In November 2012, the eleventh Workshop on Distributed Autonomous Robotic Systems (DARS 2012) was held in Baltimore, Maryland on the campus of Johns Hopkins University. Previous DARS workshops were held in 1992, 1994, and 1996 in Riken, Wako, Japan; Karlsruhe, Germany (1998); Knoxville, Tennessee, USA (2000); Fukuoka, Japan (2002); Toulouse, France (2004); Minneapolis, Minnesota, USA (2006); Tsukuba, Ibaraki, Japan (2008); and Lausanne, Switzerland (2010).

We present the synthesis and analysis of distributed ensemble control policies to enable a team of robots to control their distribution across a collection of tasks. We assume that individual robot controllers are modeled as a sequential composition of individual task controllers. A macroscopic description of the team dynamics is then used to synthesize ensemble feedback control strategies that maintain the desired distribution of robots across the tasks. We present a distributed implementation of the ensemble feedback strategy that can be implemented with minimal communication requirements. Different from existing strategies, the approach results in individual robot control policies that maintain the desired mean and the variance of the robot populations at each task. We present the stability properties of the ensemble feedback strategy, verify the feasibility of the distributed ensemble controller through high-fidelity simulations, and examine the robustness of the strategy to sensing and/or actuation failures. Specifically, we consider the case when robots are subject to estimation and navigation errors resulting from lossy inter-agent wireless communication links and localization errors.

Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation.

The use of large groups of robots in the execution of complex tasks has received much attention in recent years. Generally called robotic swarms, these systems employ a large number of simple agents to perform different types of tasks. A basic requirement for most robotic swarms is the ability for safe navigation in shared environments. Particularly, two desired behaviors are to keep robots close to their kin and to avoid merging with distinct groups. These are respectively called cohesion and segregation, which are observed in several biological systems. In this paper, we investigate two different approaches that allow swarms of robots to navigate in a cohesive fashion while being segregated from other groups of agents. Our first approach is based on artificial potential fields and hierarchical abstractions. However, this method has one drawback: It needs a central entity which is able to communicate with all robots. To cope with this problem, we introduce a distributed mechanism that combines hierarchical abstractions, flocking behaviors, and an efficient collision avoidance mechanism. We perform simulated and real experiments to study the feasibility and effectiveness of our methods. Results show that both approaches ensure cohesion and segregation during swarm navigation.

We consider the problem of dynamic reconfiguration by modular self-reconfigurable robots (MSRs) in the presence of uncertainty in their motion and the environment. Specifically, we consider the situation where the MSR is unable to continue its motion in its current configuration and needs to identify a new configuration among the existing modules, which would be the most configuration suitable for performing the robot's assigned task under the current circumstances. To address this problem, we propose a new data structure called an uncertain coalition structure graph (UCSG) that accommodates uncertainty in the MSR's motion and the environment, using a framework from cooperative game theory called the coalition structure graph. We then propose a new search algorithm called searchUCSG that intelligently prunes nodes from the UCSG using a modified branch-and-bound technique. We have shown analytically that our algorithm is anytime, that is, if it terminates arbitrarily, it returns the best solution found thus far, which is guaranteed to be within a constant bound from the optimal solution. We have verified the performance of our algorithm experimentally in simulation and shown that it is able to find a solution that is within the worst bound of 80% of the optimal solution while exploring only half of the nodes in the UCSG. Our algorithm also takes lesser computation time than the existing algorithms (that do not model uncertainty) for solving similar problems. Finally, to verify the operation of our algorithm, we have implemented it to partition a set of mobile e-puck robots into clusters and shown how different number of robots and different robot motion uncertainty parameters affect the formed clusters.

Traditional cable robots derive their manipulation capabilities using spooling winches at fixed base locations. In our previous work, we examined enhancing manipulation capabilities of cable robots by the addition of base mobility to spooling winches (allowing a group of mobile robots to cooperatively manipulate a payload using cables). Base mobility facilitated the regulation of the tension-direction (via active coordination of mobile bases) and allowed for better conditioning of the wrench-feasible workspace. In this paper we explore putting idler pulleys on the payload attachment as alternate means to simplify the design and enable practical deployment. We examine analysis of the system using ellipse geometry and develop a virtual cable-subsystem formulation (which also facilitates subsumption into the previously developed mobile cable robot analysis framework). We also seek improvement of the tension distribution by utilizing configuration space redundancy to shape the tension null space. This tension distribution shaping is implemented in the form of a tension factor optimization problem over the workspace and explored via both simulation and experimental studies.

This paper presents decentralized algorithms for coverage with mobile robots on a graph. Coverage is an important capability of multi-robot systems engaged in a number of different applications, including placement for environmental modeling, deployment for maximal quality surveillance, and even coordinated construction. We use distributed vertex substitution for locational optimization and equal mass partitioning, and the controllers minimize the corresponding cost functions. We prove that the proposed controller with two-hop communication guarantees convergence to the locally optimal configuration. We evaluate the algorithms in simulations and also using four mobile robots.

We present a model of construction using iterative amorphous depositions and give a distributed algorithm to reliably build ramps in unstructured environments. The relatively simple local strategy for interacting with irregularly shaped, partially built structures gives rise to robust adaptive global properties. We illustrate the algorithm in both single robot and multi-robot cases via simulations and describe how to solve key technical challenges to implementing this algorithm via a robotic prototype.

This paper focuses on behavior recognition in an underwater application as a substitute for communicating through acoustic transmissions, which can be unreliable. The importance of this work is that sensor information regarding other agents can be leveraged to perform behavior recognition, which is activity recognition of robots performing specific programmed behaviors, and task-assignment. This work illustrates the use of Behavior Histograms, Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs) to perform behavior recognition. We present challenges associated with using each behavior recognition technique along with results on individually selected test trajectories, from simulated and real sonar data, and real-time recognition through a simulated mission.

Beginning with a deterministic distributed feedback control for nonholonomic vehicle formations, we develop a stochastic optimal control approach for agents to enhance their non-optimal controls with additive correction terms based on the Hamilton–Jacobi–Bellman equation, making them optimal and robust to uncertainties. In order to avoid discretization of the high-dimensional cost-to-go function, we exploit the stochasticity of the distributed nature of the problem to develop an equivalent Kalman smoothing problem in a continuous state space using a path integral representation. Our approach is illustrated by numerical examples in which agents achieve a formation with their neighbors using only local observations.