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In social robotics, a pivotal focus is enabling robots to engage with humans in a more natural and seamless manner. The emergence of advanced large language models (LLMs) has driven significant advancements in integrating natural language understanding capabilities into social robots. This paper presents a system for speech-guided sequential planning in pick and place tasks, which are found across a range of application areas. The proposed system uses Large Language Model Meta AI (Llama3) to interpret voice commands by extracting essential details through parsing and decoding the commands into sequential actions. These actions are sent to DRL-VO, a learning-based control policy built on the Robot Operating System (ROS) that allows a robot to autonomously navigate through social spaces with static infrastructure and crowds of people. We demonstrate the effectiveness of the system in simulation experiment using Turtlebot 2 in ROS1 and Turtlebot 3 in ROS2. We conduct hardware trials using a Clearpath Robotics Jackal UGV, highlighting its potential for real-world deployment in scenarios requiring flexible and interactive robotic behaviors. 

Utilizing heterogeneous mobile sensors to actively gather information improves adaptability and reliability in extended environments. This article presents a cooperative multirobot multitarget search and tracking framework aimed at enhancing the efficiency of the heterogeneous sensor network, and consequently, improving the overall target tracking accuracy. The concept of normalized unused sensing capacity is introduced to quantify the information a sensor is currently gathering relative to its theoretical maximum. This measurement can be computed using entirely local information and is applicable to various sensor models, distinguishing it from previous literature on the subject. It is then utilized to develop a heuristics distributed coverage control strategy for a heterogeneous sensor network, adaptively balancing the workload based on each sensor's current unused capacity. The algorithm is validated through a series of robot operating system (ROS) and MATLAB simulations, demonstrating superior results compared to standard approaches that do not account for heterogeneity or current usage rates. 

This paper proposes a distributed estimation and control algorithm to allow a team of robots to search for and track an unknown number of targets. The number of targets in the area of interest varies over time as targets enter or leave, and there are many sources of sensing uncertainty, including false positive detections, false negative detections, and measurement noise. The robots use a novel distributed Multiple Hypothesis Tracker (MHT) to estimate both the number of targets and the states of each target. A key contribution is a new data association method that reallocates target tracks across the team. The distributed MHT is compared against another distributed multi-target tracker to test its utility for multi-robot, multi-target tracking. 

This paper compares different distributed control approaches which enable a team of robots search for and track an unknown number of targets. The robots are equipped with sensors which have a limited field of view (FoV) and they are required to explore the environment. The team uses a distributed formulation of the Probability Hypothesis Density (PHD) filter to estimate the number and the position of the targets. The resulting target estimate is used to select the subsequent search locations for each robot. This paper compares Lloyd’s algorithm, a traditional method for distributed search, with two typical stochastic optimization methods: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). This paper presents novel formulations of PSO and SA to solve the multi-target tracking problem, which more effectively trade off between exploration and exploitation. Simulations demonstrate that the use of these stochastic optimization techniques improves coverage of the search space and reduces the error in the target estimates compared to the baseline approach.