HARD-HAT: Socially aware navigation for safer heavy-duty construction.

Multi-Sensor Benchmark for Socially-Aware Navigation (CoHAN vs TEB) Overview This dataset provides a multi-session, multi-modal benchmark for socially-aware navigation in human-shared environments. It contains synchronized ROS bag recordings collected from a heavy-duty autonomous robot interacting with pedestrian. The dataset enables a direct comparison between: TEB (Timed Elastic Band) - geometric navigation baseline; and CoHAN (Cooperative Human-Aware Navigation) - socially-aware planner. Both planners are evaluated under identical conditions. Key Contributions Multi-sensor dataset (LiDAR, stereo vision, GNSS-RTK, IMU); Real human–robot interaction scenario; Quantitative benchmarking framework; and Reproducible ROS-based logs Dataset Structure TEB_bags.zip (8 sessions, 8.6 GB) CoHAN_bags.zip (10 sessions, 9.9 GB) cohan_in_the_field.mp4 (video, 186MB) Data Collection A structured data collection campaign was performed over 10 test sessions, each including human presence and interaction. For each run, the following metrics were logged and post-processed: Minimum pedestrian clearance distance (m); Number of close-contact events; and Collision occurrences. The same protocol was applied for both planners (CoHAN and TEB), ensuring comparability across datasets. During the experimental evaluation, a comprehensive set of ROS topics was recorded to ensure full observability of perception, localisation, planning, and human–robot interaction dynamics. Vision and Perception Sensors Camera intrinsic and extrinsic calibration parameters for the stereo camera setup. These topics ensure reproducibility and enable accurate depth reconstruction and image-based perception. zed_node/left/camera_info zed_node/right/camera_info Rectified and compressed RGB image streams from the stereo camera. These data provide visual context of the environment, including pedestrian appearance, posture, and relative motion. zed_node/left/image_rect_color/compressed zed_node/right/image_rect_color/compressed Cropped and fused 3D point cloud derived from LiDAR sensing. This topic represents the primary spatial perception input used for obstacle and pedestrian avoidance in the navigation stack. fused_point_cloud_cropped Localisation and State Estimation Raw IMU measurements used for high-frequency motion estimation, including linear acceleration and angular velocity. imu_rion GNSS-RTK outputs providing global position, time synchronisation, and velocity estimates. These topics support global localisation. gps/fix, /gps/time, /gps/vel Odometry output from the LIO-SAM framework, fusing LiDAR and IMU data. This serves as the primary locally consistent state estimate for navigation and trajectory execution. /lio_sam/imupreintegration/odom Dynamic and static coordinate frame transformations. These topics define the kinematic relationships between sensors, robot base, and world frames, enabling consistent spatial reasoning across all modules. /tf, /tf_static Human “Ground Truth” The “ground-truth” pose of the tracked pedestrian is actually a reference pedestrian pose estimated by the tracking pipeline. human1/base_pose_ground_truth Navigation Planning and Control The global reference path generated by the global planner. This represents the long-horizon navigation objective shared by both controllers. move_base/GlobalPlanner/plan Local trajectory generated by the HATEB (CoHAN-based) planner, explicitly incorporating human-aware constraints. move_base/HATebLocalPlannerROS/local_plan Local trajectory generated by the baseline TEB planner, used for comparative evaluation. move_base/TebLocalPlannerROS/local_plan Time-parameterised future poses of the robot predicted by the HATEB planner, used for forward simulation and human–robot interaction assessment. move_base/HATebLocalPlannerROS/local_plan_fp_poses Predicted future trajectories of surrounding agents (pedestrians) as modeled by the CoHAN framework. move_base/HATebLocalPlannerROS/agents_local_plans_fp_poses Discrete planner mode indicator (e.g., nominal navigation, human avoidance, cooperative passing), providing interpretability of planner decision-making. move_base/HATebLocalPlannerROS/mode_text Benchmarking and Evaluation Metrics Online computation of robot–pedestrian separation distance, used to derive minimum clearance metrics. navigation_benchmark/distance Event-based indicator of close-contact situations, enabling quantitative comparison of social compliance between planners. All logs include /tf and GNSS time to support post-hoc alignment and reproducible replay. navigation_benchmark/close_events Benchmark Metrics Pedestrian Clearance Distance Minimum distance between robot and humanResult: +56% improvement (CoHAN vs TEB) Close-Contact Events Threshold-based proximity violationsResult: 0 events for both planners Collision Detection Defined as footprint overlapResult: 0 collisions Panic Cost (using a MATLAB script and ROS bags) Interaction discomfort metricResult: CoHAN lower than TEB Time-to-Collision (TTC) (using a MATLAB script and ROS bags) Predictive safety metricResult: CoHAN higher than TEB Usage rosbag play <bag_file> NLU dataset For both tests, ROS bag files were recorded containing: Audio streams; ASR transcripts; NLU dialogue acts; and Performance verification prompts and results. Dataset Structure NLU_bags.zip (4 sessions) Test Methodology: Audio Playback Test Inputs A WAV file was generated containing: 3 male speakers 5 commands per speaker Commands: “STOP loader” “MOVE forward” “MOVE back” “TAKE the pallet from storage” “INCREASE velocity” Total utterances: 15 Test Environments Indoor (No Noise) Quiet room; No machinery active; and Baseline reference. Outdoor (High Noise) External environment; Loader robot powered on; and Diesel engine running continuously.