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When using a tool with a robotic hand-arm system, the stiffness at the grasped object plays a key role in the interaction with the environment, allowing the successful execution of the task. However, the rapidly increasing use of under-actuated hands in robotic systems due to their robustness and simplicity of control, pose limitations to the achievable object-level stiffness. Indeed, due to the serial coupling of the hand and the arm, the resulting object-level stiffness is determined by the most compliant of both elements. To address this problem, we propose a novel controller that takes into account the limited achievable geometry of the object stiffness ellipsoid given by a hand with under-actuation constraints, and exploits the contribution of the robotic arm in reshaping the final stiffness towards the desired profile. The under-actuation is illustrated by a coordinated stiffening of the hand fingers. The proposed method is experimentally validated by a hand-arm system performing a peg-in-hole task.

This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problems of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.

We describe the winning entry to the Amazon Picking Challenge 2015. From the experience of building this system and competing in the Amazon Picking Challenge, we derive several conclusions: (1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions--modularity versus integration, generality versus assumptions, computation versus embodiment, and planning versus feedback. (2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches. To achieve this, our community should agree on key aspects that characterize the solution space of robotic systems. (3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further. This is supported by the fact that our solution deviated from the majority of the other 2015 challenge entries along each of the spectra.

We consider the problem of configuration formation in modular robot systems where a set of modules that are initially in different configurations and located at different locations are required to assume appropriate positions so that they can get into a new, user-specified, target configuration. We propose a novel algorithm based on (sub)graph isomorphism, where the modules select locations or spots in the target configuration using a utility-based framework, while retaining their original configuration to the greatest extent possible, to reduce the time and energy required by the modules to disconnect and connect multiple times to form the target configuration. We have shown analytically that our proposed algorithm is complete and guarantees a Pareto-optimal allocation. Experimental simulations of our algorithm with different numbers of modules in different initial configurations and located initially at different locations, show that the planning time of our algorithm is nominal (order of msec for 100 modules). We have also compared our algorithm against a market-based allocation algorithm and shown that our proposed algorithm performs better in terms of time and number of messages exchanged.

We propose and evaluate a multi-robot system designed to enable live, improvisational theatric performance through online interaction between a performer and a robot system. The proposed system translates theatric performer intent into dynamically feasible trajectories for multi-robot ensembles without requiring prior knowledge of the ordering or timing of the desired robot motions. We allow a user to issue detailed instructions composed of desired motion descriptors in an online setting to specify the motion of varying collectives of robots via a centralized system planner. The centralized planner refines user motion specifications into safe and dynamically feasible trajectories thereby reducing the cognitive burden placed on the performer. We evaluate the system on a team of aerial robots (quadrotors), and show through offline simulation and online performance that the proposed system formulation translates online input into non-colliding dynamically feasible trajectories enabling a fleet of fifteen quadrotors to perform a series of coordinated behaviors in response to improvised direction from a human operator.

System monitoring can help to detect anomalies, but crafting monitors for robot systems is difficult due to the inherent complexity, changing, and uncertain operating environment. We address this challenge by automatically inferring system invariants and synthesizing those invariants into monitors to detect faults with an approach inspired by state of the art software engineering methods. Our approach is novel in that: (1) It automatically derives invariants from messages; (2) The invariants types are tailored to match the spatial, temporal, and architectural attributes of robotic systems; and (3) It automatically classifies and synthesizes invariants into an online invariants monitor node. We have assessed the approach in the context of two unmanned aerial vehicle systems running robot operating system. We found that monitoring the inferred invariants can reduce system failure rates when facing unexpected contexts from 76 to 11 %, and can detect differences between the lab environment and the field deployments.

In the original publication of the article, the incorrect author photo was displayed in biography section.

The most significant feature of the exoskeleton system, which distinguishes it from other robotic systems, is the symbiotic relationship between the exoskeleton and the wearer. For perfect symbiotic relationship, the exoskeleton should be able to exactly detect the wearer's intention to move. Existing methods by which lower extremity exoskeletons can detect human intentions are highly dependent on additional sensor systems or accurate dynamic models. In this paper, we propose a novel method for detecting human intention inspired by human behavior, and a control method that utilizes it. We define human intention as the tendency of humans to maintain a statically stable posture and minimize joint torques when supporting payloads. The control method reduces the computational requirements and simplifies the exoskeleton sensor system compared to existing methods. The experimentally measured ground reaction force was used to indirectly estimate the effects of our method on the wearer. The results suggest that the proposed method reduces the load acting on the wearer during locomotion under loaded conditions, and results in a portion of his body weight being supported by the exoskeleton when he is in an uncomfortable position.

We consider the problem of merging together multiple appearance-based maps independently built by a team of robots jointly exploring an indoor environment. Due to the lack of accepted metrics to evaluate the quality of merged appearance-based maps, we propose to use algebraic connectivity for this purpose, and we discuss why this is an appropriate measure. Next, we introduce QuickConnect, an anytime algorithm aiming to maximize the given metric and we show how it can merge couple of maps, as well as multiple maps at the same time. The proposed algorithm has been implemented and tested on a fully functioning robotic system building appearance-based maps using a bag of words approach. QuickConnect outperforms alternative methods and features a convenient tradeoff between accuracy and speed.

This paper surveys recent results in pursuit-evasion and autonomous search relevant to applications in mobile robotics. We provide a taxonomy of search problems that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment. We then list a number of fundamental results in the areas of pursuit-evasion and probabilistic search, and we discuss field implementations on mobile robotic systems. In addition, we highlight current open problems in the area and explore avenues for future work.

We present a new algorithm for merging occupancy grid maps produced by multiple robots exploring the same environment. The algorithm produces a set of possible transformations needed to merge two maps, i.e translations and rotations. Each transformation is weighted, thus allowing to distinguish uncertain situations, and enabling to track multiple cases when ambiguities arise. Transformations are produced extracting some spectral information from the maps. The approach is deterministic, non-iterative, and fast. The algorithm has been tested on public available datasets, as well as on maps produced by two robots concurrently exploring both indoor and outdoor environments. Throughout the experimental validation stage the technique we propose consistently merged maps exhibiting very different characteristics.

This paper describes an ongoing project to investigate the uses of pheromones as a means of communication in robotics. The particular example of pheromone communication considered here was inspired by queen bee pheromones that have a number of crucial functions in a bee colony, such as keeping together and stabilizing the colony. In the context of a robotic system, one of the proposed applications for robot pheromones is to allow a group of robots to be guided by a robot leader. The robot leader could release different chemicals to elicit a range of behaviors from other members of the group. A change of the operating temperature of tin oxide gas sensors has been implemented in order to differentiate different chemicals. This paper provides details of the robots used in the project and their behaviors. The sensors, especially the method of using the tin oxide gas sensors, the robot control algorithms and experimental results are presented. In this project, pheromones were used to trigger congregating behavior and light seeking in a group of robots.

This paper introduces a new robotic smart house, Intelligent Sweet Home, developed at KAIST in Korea, which is based on several robotic agents and aims at testing advanced concepts for independent living of the elderly and people with disabilities. The work focuses on technical solutions for human-friendly assistance in motion/mobility and advanced human-machine interfaces that provide simple control of all assistive robotic systems and home-installed appliances. The smart house concept includes an intelligent bed, intelligent wheelchair, and robotic hoist for effortless transfer of the user between bed and wheelchair. The design solutions comply with most of the users' requirements and suggestions collected by a special questionnaire survey of people with disabilities. The smart house responds to the user's commands as well as to the recognized intentions of the user. Various interfaces, based on hand gestures, voice, body movement, and posture, have been studied and tested. The paper describes the overall system structure and explains the design and functionality of some main system components.

In this paper, we explore the integration of an electronic nose and its odour discrimination functionalities into a multi-sensing robotic system which works over an extended period of time. The robot patrols an office environment, collecting odour samples of objects and performing user requested tasks. By considering an experimental platforms which operates over an extended period of time, a number of issues related to odour discrimination arise such as the drift in the sensor data, online learning of new odours, and the correct association of odour properties related to objects. In addition to an electronic nose our robotic system consists of other sensing modalities (vision and sonar), behaviour-based control and a high level symbolic planner.

Coordination in multi-robot systems is a very active research field in Artificial Intelligence and Robotics, since through coordination one can achieve a more effective execution of the robots' tasks. In this paper we present an approach to distributed coordination of a multi-robot system that is based on dynamic role assignment. The approach relies on the broadcast communication of utility functions that define the capability for every robot to perform a task and on the execution of a coordination protocol for dynamic role assignment. The presented method is robust to communication failures and suitable for application in dynamic environments. In addition to experimental results showing the effectiveness of our approach, the method has been successfully implemented within the team of heterogeneous robots Azzurra Robot Team in a very dynamic hostile environment provided by the RoboCup robotic soccer competitions.

The paper briefly outlines DLR's experience with real space robot missions (ROTEX and ETS VII). It then discusses forthcoming projects, e.g., free-flying systems in low or geostationary orbit and robot systems around the space station ISS, where the telerobotic system MARCO might represent a common baseline. Finally it describes our efforts in developing a new generation of “mechatronic” ultra-light weight arms with multifingered hands. The third arm generation is operable now (approaching present-day technical limits). In a similar way DLR's four-fingered hand II was a big step towards higher reliability and yet better performance. Artificial robonauts for space are a central goal now for the Europeans as well as for NASA, and the first verification tests of DLR's joint components are supposed to fly already end of 93 on the space station.

Biological collective control architectures and simple control principles used in nervous systems provide novel alternative approaches for the design of fault-tolerant, adaptable real-world robotic systems that have traditionally relied on centralized control. In this research, a robotic arm composed of multiple identical segments in a collective computational architecture was tested for its ability to produce adaptive pointing and reaching behavior. The movement rules for these robotic arm segments were derived from “reflex arc” principles in the human nervous system. These arm segments received no central directions and used no direct informational exchange, but rather the arm was sensor-driven at its leading segment in a way that maximized pointing accuracy of the arm. The remaining non-leading segments in the arm were moved in a sequential order using only sensed locally-available movement information about neighboring segments. Pointing and reaching behavior was observed in experiments with and without obstacles to movement. Because such behavior was not specified within each segment, the overall limb behavior emerged due to the interaction and coordination of all segments, rather than due to any single segment, centrally controlled influence, or explicit inter-segmental method of communication.

We discuss a robotic system composed of i>Crystalline modules. Crystalline modules can aggregate together to form distributed robot systems. Crystalline modules can move relative to each other by expanding and contracting. This actuation mechanism permits automated shape metamorphosis. We describe the Crystalline module concept and show the basic motions that enable a Crystalline robot system to self-reconfigure. We present an algorithm for general self-reconfiguration and describe simulation experiments.

Robots are needed to perform important field tasks such as hazardous material clean-up, nuclear site inspection, and space exploration. Unfortunately their use is not widespread due to their long development times and high costs. To make them practical, a modular design approach is proposed. Prefabricated modules are rapidly assembled to give a low-cost system for a specific task. This paper described the modular design problem for field robots and the application of a hierarchical selection process to solve this problem. Theoretical analysis and an example case study are presented. The theoretical analysis of the modular design problem revealed the large size of the search space. It showed the advantages of approaching the design on various levels. The hierarchical selection process applies physical rules to reduce the search space to a computationally feasible size and a genetic algorithm performs the final search in a greatly reduced space. This process is based on the observation that simple physically based rules can eliminate large sections of the design space to greatly simplify the search. The design process is applied to a duct inspection task. Five candidate robots were developed. Two of these robots are evaluated using detailed physical simulation. It is shown that the more obvious solution is not able to complete the task, while the non-obvious asymmetric design develop by the process is successful.

This volume is one of two special journal issues compiled from the best papers presented at the fourth Robotics: Science and Systems (RSS) conference, held in Zurich in June 2008. As indicated by its title, RSS is a meeting that values research papers focusing on the development of, and experimentation with, new complex robotic systems in real-world environments as much as it values new theoretical and algorithmic advances. The second special issue will be published by the International Journal of Robotics Research. The Zurich meeting of RSS received 163 submitted papers. After a rigorous review process, 40 papers were accepted covering a wide range of topics, from autonomous driving, to micro-robotics, to design of manipulators for use in functional magnetic resonance imaging, to grasping and manipulation. Following the tradition of RSS, there were a number of invited speakers who are global leaders in research areas on the frontiers of robotics. As an indication of the quality and inspirational value of the program, RSS Zurich was well attended, with nearly six times as many attendees as papers. Papers selected for a special issue were selected first, and then partitioned into two groups for the two special issues. The papers selected for this special issue of Autonomous Robots have a common theme close to the focus of this journal: the study of problems related to mobile robots or

We describe a robot system that can navigate in indoor environments, such as office buildings and laboratories, without having a detailed map of its environment and which can accept symbolic commands such as “go through the door on the left of the first desk on your right” (expressed in a formal language). Such a system can operate in different instances of similar environments and does not require the effort of constructing a detailed map of the environment. It is also not sensitive to changes in the environment such as those caused by moving furniture. It uses generic representations of the objects in the environment such as walls, desks and doors to recognize them for the purposes of landmark detection and avoids obstacles which may not be modeled explicitly.

Multi-agent robotic systems are useful in many practical applications. For some tasks, such as holding a conference, {\it cooperation} among agents are necessary. For other tasks, such as cleaning a room, multiple agents can work in parallel for better performance. This paper provides help-based (HCP) and coordination-based (CCP) protocols for controlling agents to accomplish multi-agent tasks. The HCP utilizes efficient negotiation to coordinate agents into groups. The CCP improves overall performance by exchanging local knowledge among agents and making decisions in parallel. A reactive and modularized agent architecture was employed to implement the protocols. Since each protocol is embedded into the architecture, it is efficient and effective. In addition, the protocols are deadlock-free. The protocols were utilized to solve the Object-Sorting Task, which abstracts two characteristics of tasks: parallelism and cooperation. The experimental results showed that 1) both HCP and CCP are stable under different workload; 2) the protocols can effectively utilize the agent-power to achieve super-linear improvement; 3) The CCP is better than the HCP in both performance and speedup.

Sending mobile robots to accomplish planet exploration missions is scientifically promising and technologically challenging. We present in this paper a complete approach that encompasses the major aspects involved in the design of a robotic system for planetary exploration. It includes mission teleprogramming and supervision at a ground station, and autonomous mission execution by the remote mobile robot. We have partially implemented and validated these concepts. Experimental results illustrate the approach and the results.

Multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. Communication between the robots can multiply their capabilities and effectiveness, but to what extent? In this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. Performance was measured for three different types of communication for three different tasks. The levels of communication are progressively more complex and potentially more expensive to implement. For some tasks, communication can significantly improve performance, but for others inter-agent communication is apparently unnecessary. In cases where communication helps, the lowest level of communication is almost as effective as the more complex type. The bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. The simulation results help determine appropriate parameters for the reactive control system which was ported for tests on Denning mobile robots.

The capability of autonomously discovering relations between perceptual data and motor actions is crucial for the development of robust adaptive robotic systems intended to operate in an unknown environment. In the case of robotic tactile perception, a proper interaction between contact sensing and motor control is the basic step toward the execution of complex motor procedures such as grasping and manipulation. In this paper the autonomous development of cutaneo-motor coordination is investigated in the case of a robotic finger mounted on a robotic manipulator, for a particular class of micromovements. A neural network architecture linking changes in the sensed tactile pattern with the motor actions performed is described and experimental results are analyzed. Examples of application of the developed sensory-motor coordination in the generation of motor control procedures for the estimate of surface curvature are considered.

This special issue of Autonomous Robots is based on presentations given at the NASA workshop on Biomorphic Robotics1 hosted in August 2000 by the Jet Propulsion Laboratory at the California Institute of Technology. The purpose of the workshop was to bring together a representative sample of work in biomorphic robotics likely to have an impact in future NASA missions. Biomorphic robotics can be broadly defined as the transfer of biological principles to robotics and the use of robots to evaluate and test computational models in biology. Robotics has historically been grounded in control theory, generally meant for the precise control of machines in well-defined, predictable environments. In contrast, biological systems are uniquely competent at interactions with unpredictable and dynamic environments. Thus, a primary goal of biomorphic robotics is to imbue robotic systems with the capabilities of biological organisms, to successfully maneuver within and explore unpredictable environments. This body of work is of great interest to NASA and the Jet Propulsion Laboratory. In particular, the surfaces of other planets are not well characterized, are likely to be environmentally hostile, and will demand a high degree of autonomy. Robots for space exploration are thus faced with unique challenges, and a biomorphic approach may be particularly appropriate.

To support NASA’s new missions for sustainable and affordable space exploration, the Robosphere 2002 and 2004 workshops were held at NASA Ames Research Center. The aim of the workshops was to explore the notion of self-sustaining robotic systems as a means of achieving increased scientific returns, decreased exploration costs and reduced chances of mission failure. The fundamental propositions of both workshops were that (a) self-sustaining systems need to provide not only for agent coordination, but also for the exchange of matter (parts) and energy, that (b) increased complexity need not come at the cost of decreased stability or survivability, and that (c) InSitu Resource Utilization will need to play a key role. Nature provides examples of self-sustaining systems -ecologieswhere interactions among components (organisms) involve information, matter, and energy exchanges; and where complexity confers stability. Natural ecologies are thus a source of inspiration for the types of systems we need to build. On the other hand, many valid approaches to self-sustaining human/robotic systems are also inspired by non-biological principles. The 2004 workshop extended those interests to also encompass explicit consideration of human presence and coordination with self-sustaining robotic systems. The papers published in this special issue were based

A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multi-agent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.

We present a framework for robotic cognitive control endowed with adaptive mechanisms for attentional regulation and task execution. In cognitive psychology, cognitive control is the process that orchestrates executive and cognitive processes supporting adaptive responses and complex goal-directed behaviors. Similar mechanisms can be deployed in robotic systems in order to flexibly execute complex structured tasks. In this work, following a supervisory attentional system paradigm, we propose an approach that permits to learn how to exploit top-down and bottom-up attentional regulations to guide the execution of hierarchically structured tasks. We present the overall framework discussing its functioning in a mobile robot case study considering pick-carry-place tasks. In this setting, we show that the proposed system can be on-line trained by a user in order to execute incrementally complex activities.