Command and control of an aerial swarm is a complex task. This task increases in difficulty when the flight volume is restricted and the swarm and operator inhabit the same workspace. This work presents a novel method for interacting with and controlling a swarm of quadrotors in a confined space. EMG-based gesture control is used to control the position, orientation, and density of the swarm. Inter-agent as well as agent-operator collisions are prevented through a velocity controller based on a distance-based potential function. State feedback is relayed to the operator via a vibrotactile haptic vest. This cobot human swarm interface prioritizes operator safety while reducing the cognitive load during control of a cobot swarm. This work demonstrates that an operator can safely and intuitively control a swarm of aerial robots in the same workspace.

This paper presents an estimation and control framework to stabilize the parallel motion of a school of robotic fish using sensory feedback. Each robot is modeled as a constant speed, planar, self-propelled particle that produces a flowfield according to a dipole potential flow model. An artificial lateral line system senses pressure fluctuations at several locations along each robot's body. The equations of motion and measurement model are formulated in a path frame that translates and rotates with each robot's position and velocity, respectively. A particle filter estimates the relative position and heading of other nearby robots. The resulting estimate drives a Lyapunov-based formation controller to synchronize the headings of the robots and achieve a parallel formation. Numerical simulations illustrate the proposed approach.

In recent years, micro-mobility transport vehicles have become very popular. As a result, the challenge of developing new strategies and tools for the safety of users and pedestrians in shared travelways has attracted a lot of attention. Previous studies have generally used a social force model as the main tool for the prediction of pedestrian movements. However, existing models cannot be directly applied to wheeled vehicles such as electrical scooters. This study presents a modified social force model to predict the interactions of an electric scooter with a pedestrian crowd by considering the scooter’s kinematics constraints and geometry and the velocity-dependent behaviors of the rider. Moreover, experiments are performed to calibrate the parameters of the proposed model and compare it to an existing social force model. The experimental results demonstrate that the scooter social force model is superior to the original model due to its higher prediction accuracy. Using the scooter model with experimentally calibrated parameters, numerical simulations illustrate the behavior of an e-scooter rider in a pedestrian crowd. Acceleration and force metrics are introduced to evaluate the pedestrian comfort and safety. Monte Carlo simulations provide insights for urban planners about how travelway width, pedestrian density, and the e-scooter rider’s desired velocity affect pedestrian actual and perceived safety.

We describe a virtual-reality framework for investigating startle-response behavior in fish. Using real-time three-dimensional tracking, we generate looming stimuli at a specific location on a computer screen, such that the shape and size of the looming stimuli change according to the fish's perspective and location in the tank. We demonstrate the effectiveness of the setup through experiments on Giant danio and compute the success rate in eliciting a response. We also estimate visual startle sensitivity by presenting the stimulus from different directions around the fish head. The aim of this work is to provide the basis for quantifying escape behavior in fish schools.

This paper describes a probabilistic framework to estimate the shape and position of multiple fish in a school. We model the fish shape as an ellipsoid with a curvature coefficient that allows us to incorporate bending. An expression for the extremal contour in terms of state parameters is used to derive a likelihood function for shape. We present a motion model that uses curvature as an input to the turning rate. Tracking is performed using a particle filter with joint probabilistic data association. We evaluate our algorithm using simulated data and further characterize its performance using real data from a laboratory experiment with six giant danios.

This paper describes a model-based probabilistic framework for tracking a fleet of laboratory-scale underwater vehicles using multiple fixed cameras. We model the target motion as a steered particle whose dynamics evolve on the special Euclidean group. We provide a likelihood function that extracts three-dimensional position and pose measurements from monocular images using projective geometry. The tracking algorithm uses particle filtering with selective resampling based on a threshold and nearest neighbor data association for multiple targets.We describe results obtained from two tracking experiments: first with one vehicle and a second experiment with two targets. The tracking algorithm for single target experiment is validated using data denial.

This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored