This paper proposes a heterogeneous teaming solution to the problem of target discovery and monitoring in unknown, non-convex environments. The team consists of two types of agents: agile agents with sensors capable of mapping their surroundings and slower agents that are capable of monitoring or servicing discovered targets. We propose an exploration algorithm that utilizes the IRIS algorithm to generate a graph decomposition from collision free ellipses contained within the environment. This graph is passed to the monitoring agents who execute polynomial complexity assignment and touring algorithms to generate high quality path plans which service all discovered targets. Our algorithmic structure allows the team to solve the problems of exploration, target discovery, assignment, and monitoring within unknown, non-convex environments efficiently using limited information. The performance of our proposed method is verified through batch simulations and complexity analysis.

Recent research has seen the advancement of drone depot models as a promising way to allocate drones for large-scale task completion. Applications of these drone depot models include data collection, environmental monitoring, package delivery, and more. This paper focuses on sharing agents between static depots for task allocation based on expected demand. We model the problem as a Binary Nonlinear Program, then derive an iterative neighborhood search based on solving a series of Binary Linear Programs to drive towards the optimal configuration of agents for each depot. We show that our method is more tractable than a Branch and Bound approach for this model as problem complexity grows. We also show through simulations that with near optimal allocation between local depots, the overall system performance will outperform greedy and non-sharing approaches.

This paper proposes a formulation for the risk-aware path planning problem which utilizes multi-objective optimization to dynamically plan trajectories that satisfy multiple complex mission specifications. In the setting of persistent monitoring, we develop a method for representing environmental information and risk in a way that allows for local sampling to generate Pareto-dominant solutions over a receding horizon. We propose two algorithms capable of solving these problems: a dense sampling approach and an improved method utilizing noisy gradient descent. Simulation results demonstrate the efficacy of our methods at persistently gathering information while avoiding risk, robust to randomly-generated environments.

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.

This paper proposes an algorithm for generating Pareto-optimal privacy-aware trajectories for multi-robot coverage. Our approach utilizes a genetic algorithm to generate a set of modified trajectories for a team of robots that wishes to obscure its goal from an observer. A novel velocity-constrained crossover algorithm ensures all child trajectories are feasible for a holonomic vehicle. The Pareto front of generated trajectories allows a team to select an allowable trade-off between privacy and coverage cost given within their task. Simulation results demonstrate the performance of our algorithm in Voronoi-based coverage control. We show our approach successfully obscures the objective from our proposed observer.

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 apply a novel framework for decomposing and reasoning about free space in an environment to a multi-agent persistent monitoring problem. Our decomposition method represents free space as a collection of ellipsoids associated with a weighted connectivity graph. The same ellipsoids used for reasoning about connectivity and distance during high level planning can be used as state constraints in a Model Predictive Control algorithm to enforce collision-free motion. This structure allows for streamlined implementation in distributed multi-agent tasks in 2D and 3D environments. We illustrate its effectiveness for a team of tracking agents tasked with monitoring a group of target agents. Our algorithm uses the ellipsoid decomposition as a primitive for the coordination, path planning, and control of the tracking agents. Simulations with four tracking agents monitoring fifteen dynamic targets in obstacle-rich environments demonstrate the performance of our algorithm.

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.

This paper presents a mobile UVC disinfection robot designed to mitigate the threat of airborne and surface pathogens. Our system comprises a mobile robot base, a custom UVC lamp assembly, and algorithms for autonomous navigation and path planning. We present a model of UVC disinfection and dosage of UVC light delivered by the mobile robot. We also discuss challenges and prototyping decisions for rapid deployment of the robot during the COVID-19 pandemic. Experimental results summarize a long-term deployment at The Greater Boston Food Bank, where the robot delivers (nightly) UVC dosages of at least 10 mJ/cm2to a 4000 ft2area in under 30 minutes. These dosages are capable of neutralizing 99% of coronaviruses, including SARS-CoV-2, on surfaces and in airborne particles. Further simulations present how this mobile UVC disinfection robot may be extended to classic problems in robotic path planning and adaptive multi-robot coverage control.

We address the problem of assigning a team of drones to autonomously capture a set desired shots of a dynamic target in the presence of obstacles. We present a two-stage planning pipeline that generates offline an assignment of drone to shots and locally optimizes online the viewpoint. Given desired shot parameters, the high-level planner uses a visibility heuristic to predict good times for capturing each shot and uses an Integer Linear Program to compute drone assignments. An online Model Predictive Control algorithm uses the assignments as reference to capture the shots. The algorithm is validated in hardware with a pair of drones and a remote controlled car.

Information gathering while interacting with other agents under sensing and motion uncertainty is critical in domains such as driving, service robots, racing, or surveillance. The interests of agents may be at odds with others, resulting in a stochastic noncooperative dynamic game. Agents must predict others’ future actions without communication, incorporate their actions into these predictions, account for uncertainty and noise in information gathering, and consider what information their actions reveal. Our solution uses local iterative dynamic programming in Gaussian belief space to solve a game-theoretic continuous POMDP. Solving a quadratic game in the backward pass of a game-theoretic belief-space variant of iterative linear-quadratic Gaussian control (iLQG) achieves a runtime polynomial in the number of agents and linear in the planning horizon. Our algorithm yields linear feedback policies for our robot, and predicted feedback policies for other agents. We present three applications: Active surveillance, guiding eyes for a blind agent, and autonomous racing. Agents with game-theoretic belief-space planning win 44% more races than without game theory and 34% more than without belief-space planning.

The safety of an autonomous vehicle not only depends on its own perception of the world around it, but also on the perception and recognition from other vehicles. If an ego vehicle considers the uncertainty other vehicles have about itself, then by reducing the estimated uncertainty it can increase its safety. In this paper, we focus on how an ego vehicle plans its trajectories through the blind spots of other vehicles. We create visibility-aware planning, where the ego vehicle chooses its trajectories such that it reduces the perceived uncertainty other vehicles may have about the state of the ego vehicle. We present simulations of traffic and highway environments, where an ego vehicle must pass another vehicle, make a lane change, or traverse a partially-occluded intersection. Emergent behavior shows that when using visibility-aware planning, the ego vehicle spends less time in a blind spot, and may slow down before entering the blind spot so as to increase the likelihood other vehicles perceive the ego vehicle.

This paper introduces the Weighted Buffered Voronoi tessellation, which allows us to define distributed, semicooperative multi-agent navigation policies with guarantees on collision avoidance. We generate the Voronoi cells with dynamic weights that bias the boundary towards the agent with the lower relative weight while always maintaining a buffered distance between two agents. By incorporating agent weights, we can encode selfish or prioritized behavior among agents, where a more selfish agent will have a larger relative cell over less selfish agents. We consider this semi-cooperative since agents do not cooperate in symmetric ways. Furthermore, when all agents start in a collision-free configuration and plan their control actions within their cells, we prove that no agents will collide. Simulations demonstrate the performance of our algorithm for agents navigating to goal locations in a position-swapping game. We observe that agents with more egoistic weights consistently travel shorter paths to their goal than more altruistic agents.

This paper investigates the safe path planning problem for an autonomous vehicle operating in unstructured, cluttered environments. While some objects may be accurately with canonical perception algorithms, other objects and clutter may be harder to track. We present an approach that combines two methods of risk assessment: for objects with reliable tracking, we use a Gaussian Process (GP) regulated risk map to describe the risk map information; for unknown objects that we fail to accurately track, we compute a Dynamic Risk Density (DRD) from the overall occupancy and velocity field from LiDAR scan snapshots. Several methods are proposed for combining the GP risk map and DRD, and the resultant hybrid risk map is used for the proposed safe path planning algorithm. Experimental results on an autonomous buggy show that the hybrid risk map is able to yield a safe path planner to navigate the autonomous testbed within the cluttered environments.

In this paper, we examine the problem of navigating cluttered environments without explicit object detection and tracking. We introduce the dynamic risk density to map the congestion density and spatial flow of the environment to a cost function for the agent to determine risk when navigating that environment. We build upon our prior work, wherein the agent maps the density and motion of objects to an occupancy risk, then navigate the environment over a specified risk level set. Here, the agent does not need to identify objects to compute the occupancy risk, and instead computes this cost function using the occupancy density and velocity fields around them. Simulations show how this dynamic risk density encodes movement information for the ego agent and closely models the object-based congestion cost. We implement our dynamic risk density on an autonomous wheelchair and show how it can be used for navigating unstructured, crowded and cluttered environments.

Current methods for autonomous management use strict first-come, first-serve (FCFS) ordering to manage incoming autonomous vehicles at an intersection. In this work, we present a coordination policy that swaps agent ordering to increase the system-wide performance while ensuring that the swaps are socially compliant. By considering an agent's Social Value Orientation (SVO), a social psychology metric for their willingness to help another vehicle, the central coordinator can reduce system delays while ensuring each individual vehicle increases their own utility. The FCFS-SVO algorithm is both computationally tractable and accounts for a variety of real-world agent types, such as human drivers and a variety of social orientations. Simulation results show that average vehicle delays decrease with swapping by enabling cooperation between agents. In addition, we show that the proportion of human drivers, as well as, the distribution of prosocial and egoistic vehicles in the system can have a prominent effect on the performance of the system.

This paper examines how vehicles can quickly quantify the level of congestion in their environment for planning. We use risk level sets to define a metric of congestion for the vehicles. Using this metric, we can quickly identify distributions of environment and driver features, such as velocities and number of neighbors, based on risk within human driving data sets. We use the NGSIM and highD data sets to study how risk influences behaviors in city and highway driving. From these data sets, we learn common risk thresholds for classifying low, medium, and high-risk situations. Using these thresholds, we develop simulations of an autonomous vehicle driving along a highway, and demonstrate how the chosen risk threshold influences the autonomous vehicle behavior.

In this paper, we address the problem of navigating in a cluttered environment by introducing a congestion cost that maps the density and motion of objects to an occupancy risk. We propose that an agent can choose a “risk level set” from this cost function and construct a planning space from this set. In choosing different levels of risk, the agent adjusts its interactions with the other agents. From the assumption that agents are self-preserving, we show that any agent planning within their risk level set will avoid collisions with other agents. We then present an application of planning with risk level sets in the framework of an autonomous vehicle driving along a highway. Using the risk level sets, the agent can determine safe zones when planning a sequence of lane changes. Through simulations in Matlab, we demonstrate how the choice of risk threshold manifests as aggressive or conservative behavior.

We present control strategies for robotic herders to drive noncooperative herds. Our key insight enforces geometrical relationships that map the combined dynamics to simple two-dimensional or three-dimensional nonholonomic vehicle models. We prove convergence of single-agent herds to a goal and propose strategies for multi-agent herds, verified in simulations and experiments.

We investigate the cooperative pursuit of an evader by a group of quadrotors in an environment with no-fly zones. While the pursuers cannot enter into no-fly zones, the evader may freely move through zones to avoid capture. Once the evader enters a no-fly zone, the pursuers calculate a reachable set of evader positions. Using tools from Voronoi-based coverage control applied to the evader's reachable set, we provide an algorithm that distributes the pursuers around the zone's boundary and minimizes the capture time once the evader leaves the no-fly zone. Robust model predictive control (RMPC) tools are used to control the quadrotors and to ensure that they always remain in free space. We demonstrate the performance of our proposed algorithms through a series of experiments on KMEL Nano+ quadrotors.

This paper proposes a new approach for a group of robots carrying out a collaborative task to adapt on-line to actuation performance variations among the robots. We consider the problem of multi-robot coverage, where a group of robots has to spread out to cover the environment. We suppose that some robots have poor actuation performance (e.g. weak motors, friction losses in the gear train, wheel slip, etc.) and some have strong actuation performance (powerful motors, little friction, favorable terrain, etc.). The robots do not know before hand the relative strengths of their actuation compared to the others in the team. The algorithm in this paper learns the relative actuation performance variations among the robots on-line, in a distributed fashion, and automatically compensates by giving the weak robots a small portion of the environment, and giving the strong robots a larger portion. Using a Lyapunov-type proof, we prove that the robots converge to locally optimal positions for coverage. The algorithm is demonstrated in both Matlab simulations and experiments using Pololu m3pi robots.

This paper presents a new control strategy to control a group of dog-like robots to drive a herd of non-cooperative sheep-like agents to a goal region in the environment. The sheep-like agents, which may be biological or robotic, respond to the presence of the dog-like robots with a repelling potential field common in biological models of the behavior of herding animals. Our key insight in designing control laws for the dog-like robots is to enforce geometrical relationships that allow for the combined dynamics of the dogs and sheep to be mapped to a simple unicycle robot model. We prove convergence of a single sheep to a desired goal region using two or more dogs, and we propose a control strategy for the case of any number of sheep driven by two or more dogs. Simulations in Matlab and hardware experiments with Pololu m3pi robots demonstrate the effectiveness of our control strategy.