Research task (Technical Documentation):
I'm trying to scope out my research proposal. It's focused on decentralized control for a heterogeneous swarm (UAVs and UGVs) doing search and rescue in unstructured, GPS-denied environments. Think collapsed buildings, dense forests, etc

First, I need a way for each agent to handle its own state estimation. I'm thinking multi-sensor fusion, that is, fusing onboard vision, LiDAR, and IMU data for robust SLAM and odometry, but it also has to be good enough to detect survivors or hazards, not just navigate. How do we manage real-time role assignment, eg who's mapping, who's actively searching, who's clearing debris? It needs to be decentralized and robust to failures. for example if a drone battery dies or a ground bot gets stuck, the system has to autonomously reallocate its tasks without a central coordinator.  The comms will be spotty, so how does the swarm propagate critical info like victim locations, new hazards, blocked passages, etc over an ad-hoc mesh network that's constantly dropping connections? Maybe some distributed mapping or data sharing protocol? For the analysis, I need to show this framework is actually effective. We probably need some formal or at least strong empirical guarantees? Something to show that, say 90% of reachable victims are located within a bounded time, even if we lose up to 30% of the agents. Finally, for evaluation, I'm planning to implement this in a high-fidelity simulator, probably ROS/Gazebo, with realistic sensor noise and complex terrain. Then I'd run it across maybe three disaster scenarios of increasing complexity. We'd need to report metrics on coverage, victim-finding rate, rescue latency, and the network overhead to make sure it's efficient.

Evaluation rubric:
criterion: The response defines the primary safety property as a probabilistic temporal logic formula (e.g., Probabilistic Computation Tree Logic (PCTL), Continuous Stochastic Logic (CSL), Probabilistic Linear Temporal Logic (PLTL), Probabilistic Timed Computation Tree Logic (PTCTL)) that specifies at least a 0.95 probability of locating at least 90% of victims within a bounded time T, given a robot failure rate of up to 30%.
weight: 5
axis: Explicit Criteria

criterion: The response justifies its decentralized architecture by explicitly naming at least two failure modes of centralized systems in disaster environments (e.g., single point of failure, communication bottlenecks, delayed decision-making, limited scalability) and explaining how its proposed mechanisms mitigate these specific risks (e.g., distributed control, ad hoc networking, consensus-based coordination, fault-tolerant task allocation).
weight: 4
axis: Implicit Criteria

criterion: The response describes a novel decentralized task allocation mechanism that explicitly explains how it reallocates roles when failures occur (e.g., silent failures of aerial drones, ground robot immobilization, sensor malfunction, loss of communication) and justifies why this mechanism is appropriate for resilient operation in disaster environments by, for example, adapting a known algorithm (e.g., Parker’s ALLIANCE, Consensus-Based Bundle Algorithm (CBBA), Contract Net Protocol (CNP), Market-Based Task Allocation (MBTA)).
weight: 4
axis: Explicit Criteria

criterion: The response mentions both Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs).
weight: 3
axis: Implicit Criteria

criterion: The response assigns at least one primary task to UAVs that is unique to them (i.e., a task UGVs cannot perform, like aerial mapping or comms relay).
weight: 3
axis: Implicit Criteria

criterion: The response assigns at least one primary task to UGVs that is unique to them (i.e., a task UAVs cannot perform, like sub-debris inspection or clearing passages).
weight: 3
axis: Implicit Criteria

criterion: The response explains why a mixed swarm (containing both UAVs and UGVs) is fundamentally more effective for a disaster response mission than a homogeneous swarm (e.g., only UAVs or only UGVs).
weight: 3
axis: Implicit Criteria

criterion: The response defines its task allocation mechanism as a multi-objective optimization problem that includes at least three competing objectives (e.g., risk minimization by avoiding robot loss, mission completeness by maximizing area coverage, energy efficiency by minimizing battery usage, communication robustness by reducing packet loss) and explains why balancing these objectives is essential for adaptive and resilient task allocation in disaster environments.
weight: 3
axis: Implicit Criteria

criterion: The response names at least one filter-based or graph-based Simultaneous Localization and Mapping (SLAM) algorithm (e.g., Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Factor Graph Optimization, ORB-SLAM3) and explains that filter-based methods enable efficient real-time state estimation on resource-limited robots, while graph-based methods provide higher accuracy and robustness in large, noisy disaster environments.
weight: 3
axis: Implicit Criteria

criterion: The response describes a task allocation mechanism that specifies how a robot’s suitability for a task is determined (e.g., by considering current energy level, available sensors, proximity to the task, communication quality) and explains how this mechanism supports effective role assignment and reallocation in heterogeneous swarms operating in disaster environments.
weight: 4
axis: Explicit Criteria

criterion: The response defines at least one endogenous fault detection mechanism (e.g., internal health monitoring of motors, onboard battery level sensing, thermal overload detection, internal sensor self-checks) and at least one exogenous fault detection mechanism (e.g., heartbeat timeout protocols, neighbor robots detecting lack of motion, failure to acknowledge communication messages, abnormal deviation from planned trajectory), and explains how these detected faults trigger a specific task reallocation strategy (e.g., redistributing mapping tasks to nearby UAVs, assigning debris removal to alternate UGVs, shifting communication relay duties to another aerial robot, reallocating victim detection to a functioning neighbor).
weight: 4
axis: Implicit Criteria

criterion: The response specifies a named routing protocol for its ad hoc mesh network (e.g., Ad hoc On-Demand Distance Vector (AODV), Optimized Link State Routing (OLSR), Dynamic Source Routing (DSR), Better Approach To Mobile Ad hoc Networking (BATMAN)) or describes a specific networking approach (e.g., delay-tolerant networking with store-and-forward, epidemic routing, opportunistic forwarding, hierarchical clustering) and explains why the choice is appropriate for maintaining communication under disaster conditions with intermittent connectivity.
weight: 4
axis: Implicit Criteria

criterion: The response outlines a formal verification strategy that names a specific verification technique (e.g., model checking, reachability analysis, theorem proving, temporal logic verification) and identifies a representative software tool used for such analysis (e.g., PRISM, UPPAAL, Spin, NuSMV), and explains why such methods are needed to ensure safety and robustness when robots fail in disaster environments.
weight: 4
axis: Implicit Criteria

criterion: The response provides an empirical scalability analysis by reporting how at least one performance metric (e.g., network overhead, mission completion time, victim detection rate, communication latency) changes as the number of robots increases to at least 50, and explains why this analysis is important for assessing swarm effectiveness in large-scale disaster environments.
weight: 4
axis: Implicit Criteria

criterion: The response provides an empirical scalability analysis by reporting how at least one performance metric (e.g., network overhead, mission completion time, victim detection rate, communication latency) changes as the number of robots increases to at least 50, and explains why this analysis is important for assessing swarm effectiveness in large-scale disaster environments.
weight: 4
axis: Explicit Criteria

criterion: The response explicitly lists and defines at least four quantitative performance metrics, including coverage (e.g., area coverage rate, percentage of mapped terrain, sensor field-of-view utilization, overlap/redundancy ratio), mission success (e.g., victim-finding rate, percentage of survivors located, mission completion ratio, false negative rate), time (e.g., rescue latency, average task completion time, mission duration, mean time to victim detection), and communication efficiency (e.g., network overhead, packet delivery ratio, end-to-end latency, bandwidth utilization).
weight: 4
axis: Explicit Criteria

criterion: The response specifies a method for differentiating potential survivors from debris or other environmental noise, referencing at least one confirmation technique (e.g., analysis of thermal signatures from an infrared camera, detection of human-like motion patterns, acoustic sensing for signs of life such as tapping or calling, micro-Doppler radar for detecting breathing or heartbeat) and explains why such methods are necessary for reliable survivor detection in disaster environments.
weight: 4
axis: Explicit Criteria

criterion: The response specifies a data prioritization scheme within its communication protocol that ensures critical messages have precedence over routine data when the ad hoc network is bandwidth-constrained (e.g., survivor detected, imminent hazard, structural collapse warning, emergency task reallocation) compared to lower-priority information (e.g., routine telemetry, area mapping updates, battery status reports, periodic localization beacons).
weight: 3
axis: Implicit Criteria

criterion: The response specifies that the high-fidelity simulator (e.g., Robot Operating System (ROS), Gazebo, Webots, Microsoft AirSim) models at least two types of realistic sensor degradation to test framework robustness (e.g., Gaussian noise on LiDAR returns, motion blur on visual feeds in fast-moving UAVs, signal attenuation through rubble, dropped packets in wireless communication).
weight: 3
axis: Implicit Criteria

criterion: The response explicitly defines a fault-tolerance strategy for when a robot fails mid-task, detailing how the incomplete task is released, its state is communicated, and it is reassigned to another suitable robot in the swarm (e.g., a ground unit gets stuck in rubble, an aerial drone loses power, a robot’s sensor package malfunctions, a unit loses network connectivity).
weight: 4
axis: Explicit Criteria

criterion: The response provides a mathematical or empirical justification for achieving the mission within a bounded time, defining the key variables that influence this bound (e.g., search area dimensions, average robot velocity, effective sensor range, number of active robots).
weight: 4
axis: Explicit Criteria

criterion: The response includes an analysis of scalability that discusses how the computational or communication load of the decentralized coordination algorithm grows with swarm size (e.g., processing time per decision step, memory requirements, inter-robot message complexity, bandwidth utilization) and explains why this impacts performance in large-scale disaster simulations.
weight: 2
axis: Implicit Criteria

criterion: The response incorporates robot energy levels as a constraint in its task allocation mechanism, ensuring that robots autonomously prioritize returning to a designated charging or recovery station over accepting new non-critical tasks when energy is low (e.g., battery below 15%, battery degradation alerts, power draw from malfunctioning hardware, loss of ability to support high-energy sensors).
weight: 2
axis: Implicit Criteria

criterion: The response describes the data fusion process, specifying how Inertial Measurement Unit (IMU) data is combined with at least two other distinct sensor modalities (e.g., vision, LiDAR, radar, depth cameras) to support accurate localization and survivor or hazard detection in disaster environments.
weight: 3
axis: Explicit Criteria

criterion: The response presents validation results using at least one structured format (e.g., table, line chart, bar chart, scalability curve) to clearly illustrate trends in performance metrics across different disaster scenarios.
weight: 3
axis: Communication Quality

criterion: The response mentions technologies or frameworks that require GPS or Internet connectivity to function (e.g., GPS-based localization, cloud-based task allocation, remote SLAM servers, centralized Internet-dependent coordination).
weight: -5
axis: Explicit Criteria

criterion: The response emphasizes unrelated machine learning techniques without tying them to survivor or hazard detection in disaster environments (e.g., generic image classification benchmarks such as CIFAR-10 or ImageNet, large language model text generation, sentiment analysis of social media, generic speech recognition pipelines).
weight: -5
axis: Explicit Criteria

criterion: The response discusses generic project management or team logistics (e.g., assigning human operators, budget planning, training sessions, or organizational workflow) without justifying their relevance to the technical swarm control framework.
weight: -5
axis: Explicit Criteria

criterion: The response proposes a specific distributed mapping or data sharing protocol (e.g., distributed pose graph optimization, multi-robot map merging, a distributed hash table for landmark sharing, gossip-based information sharing) and explains how it allows the swarm to share critical information (e.g., victim locations, new hazards, blocked passages, task assignments) efficiently over a spotty network.
weight: 4
axis: Explicit Criteria

criterion: The response specifies a method for detecting environmental hazards, referencing at least one sensor-based technique (e.g., using LiDAR for structural integrity assessment, gas sensors for detecting toxic environments, thermal imaging for identifying fire risks) and explains how this information is used by the swarm (e.g., to update task allocation, to mark 'no-go' zones on the distributed map, to detect survivors of hazards, to triage rescues).
weight: 3
axis: Explicit Criteria

criterion: The response's task allocation mechanism explicitly differentiates between distinct functional roles mentioned in the prompt (i.e., mapping, actively searching, removing debris, clearing blockages) and explains how robots are assigned to these different task types based on their suitability (e.g., platform type, sensor payload, energy level, mobility level).
weight: 3
axis: Explicit Criteria