Robotic detection of people in crowded and/or cluttered human-centered environments including hospitals, stores and airports is challenging as people can become occluded by other people or objects, and deform due to clothing or pose variations. There can also be loss of discriminative visual features due to poor lighting. In this letter, we present a novel multimodal person detection architecture to address the mobile robot problem of person detection under intraclass variations. We present a two-stage training approach using: 1) a unique pretraining method we define as Temporal Invariant Multimodal Contrastive Learning (TimCLR), and 2) a Multimodal YOLOv4 (MYOLOv4) detector for finetuning. TimCLR learns person representations that are invariant under intraclass variations through unsupervised learning. Our approach is unique in that it generates image pairs from natural variations within multimodal image sequences and contrasts crossmodal features to transfer invariances between different modalities. These pretrained features are used by the MYOLOv4 detector for finetuning and person detection from RGB-D images. Extensive experiments validate the performance of our DL architecture in both human-centered crowded and cluttered environments. Results show that our method outperforms existing unimodal and multimodal person detection approaches in detection accuracy when considering body occlusions and pose deformations in different lighting.

Hand-drawn maps can be used to convey navigation instructions between humans and robots in a natural and efficient manner. However, these maps can often contain inaccuracies such as scale distortions and missing landmarks which present challenges for mobile robot navigation. This letter introduces a novel Hand-drawn Map Navigation (HAM-Nav) architecture that leverages pre-trained vision language models (VLMs) for robot navigation across diverse environments, hand-drawing styles, and robot embodiments, even in the presence of map inaccuracies. HAM-Nav integrates a unique Selective Visual Association Prompting approach for topological map-based position estimation and navigation planning as well as a Predictive Navigation Plan Parser to infer missing landmarks. Extensive experiments were conducted in photorealistic simulated environments, using both wheeled and legged robots, demonstrating the effectiveness of HAM-Nav in terms of navigation success rates and Success weighted by Path Length. Furthermore, a user study in real-world environments highlighted the practical utility of hand-drawn maps for robot navigation as well as successful navigation outcomes compared against a non-hand-drawn map approach.

Multirobot coordination for finding multiple users in an environment can be used in numerous robotic applications, including search and rescue, surveillance/monitoring, and activities of daily living assistance. Existing approaches have limited coordination between robots when generating team plans or do not consider user location probability within these plans. This results in long searches and robots potentially revisiting the same locations in succession. In this article, we present a novel multirobot person search system to generate search plans for multirobot teams to find multiple dynamic users before a deadline. Our approach is unique in that it simultaneously considers the search actions of all robots and user location probabilities when generating team plans, where user location probabilities are represented as conditional spatial-temporal probability density functions. We model this multirobot person search problem as a two-stage optimization problem to maximize the expected number of users found before the deadline. Stage 1 solves the action selection problem to determine a set of team actions, and the second stage solves the action allocation problem to distribute these actions amongst the robots. Namely, in stage 1, a novel conditional multiperiod multiknapsack problem is modeled as a min-flow graph solved sequentially by the Bellman–Ford shortest path algorithm. Stage 2 is a variant of the min-max multitraveling salesperson problem which models the environment topology as a search region network and search times selected by the previous stage. This stage is solved by a novel fuzzy clustering method. Numerous experiments comparing our proposed method to other existing approaches with varying environment sizes, search durations, and the number of users showed that our approach was able to find more target users before a defined deadline.