Nilanjan Chakraborty

Human Interaction With Robot Swarms: A Survey

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.

Provably-Good Distributed Algorithm for Constrained Multi-Robot Task Assignment for Grouped Tasks

In this paper, we present provably-good distributed task assignment algorithms for a heterogeneous multi-robot system, in which the tasks form disjoint groups and there are constraints on the number of tasks a robot can do (both within the overall mission and within each task group). Each robot obtains a payoff (or incurs a cost) for each task and the overall objective for task allocation is to maximize (minimize) the total payoff (cost) of the robots. In general, existing algorithms for task allocation either assume that tasks are independent or do not provide performance guarantee for the situation, in which task constraints exist. We present a distributed algorithm to provide an almost optimal solution for our problem. The key aspect of our distributed algorithm is that the overall objective is (almost) maximized by each robot maximizing its own objective iteratively (using a modified payoff function based on an auxiliary variable, called price of a task). Our distributed algorithm is polynomial in the number of tasks, as well as the number of robots.

Bounds of Neglect Benevolence in Input Timing for Human Interaction with Robotic Swarms

Robotic swarms are distributed systems whose members interact via local control laws to achieve a variety of behaviors, such as flocking. In many practical applications, human operators may need to change the current behavior of a swarm from the goal that the swarm was going towards into a new goal due to dynamic changes in mission objectives. There are two related but distinct capabilities needed to supervise a robotic swarm. The first is comprehension of the swarms state and the second is prediction of the effects of human inputs on the swarms behavior. Both of them are very challenging. Prior work in the literature has shown that inserting the human input as soon as possible to divert the swarm from its original goal towards the new goal does not always result in optimal performance (measured by some criterion such as the total time required by the swarm to reach the second goal). This phenomenon has been called Neglect Benevolence, conveying the idea that in many cases it is preferable to neglect the swarm for some time before inserting human input. In this paper, we study how humans can develop an understanding of swarm dynamics so they can predict the effects of the timing of their input on the state and performance of the swarm. We developed the swarm configuration shape-changing Neglect Benevolence Task as a Human Swarm Interaction (HSI) reference task allowing comparison between human and optimal input timing performance in control of swarms. Our results show that humans can learn to approximate optimal timing and that displays which make consensus variables perceptually accessible can enhance performance. Categories and Subject Descriptors I.2.9 [Robotics]: Operator Interfaces; I.2.11 [Distributed Artificial Intelligence]: Multiagent Systems; H.1.2 [User/Machine Systems]: Human Factors; H.5.2 [User Interfaces]: Benchmarking General Terms Algorithms; Design; Experimentation; Human Factors; Performance; Theory

Distributed Algorithms for Multirobot Task Assignment With Task Deadline Constraints

We present distributed algorithms for multirobot task assignment where the tasks have to be completed within given deadlines. Each robot has a limited battery life and thus there is an upper limit on the amount of time that it has to perform tasks. Performing each task requires certain amount of time (called the task duration) and each robot can have different payoffs for the tasks. Our problem is to assign the tasks to the robots such that the total payoff is maximized while respecting the task deadline constraints and the robots battery life constraints. Our problem is NP-hard since a special case of our problem is the classical generalized assignment problem (which is NP-hard). There are no known algorithms (distributed or centralized) for this problem with provably good guarantees of performance. We present a distributed algorithm for solving this problem and prove that our algorithm has an approximation ratio of 2. For the special case of constant task duration we present a distributed algorithm that is provably almost optimal. Our distributed algorithms are polynomial in the number of robots and the number of tasks. We also present simulation results to depict the performance of our algorithms. Note to Practitioners-In this paper, we present provably good multirobot task assignment algorithms, while considering practical constraints like task deadlines and limited battery life of robots. Such constraints are relevant in many applications including parts movement by robots in manufacturing, delivery of goods by unmanned vehicles, and search and rescue operations. Our solution is applicable to a group of heterogeneous robots with different suitability (i.e., payoffs) for different tasks. Our distributed approach is independent of the underlying robot communication network topology, and thus can be applied to a wide range of robot network deployments. Finally, our approach is easy to implement, has low communication requirements, and it is scalable, since its running time is linear in the number of robots and tasks.

Multi-robot long-term persistent coverage with fuel constrained robots

In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.

Demand side energy management via multiagent coordination in consumer cooperatives

A key challenge in creating a sustainable and energy-efficient society is to make consumer demand adaptive to the supply of energy, especially to the renewable supply. In this article, we propose a partially-centralized organization of consumers (or agents), namely, a consumer cooperative that purchases electricity from the market. In the cooperative, a central coordinator buys the electricity for the whole group. The technical challenge is that consumers make their own demand decisions, based on their private demand constraints and preferences, which they do not share with the coordinator or other agents. We propose a novel multiagent coordination algorithm, to shape the energy demand of the cooperative. To coordinate individual consumers under incomplete information, the coordinator determines virtual price signals that it sends to the consumers to induce them to shift their demands when required. We prove that this algorithm converges to the central optimal solution and minimizes the electric energy cost of the cooperative. Additionally, we present results on the time complexity of the iterative algorithm and its implications for agents incentive compatibility. Furthermore, we perform simulations based on real world consumption data to (a) characterize the convergence properties of our algorithm and (b) understand the effect of differing demand characteristics of participants as well as of different price functions on the cost reduction. The results show that the convergence time scales linearly with the agent population size and length of the optimization horizon. Finally, we observe that as participants flexibility of shifting their demands increases, cost reduction increases and that the cost reduction is not sensitive to variation in consumption patterns of the consumers.

Asynchronous distributed information leader selection in robotic swarms

This paper presents asynchronous distributed algorithms for information leader selection in multi-robot systems based on local communication between each robot and its direct neighbours in the systems communication graph. In particular, the information leaders refer to a small subset of robots that are near the boundary of the swarm and suffice to characterize the swarm boundary information. The leader selection problem is formulated as finding a core set that can be used to compute the Minimum-Volume Enclosing Ellipsoid (MVEE) representing the swarm boundary. Our algorithms extract this core set in a fully distributed manner and select core set members as information leaders, thus extending abstract centralized MVEE core set algorithms for robotic swarm applications. We consider different communication conditions (e.g. dynamic network topology) and system configurations (e.g. anonymous robots or uniquely identified robots) and present a variety of approaches for core set selection with associated proofs for convergence. Results for simulated swarms of 50 robots and experiments with a swarm of 10 TurtleBots are provided to evaluate the effectiveness of the proposed algorithms.

Automated sequencing of swarm behaviors for supervisory control of robotic swarms

Robotic swarms are distributed systems that exhibit global behaviors arising from local interactions between individual robots. Each robot can be programmed with several local control laws that can be activated depending on an operators choice of global swarm behavior. While some simple behaviors (e.g. rendezvous) with guaranteed performance on known objectives under strict assumptions have been studied in the literature, real missions occur in uncontrolled environments with dynamically arising objectives and require combinations of behaviors. Given a library of swarm behaviors, a supervisory operator commanding the swarm must choose a sequence of behaviors to execute in order to accomplish a particular task during a mission composed of many dynamically arising tasks. In this paper, we formalize the problem of finding an optimal behavior sequence to maximize swarm performance on a complex task. Given the swarm behavior library, a set of decision time points and a performance criterion, we present an informed search algorithm that computes the maximum performance behavior sequence. The algorithm is proven to be optimal and complete. A relevant modification is presented that generates bounded suboptimal solutions more quickly. We apply the algorithm to a swarm navigation application and a dynamic area coverage application, demonstrating the utility of our algorithm even in situations where the behaviors in the library have not been designed for the task at hand.

Distributed dynamic priority assignment and motion planning for multiple mobile robots with kinodynamic constraints

We present a distributed on-line coordinated motion planning approach for a group of mobile robots moving amidst dynamic obstacles. The objective for the motion planning is to minimize the total distance traveled by the robots as well as the danger of deadlock. Kinematic constraints, robot-obstacle collision avoidance constraints, and velocity/acceleration constraints are explicitly considered in individual robots motion planner. A dynamic priority based scheme is proposed to deal with pair-wise inter-robot collision constraints. In particular, we model the assignment of priority into a minimum linear ordering problem (MLOP). We prove that the objective function of the MLOP is supermodular and propose a decentralized supermodular linear ordering algorithm that interleaves dynamic priority assignment and planning for the robots, such that the overall path length and the danger of deadlock are both minimized. Simulation results are provided to show the effectiveness of the proposed approach.

Multi-Robot Persistent Coverage with stochastic task costs

We propose the Stochastic Multi-Robot Persistent Coverage Problem (SMRPCP) and correspondant methodology to compute an optimal schedule that enables a fleet of energy-constrained unmanned aerial vehicles to repeatedly perform a set of tasks while maximizing the frequency of task completion and preserving energy reserves via recharging depots. The approach enables online modeling of uncertain task costs and yields a schedule that adapts according to an evolving energy expenditure model. A fast heuristic method is formulated that enables online generation of a schedule that concurrently maximizes task completion frequency and avoids the risk of individual robot energy-depletion and consequential platform failure. Failure mitigation is introduced through a recourse strategy that routes robots based on acceptable levels of risk. Simulation and experimental results evaluate the efficacy of the proposed methodology and demonstrate online system-level adaptation due to increasingly certain costs models acquired during the deployment execution.