When agents in a multi-robot team have limited knowledge about their relative performance, their teammates, or the environment, robots must observe individual performance variations and adapt accordingly. We propose robot reputation to assess the historical performance of agents and make future adaptations in a persistent coverage task. We consider a heterogeneous multi-robot team, where robots are equipped with different capabilities to serve discrete events in an environment. We utilize a heterogeneous coverage control approach to partition the space according to robot capabilities and the estimated probability density, such that the robot is responsible for serving the events in its assigned region. As the team serves events, we assign each robot a reputation, which is then used to adjust the size of a robot’s region, thus adjusting the amount of space a robot is responsible for serving. Our simulations show that using reputation to weigh the size of the Voronoi cells outperforms the case where we neglect reputation.

This paper presents a method to reduce computations for stochastic dynamic games with game-theoretic belief space planning through partially propagating beliefs. Complex interactions in scenarios such as surveillance, herding, and racing can be modeled using game-theoretic frameworks in the belief space. Stochastic dynamic games can be solved to a local Nash Equilibrium using a game-theoretic belief space variant of an iterative Linear Quadratic Gaussian (iLQG). However, the scalability of this method suffers due to the large dimensionality of beliefs which the iLQG must propagate. We examine the utility of partial belief space propagation, which allows polynomial runtime to decrease. We validate our findings through simulations and hardware implementation.

We consider a team of heterogeneous robots, each equipped with various types and quantities of resources, and tasked with supplying these resources to multiple areas of demand. We propose a Voronoi-based coverage control approach to deploy robots to areas of demand by defining a position- and time-varying density function to represent the quality at which demand is being met in the environment. This approach allows robots to prioritize the various demand locations in a continuous, distributed fashion. We present analyses to show that our controls drive the robots to critical points in the environment, along with simulations and hardware-in-the-loop experiments to demonstrate our approach.

We propose a co-navigation algorithm that enables a human and a robot to work together to navigate to a common goal. In this system, the human is responsible for making high-level steering decisions, and the robot, in turn, provides haptic feedback for collision avoidance and path suggestions while reacting to changes in the environment. Our algorithm uses optimized Rapidly-exploring Random Trees (RRT*) to generate paths to lead the user to the goal, via an attractive force feedback computed using a Control Lyapunov Function (CLF). We simultaneously ensure collision avoidance where necessary using a Control Barrier Function (CBF). We demonstrate our approach using simulations with a virtual pilot, and hardware experiments with a human pilot. Our results show that combining RRT* and CBFs is a promising tool for enabling collaborative human-robot navigation.

In this work, we consider a team of heterogeneous robots equipped with various types and quantities of resources, and tasked with supplying these resources to multiple dynamic demand locations. We present an adaptive control policy that enables robots to serve a dynamic demand: we allow demand to deplete as robots supply resources, and we allow demand injection and movement of demand locations. We show that the demand is input-to-state stable (ISS) under our proposed resource dynamics, and thus the robots can drive the demand to a steady state. Finally, we present simulations and hardware experiments to demonstrate our approach, and demonstrate the benefits of coverage over a persistent monitoring approach.

We propose a collaborative teleoperation algorithm which utilizes haptic force feedback to guide users around oncoming obstacles while accounting for non-holonomic constraints. The proposed algorithm predicts the user's goal, plans a path using a modified RRT*algorithm to the predicted goal, and provides haptic guidance to the path and away from obstacles when the user is in an unsafe pose. We show that the vehicle cannot collide with obstacles under the proposed algorithm following the haptic commands. We assess the per-formance of our algorithm with a virtual pilot in simulations and hardware experiments, demonstrating its ability to prevent collisions while reaching the goal location. Additionally, we demonstrate human-in-the-loop navigation with a Geomagic Touch haptic device providing force feedback to the user. These simulations and experiments show that the proposed haptic guidance system is a useful and effective tool for co-navigation of non-holonomic vehicles via teleoperation.