Efficient Mission Planning for Robot Networks in Communication Constrained Environments

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes

The effect of nest topology on spatial organization and recruitment in the red ant Myrmica rubra

Nests of social insects are an important area for the exchange of food and information among workers. We investigated how the topology of nest chambers (as opposed to nest size or environmental factors) affects the spatial distribution of nestmates and the foraging behavior of Myrmica rubra ant colonies. Colonies were housed in artificial nests, each with same-sized chambers differing in the spatial arrangement of galleries. A highly connected central chamber favored higher occupancy rates and a more homogeneous distribution of ants across chambers. In contrast, a chain of successive chambers led to a more heterogeneous distribution of ants, with the occupancy of a chamber chiefly mediated by its distance to the entrance. Irrespective of nest topology, the entrance chamber housed the largest proportion of ants, often including the queen, which exhibited a preference for staying in densely populated chambers. Finally, we investigated how nest topology influenced nestmate recruitment. Surprisingly, a highly connected chamber in the center of the nest did not promote greater recruitment nor activation of ants. At the onset of foraging, the largest number of moving ants was reached in the topology where the most connected chamber was the nest entrance. Later in the process, we found that a chain of successive chambers was the best topology for promoting ant’s mobilization. Our work demonstrates that nest topology can shape the spatial organization and the collective response of ant colonies, thereby taking part in their adaptative strategies to exploit environmental resources.info:eu-repo/semantics/publishe