-aware voyage planner taking into account spatio-temporal environmental conditions. The planner is based on a graph-based search algorithm A* . We discretize the required area into a graph, we store various layers of information into the edges of the graph (such as risk and weather conditions) in a form of numeric weights and we define a bi-objective planning problem with a tradeoff between security and duration of the voyage. The nature of the algorithm guarantees a complete and optimal solution in a form of an optimized voyage with respect to the criterion function composed of the two weighted components, i.e, duration and security of the voyage. We demonstrate the approach on our area of interest: Indian Ocean. We use NATO piracy activity risk surface as the risk layer and we compute all transit voyages between relevant routing points in the area. Finally, thanks to the discretization of the problem, we are able to integrate corridors imposed by the shipping authorities and evaluate additional what-if scenarios with extended corridor systems. The resulting planner is exposed to the public using a web service with an easy interface requiring start time of the voyage and the origin and the destination point of voyage. Combined with an expressive visualization, this tool demonstrates the capabilities of the proposed solution

Maize MADS-Box Genes Galore

MADS-box genes encode a family of transcription factors which control diverse developmental processes in flowering plants ranging from root to flower and fruit development. A large screening for MIKC-type MADS-box gene cDNAs in maize yielded sequences for 31 different genes, 29 of which are of the MIKC-type. 15 of these MIKC-type genes were novel. Together with three published genes for which a cDNA did not appear in our screen 32 different MIKC-type genes have thus been identified in maize now. All of these genes are members of subfamilies known from eudicots. However, it appears that in many subfamilies there are more gene members in maize than in eudicot model plants such as Arabidopsis. Phylogeny reconstructions involving all published MADS-box genes identified two major reasons for this. First, after the establishment of the defined gene subfamilies in a common ancestor of eudicots and monocots, a number of gene duplications occurred in the lineage that led to extant monocots after the eudicots had branched off, but before the separation of the lineages that led to extant maize and rice. Based on our gene collection we could estimate that there must have been at least 20 different MIKC-type genes in the most recent common ancestor of maize and rice about 50-70 million years ago. In contrast, the same data set supports only the presence of at least 11 different genes in the last common ancestor of monocots and eudicots about 200 million years ago. Second, phylogenetic trees in line with chromosomal mapping data revealed that the event that gave rise to the ancient segmental allotetraploidy of the maize genome established typically two young paralogs for many orthologous rice MADS-box genes. By chromosomal mapping also candidate genes for some interesting maize developmental gene loci could be identified. The genes reported here are a rich ressource for further studies on the evolutionary dynamics of a complex gene family, the developmental genetics of maize, and a rational crop design employing developmental control genes as tools