In this paper, we consider a multi-phased, minimalistic approach to mobile robot localization that constrains the robot's ability to sense its environment to a binary detection of uniquely identifiable landmarks having unknown position (e.g., a WiFi transceiver detecting network SSIDs). Central to the proposed solution are dual landmark and observation complexes (instances of simplicial nerve complexes), which can be iteratively built through local observations without any metric or time-sequenced information. We have shown that these complexes approximate the topology of the underlying physical environment. Specifically, the notion of a “hole” (i.e., a topological invariant) within these complexes naturally represents a physical structure (e.g., a building) that limits landmark visibility/communication with respect to the robot's location. Taking advantage of this property, we formulate a homological sensing model that operates on these constructs enabling the robot to “count” the number of structures in its vicinity using local homology computations as a pseudo-metric surrogate sensor. Our homological sensor is highlighted in the context of a Monte-Carlo localization algorithm that resolves robot location by correlating the measured number of topological invariants with an unlabeled, metric map location.

This paper presents the locomotion approach of a novel quadruped robot which is able to carry various effectors for achieving manufacturing tasks in large workspaces. Equipped with lockers on some of the passive joints and clamping devices at the end of its limbs, this quadruped uses eight actuators for achieving manufacturing tasks as well as locomotion tasks. In the following sections, we first present the proposed robot and its two working modes. Then, the locking strategy of the robot is formulated as an optimization problem. Also, a practical method for managing the limbs swinging movement is addressed. At last, the presented approach is applied on two concrete examples. Possessing a low degree of kinematic redundancy, the proposed quadruped shows a reasonable locomotion capacity which allows it to achieve locomotion with respect to some extra constrains in its workspaces.

In this paper, we formulate a distributed, energy-aware control policy aimed at enabling persistent surveillance of a specified region of interest by teams of networked robots. Central to our formulation is the fundamental idea that as an agent participating in coverage approaches a low energy reserve the team should cooperatively adjust the coverage formation to allow the agent to return to a designated base station, where it can recharge before rejoining the effort. Towards this end, we build upon recent efforts in employing Centroidal Voronoi Tessellation (CVT)-based coverage control laws by defining a policy that exploits a power-dependent weighting scheme that embeds an agent's trade-off to achieve its coverage mission and to maintain a desired energy reserve to guarantee its own safety. Stability of the proposed approach is considered, and we show that coupling our continuous controller with a straightforward switching mechanism guarantees every agent will return to its base station safely. Simulation results are presented to verify and demonstrate the utility of the proposed control scheme.

In this note, we present initial results towards developing a distributed algorithm for repairing topological holes in the sensor cover of a mobile robot team. Central to our approach is the melding of recent advances in the application of computational homology (a sub-discipline of algebraic topology) to static sensor networks with relative metric information (i.e. relative pose). More precisely, we consider a greedy, hybrid (discrete-continuous) algorithm whereby a desired Cěch complex, the simplicial complex that captures the underlying topology of the sensing cover, is iteratively generated using local rules (between multi-hop neighbors) and agents are driven towards achieving this topology via a gradient-ascent simplicial control law. Convergence of the proposed algorithm is established as a function of the convergence of the underlying simplicial control law, and the relationship of the latter to the spectrum of the combinatorial Laplacian is considered. Simulation results for teams operating in ℝ2are presented.

In this paper, we present an optimization framework for target tracking with mobile robot teams. The target tracking problem is modeled as a generic semidefinite program (SDP). When paired with an appropriate objective function, the solution to the resulting problem instance yields an optimal robot configuration for target tracking at each time-step, while guaranteeing target coverage (each target is tracked by at least one robot) and maintaining network connectivity. Our methodology is based on the graph theoretic result where the second smallest eigenvalue of the interconnection graph Laplacian matrix is a measure for the connectivity of the graph. This formulation enables us to model agent-target coverage and inter-agent communication constraints as linearmatrix inequalities.We also show that when the communication constraints can be relaxed, the resulting problem can be reposed as a second-order cone program (SOCP) which can be solved significantly more efficiently than its SDP counterpart. Simulation results for a team of robots tracking multiple targets are presented.

This paper investigates convex optimization strategies for coordinating a large-scale team of fully actuated mobile robots. Our primary motivation is both algorithm scalability as well as real-time performance. To accomplish this, we employ a formal definition from shape analysis for formation representation and repose the motion planning problem to one of changing (or maintaining) the shape of the formation. We then show that optimal solutions, minimizing either the total distance or minimax distance the nodes must travel, can be achieved through second-order cone programming techniques. We further prove a theoretical complexity for the shape problem of O(m1.5) as well as O(m) complexity in practice, where m denotes the number of robots in the shape configuration. Solutions for large-scale teams (1000's of robots) can be calculated in real time on a standard desktop PC. Extensions integrating both workspace and vehicle motion constraints are also presented with similar complexity bounds. We expect these results can be generalized for additional motion planning tasks, and will prove useful for improving the performance and extending the mission lives of large-scale robot formations as well as mobile ad hoc networks.

The hybrid free-space optics/radio frequency (FSO/RF) network paradigm promises new levels of throughput for sensor and mobile ad-hoc networks. However, several challenges must be addressed before such a network model can be realized. These include a means by which deployed robots can autonomously establish optical links over sufficiently long distances as well as the formulation of a mobile network architecture that can exploit high throughput FSO channels. In this paper, we offer solutions to these problems in the form of hierarchical link acquisition and routing protocols. The heart of our link acquisition system (LAS) is a vision-based alignment phase for locating robot link partners via high zoom camera systems. Identification is accomplished in real-time using a multi-resolution image representation and normalized intensity distribution (NID) as a similarity metric. Our routing protocol relies upon the hierarchical state routing (HSR) model adapted to the FSO/RF paradigm. Experimental results from a large set of link acquisition trials as well as a small scale FSO/RF deployment are provided to support our approach.