Regis Vincent

Efficient Sparse Pose Adjustment for 2D mapping

Pose graphs have become a popular representation for solving the simultaneous localization and mapping (SLAM) problem. A pose graph is a set of robot poses connected by nonlinear constraints obtained from observations of features common to nearby poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow cubically with the size of the graph. In this paper, we propose an efficient method for constructing and solving the linear subproblem, which is the bottleneck of these direct methods. We compare our method, called Sparse Pose Adjustment (SPA), with competing indirect methods, and show that it outperforms them in terms of convergence speed and accuracy. We demonstrate its effectiveness on a large set of indoor real-world maps, and a very large simulated dataset. Open-source implementations in C++, and the datasets, are publicly available.

Centibots: very large scale distributed robotic teams

We describe the development of Centibots, a framework for very large teams of robots that are able to perceive, explore, plan and collaborate in unknown environments.

The Soft Real-Time Agent Control Architecture

Real-time control has become increasingly important as technologies are moved from the lab into real world situations. The complexity associated with these systems increases as control and autonomy are distributed, due to such issues as temporal and ordering constraints, shared resources, and the lack of a complete and consistent world view. In this paper we describe a soft real-time architecture designed to address these requirements, motivated by challenges encountered in a real-time distributed sensor allocation environment. The system features the ability to generate schedules respecting temporal, structural and resource constraints, to merge new goals with existing ones, and to detect and handle unexpected results from activities. We will cover a suite of technologies being employed, including quantitative task representation, alternative plan selection, partial-order scheduling, schedule consolidation and execution and conflict resolution in an uncertain environment. Technologies which facilitate on-line real-time control, including meta-level accounting, schedule caching and variable time granularities are also discussed.

Task inference and distributed task management in the Centibots robotic system

We describe the Centibots system, a very large scale distributed robotic system, consisting of more than 100 robots, that has been successfully deployed in large, unknown indoor environments, over extended periods of time (i.e., durations corresponding to several power cycles). Unlike most multiagent systems, the set of tasks about which teams must collaborate is not given a priori. We first describe a task inference algorithm that identifies potential team commitments that collectively balance constraints such as reachability, sensor coverage, and communication access. We then describe a dispatch algorithm for task distribution and management that assigns resources depending on either task density or replacement requirements stemming from failures or power shortages. The targeted deployment environments are expected to lack a supporting communication infrastructure; robots manage their own network and reason about the concomitant localization constraints necessary to maintain team communication. Finally, we present quantitative results in terms of a search and rescue problem and discuss the team-oriented aspects of the system in the context of prevailing theories of multiagent collaboration.

Distributed multirobot exploration, mapping, and task allocation

We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams—of what we refer to as Centibots—consisting of as many as 100 robots. The results that we present involve search tasks that can be divided into a mapping stage in which robots must jointly explore a large unknown area with the goal of generating a consistent map from the fragment, a search stage in which robots are deployed within the environment in order to systematically search for an object of interest, and a protection phase in which robots are distributed to track any intruders in the search area. During the first stage, the robots actively seek to verify their relative locations in order to ensure consistency when combining data into shared maps; they must also coordinate their exploration strategies so as to maximize the efficiency of exploration. In the second and third stages, robots allocate search tasks among themselves; since tasks are not defined a priori, the robots first produce a topological graph of the area of interest and then generate a set of tasks that reflect spatial and communication constraints. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust.

Using Autonomy, Organizational Design and Negotiation in a Distributed Sensor Network

In this paper we describe our solution to a real-time distributed tracking problem. The system works not by finding an optimal solution, but through a satisficing search for an allocation that is “goodenough” to meet the specified resource requirements, which can then be revised over time if needed. The agents in the environment are first organized by partitioning them into sectors, reducing the level of potential interaction between agents. Within each sector, agents dynamically specialize to address scanning, tracking, or other goals, which are instantiated as task structures for use by the SRTA control architecture. These elements exist to support resource allocation, which is directly effected through the use of the SPAM negotiation protocol. The agent problem solving component first discovers and generates commitments for sensors to use for gathering data, then determines if conflicts exist with that allocation, finally using arbitration and relaxation strategies to resolve such conflicts. We have empirically tested and evaluated these techniques in both the Radsim simulation environment and using the hardware-based system.

Comparing Three Approaches to Large-Scale Coordination

Coordination of large groups of agents or robots is starting to reach a level of maturity where prototype systems can be built and tested in realistic environments. These more realistic systems require that both algorithmic and practical issues are addressed in an integrated solution. In this chapter, we look at three implementations of large-scale coordination examining common issues, approaches, and open problems. The key result of the comparison is that there is a surprising degree of commonality between the independently developed approaches, in particular the use of partial, dynamic centralization. Conversely, open issues and problems encountered varied greatly with the notable exception that debugging was a major issue for each approach.

Comparison of indoor robot localization techniques in the absence of GPS

When available, GPS is the quick and easy solution to localizing a robot. However, because it is often not available (e.g. indoors) or not reliable enough, other techniques, using laser range finders or cameras have been developed that offer better performance. For 2D localization,lLaser range finders are far more precise and easier to work with than cameras. We report here on the performance of several implementations of the main class of localization algorithms that use a laser, Simultaneous Localization And Mapping (SLAM) on the RAWSEEDS benchmark. SRI Internationals SLAM system has an RMS error in XY of 0.32m (0.22%). This is the best reported performance on this benchmark.

Dynamic Resource-bounded Negotiation in Non-additive Domains

The problem of group decision making in a non-strategic environment is presented and analyzed. The main focus is on the decision problem of task assignment in situations in which tasks interact and information relevant to the assignment problem is distributed and is contained locally in the agents in the group. Practically, it may be impossible to communicate all relevant distributed information to a single central decision maker due to communication costs, the size of the set of information, or other limitations. Instead, we provide a method to coordinate the sharing of a limited amount of information while making satisfactory (though possibly suboptimal) task assignments via a center-based algorithm called Mediation. Mediation implements an iterative and interactive hill-climbing search in a subset of the solution space by making successive proposals and sending those proposals to the group. Each proposal provides a context on which group members base their responses which provide the mediator with information to find a satisfactory outcome to the assignment problem. The properties of Mediation are compared with other approaches including parallel and combinatorial auctions. The theory and analysis is illustrated with examples from the domain of multi-sensor intruder tracking. Dynamic mediation extends the algorithm to environments in which problem features change during the decision-making process and in which agents augment the information that they provide using the language of rich bids. Experiments are used to validate the usefulness of mediation in key problem domains, including multi-sensor tracking. Finally, an architecture for agents, who need not be stationary, is described whereby agents can monitor task progress at execution time and then modify existing resource allocations based on the evolving situation.

UAV airspace management system UAMS

As technology advances, unmanned aerial vehicles (UAVs) are capable of more complex missions. Miniaturization results in a migration of capability from larger to smaller UAV platforms and in cost reduction. According to the DoDs Unmanned Aircraft Systems Roadmap 2005-2030, military UAVs are now required to execute intelligence, surveillance, reconnaissance, strike, suppression of enemy air defense, electronic attack, communications, aerial delivery, and resupply missions. It is therefore evident that UAV developments require a more intelligent and autonomous airspace and mission management approach then ever.