Christopher M. Clark

The Malta Cistern Mapping Project: Underwater Robot Mapping and Localization within Ancient Tunnel Systems

Cory White et al. — Wed, 27 Apr 2011 11:48:45 PDT

This paper documents the development of an underwater robot system enabled with several mapping and localization techniques applied to a particular archaeological expedition. The goal of the expedition was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. Such cisterns often consisted of several connected chambers, still containing water. A sonar-equipped remotely operated vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Six different mapping and localization techniques were employed, including (1) sonar image mosaics using stationary sonar scans, (2) sonar image mosaics using stationary sonar scans with Smart Tether position data, (3) simultaneous localization and mapping (SLAM) while the vehicle was in motion, (4) SLAM using stationary sonar scans, (5) localization using previously created maps, and (6) SLAM while the vehicle was in motion with Smart Tether position data. Top-down-view maps of 22 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C., and few records of their size, shape, and connectivity existed before the expedition.

Enhanced Vehicle Identification Utilizing Sensor Fusion and Statistical Algorithms

Stephane Roussel et al. — Fri, 11 Feb 2011 10:27:39 PST

Several studies in the area of vehicle detection and identification involve the use of probabilistic analysis and sensor fusion. While several sensors utilized for identifying vehicle presence and proximity have been researched, their effectiveness in identifying vehicle types has remained inadequate. This study presents the utilization of an ultrasonic sensor coupled with a magnetic sensor and the development of statistical algorithms to overcome this limitation.

Mathematical models of both the ultrasonic and magnetic sensors were constructed to first understand the intrinsic characteristics of the individual sensors and also to provide a means of simulating the performance of the combined sensor system and to facilitate algorithm development. Preliminary algorithms that utilized this sensor fusion were developed to make inferences relating to vehicle proximity as well as type. It was noticed that while it helped alleviate the limitations of the individual sensors, the algorithm was affected by high occurrences of false positives. Also, since sensors carry only partial information about the surrounding environment and their measured quantities are partially corrupted with noise, probabilistic techniques were employed to extend the preliminary algorithms to include these sensor characteristics. These statistical techniques were utilized to reconstruct partial state information provided by the sensors and to also filter noisy measurement data. This probabilistic approach helped to effectively utilize the advantages of sensor fusion to further enhance the reliability of inferences made on vehicle identification.

In summary, the study investigated the enhancement of vehicle identification through the use of sensor fusion and statistical techniques. The algorithms developed showed encouraging results in alleviating the occurrences of false positive inferences. One of the several applications of this study is in the use of ultrasonic-magnetic sensor combination for advanced traffic monitoring such as smart toll booths.

Multi-Robot Boundary Tracking with Phase and Workload Balancing

Michael Boardman et al. — Wed, 09 Feb 2011 09:17:21 PST

This paper discusses the use of a cooperative multiple robot system as applied to distributed tracking and sampling of a boundary edge. Within this system the boundary edge is partitioned into subsegments, each allocated to a particular robot such that workload is balanced across the robots. Also, to minimize the time between sampling local areas of the boundary edge, it may be desirable to minimize the difference between each robots progression (i.e. phase) along its allocated sub segment of the edge. The paper introduces a new distributed controller that handles both workload and phase balancing. Simulation results are used to illustrate the effectiveness of the controller in an Autonomous Underwater Vehicle (AUV) under ice edge sampling application. Successful results from experimentation with three iRobot Creates are also presented.

Dynamics of Step-Climing with Deformable Wheels and Applications for Mobile Robotics

Alexander Wilhelm et al. — Wed, 14 Jul 2010 15:56:52 PDT

Wheeled-mobile robots operating in human environments typically encounter small steps. Surmounting steps is normally not considered when determining peak torque needs, yet it can be the maximum requirement. This work looks at the statics and dynamics of this situation to determine the necessary peak torque. It finds that using a dynamic model that includes the wheel elasticity is essential for properly representing a real-world tire. When torque is increased using a step function, energy is stored in the tire-higher tire elasticity eases climbing. Knowledge of this phenomenon could facilitate the use of smaller actuators. The model is numerically integrated and results are found to agree with experiment.

Development of a systematic and practical methodology for the design of vehicles semi-active suspension control system

Hamidreza Bolandhemmat et al. — Wed, 14 Jul 2010 15:56:51 PDT

In this paper, a novel systematic and practical methodology is presented for design of vehicle semi-active suspension systems. Typically, the semi-active control strategies developed to improve vehicle ride comfort and stability have a switching nature. This makes the design of the controlled suspension systems difficult and highly dependent on an extensive trial-and-error process. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time and frequency design techniques, established in the conventional control system theory, can be applied. If the semi-active control system is designed to satisfy some ride and stability requirements, an inverse mapping offers the ultimate control law. At the end, the entire design procedure is summarised in six steps. The effectiveness of the proposed methodology in the design of a semi-active suspension system for a Cadillac SRX 2005 is demonstrated with road tests results. Real-time experiments confirm that the use of the newly developed systematic design method reduces the required time and effort in real industrial problems.

Altruistic Task Allocation despite Unbalanced Relationships within Multi-Robot Communities

Ryan Morton et al. — Wed, 27 Jan 2010 09:31:33 PST

Typical Multi-Robot Systems consist of robots cooperating to maximize global fitness functions. However, in some scenarios, the set of interacting robots may not share common goals and thus the concept of a global fitness function becomes invalid. This work examines Multi-Robot Communities (MRC), in which individual robots have independent goals. Within the MRC context, we present a task allocation architecture that optimizes individual robot fitness functions over long time horizons using reciprocal altruism.

Previous work has shown that reciprocating altruistic relationships can evolve between two willing robots, using market-based task auctions, while still protecting against selfish robots aiming to exploit altruism. As these relationships grow, robots are increasingly likely to perform tasks for one another without any reward or promise of payback. This work furthers this notion by considering cases where an imbalance exists in the altruistic relationship. The imbalance occurs when one robot can perform another robot's task, thereby exhibiting altruism, but the other robot cannot reciprocate since it is physically unable (e.g. lack of adequate sensors or actuators). A new altruistic controller to deal with such imbalances is presented. The controller permits a robot to build altruistic relationships with the community as a whole (one-to-many), instead of just with single robots (one-to-one). The controller is proven stable and guarantees altruistic relationships will grow, if robots are willing, while still minimizing the effects of selfish robots. Results indicate that the one-to-many controller performs comparable to the one-to-one on most problems, but excels in the case of an unbalanced altruistic relationship.

Towards Gaussian Multi-Robot SLAM for Underwater Robotics

Dave Kroetsch et al. — Tue, 22 Sep 2009 11:23:09 PDT

This paper presents initial steps towards developing autonomous navigation capabilities for cooperating underwater robots. Specifically, Simultaneous Localization and Mapping, or SLAM, capabilities are investigated for a group of micro vehicles each equipped with a single downward facing camera and an Inertial Measurement Unit (IMU). To verify the approach, simulations of the multi-robot SLAM running in a 3D environment were conducted, where vehicles in close proximity of one another exchange maps to improve localization.

Autonomous Control for a Differential Thrust ROV

Wei Wang et al. — Tue, 22 Sep 2009 11:23:08 PDT

Smaller autonomous underwater vehicles that use differential thrust for surge and yaw motion control has the advantage of low cost and, at the same time, increased maneuverability in yaw direction. However, since such vehicles are underactuated vehicles, design of an autonomous control system that enables the vehicle to autonomously track a predefined trajectory is challenging.

In this paper, we presented such an autonomous control system and implemented it on a small underactuated ROV with the use of unscented Kalman filter for vehicle localization, a underwater acoustic positioning system as the position sensor and a compass as the direction sensor. In designing the control law, the integrator backstep technique is used to achieve Lyapunov stability. Computer simulation and field tests have shown that the autonomous control system works well for the vehicle to track a predefined trajectory and the the tracking error converged to a certain small value.

Robotic System Sensitivity to Neural Network Learning Rate: Theory, Simulation, and Experiments

Christopher M. Clark et al. — Fri, 21 Aug 2009 09:28:00 PDT

Selection of neural network learning rates to obtain satisfactory performance from neural network controllers is a challenging problem. To assist in the selection of learning rates, this paper investigates robotic system sensitivity to neural network (NN) learning rate. The work reported here consists of experimental and simulation results. A neural network controller module, developed for the purpose of experimental evaluation of neural network controller performance of a CRS Robotics Corporation A460 robot, allows testing of NN controllers using real-time iterative learning. The A460 is equipped with a joint position proportional, integral, and derivative (PID) controller. The neural network module supplies a signal to compensate for remaining errors in the PID-controlled system. A robot simulation, which models this PID-controlled A460 robot and NN controller, was also developed to allow the calculation of sensitivity to the NN learning rate. This paper describes the implementation of three NN architectures: the error back-propagation (EBP) NN, mixture of experts (ME) NN, and manipulator operations using value encoding (MOVE) NN. The sensitivity of joint trajectory error of three NN controllers to learning rate was investigated using both simulation and experimentation. Similar results were obtained from the robot experiments and the dynamic simulation. These results of state sensitivity to NN learning rate confirm that the MOVE NN is least sensitive to learning rate, implying that selection of suitable learning rates for this NN architecture for the system considered is accomplished more readily than other NN architectures.

Dynamic Networks for Motion Planning in Multi-Robot Space Systems

Christopher M. Clark et al. — Tue, 18 Aug 2009 12:42:55 PDT

A new motion planning framework is presented that enables multiple mobile robots with limited ranges of sensing and communication to maneuver and achieve goals safely in dynamic environments. The framework is applicable to both planetary rover and free-floating space robot applications. To combine the respective advantages of centralized and de-centralized planning, this framework is based on the concept of centralized planning within dynamic robot networks. As the robots move in their environment, localized robot groups form networks, within which world models and robot goals can be shared. Whenever a network is formed, new information becomes available to all robots in this network. With this new information, each robot uses a fast, centralized planner to compute new coordinated trajectories on the fly. Planning over multiple robot networks is decentralized and distributed. The applicability of the framework to planetary rovers is demonstrated in both simulations and real robot experiments. Also, the framework’s applicability to free-floating robots in a 3D space environment is demonstrated in simulation.

Randomized Motion Planning for Groups of Nonholonomic Robots

Christopher M. Clark et al. — Tue, 18 Aug 2009 12:42:54 PDT

This paper presents a technique for motion planning which is capable of planning trajectories for a large number of nonholonomic robots. The robots plan within a two dimensional environment that consists of stationary/moving obstacles, and fixed boundaries. Each robot uses randomized motion planner techniques based on Probabilistic Road Maps (PRM’s) to construct it’s own trajectory that is free of collisions with moving obstacles and other robots. The randomized motion planner allows easy integration of the robots nonholonomic constraint into the planning so that only kinematically consistent plans are constructed. It is important to include this constraint in the planning problem since the majority of planetary surface robots are nonholonomic. The speed of the road map construction allows planning in real-time, enabling the robot to maneuver safely in a dynamic environment. Communication between robots is infrequent since robots only communicate on a “need to know basis”. To verify the planner’s effectiveness, it was tested using both simulation and experiment.

Dynamic Robot Networks: A Coordination Platform for Multi-Robot Systems

Christopher M. Clark — Wed, 12 Aug 2009 17:08:18 PDT

A large number of tasks, from manufacturing to planetary exploration, have been successfully accomplished using single robot systems. Many of these tasks could be completed faster, more reliably, and on a larger scale using a cooperating team of autonomous mobile robots. However, robots must be able to coordinate their actions before cooperation is possible.

This work aims to enable robots with the ability to coordinate their actions for safe navigation in dynamic, unknown environments. Specifically, the work focuses on: 1) the coordination of multiple robots when sensing and inter-robot communication are limited and 2) multi-robot motion planning in dynamic, unknown environments.

First, a new coordination platform is introduced - Dynamic Robot Networks - that facilitates centralized robot coordination across ad hoc networks. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.

Second, a fast motion planner called within robot networks is presented. The planner is a probabilistic roadmap (PRM) motion planner augmented with new sampling strategies. These strategies decrease the planner's run time to enable on-the-fly planning - a key requirement for navigation in environments that are unknown a priori and contain moving obstacles.

Simulations and real robot experiments are presented that demonstrate: 1) centralized robot coordination across dynamic robot networks, 2) on-the-fly motion planning to avoid moving and previously unknown obstacles, and 3) autonomous robot navigation towards individual goal locations.

Altruistic Relationships for Optimizing Task Fulfillment in Robot Communities

Christopher M. Clark et al. — Wed, 12 Aug 2009 17:06:00 PDT

This paper introduces the concept of a multi-robot community in which multiple robots must fulfill their individual tasks while operating in a shared environment. Unlike typical multi-robot systems in which global cost functions are minimized while accomplishing a set of global tasks, the robots in this work have individual tasks to accomplish and individual cost functions to optimize (e.g. path length or number of objects to gather).

A strategy is presented in which a robot may choose to aid in the completion of another robot’s task. This type of “altruistic” action leads to evolving altruistic relationships between robots, and can ultimately result in a decrease in the individual cost functions of each robot. However, altruism with respect to another robot must be controlled such that it allows a relationship where both robots are altruistic, but protects an altruistic robot against a selfish robot that does not help others.

A quantitative description of this altruism is presented, along with a law for controlling an individuals altruism. With a linear model of the altruism dynamics, altruistic relationships are proven to grow when robots are altruistic, but protect an altruistic robot from a selfish robot. Results of task planning simulations are presented that highlight the decrease in individual robot cost functions, as well as evolutionary trends of altruism between robots.

Probabilistic Road Map Sampling Strategies for Multi-Robot Motion Planning

Christopher M. Clark — Wed, 12 Aug 2009 17:05:59 PDT

This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks—a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication.

First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.

Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations.

Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations.

A Decentralized Reinforcement Learning Controller For Collaborative Driving

Luke Ng et al. — Wed, 12 Aug 2009 17:05:58 PDT

Research in the collaborative driving domain strives to create control systems that coordinate the motion of multiple vehicles in order to navigate traffic both efficiently and safely. In this paper a novel individual vehicle controller based on reinforcement learning is introduced. This controller is capable of both lateral and longitudinal control while driving in a multi-vehicle platoon. The design and development of this controller is discussed in detail and simulation results showing learning progress and performance are presented.

Complete and Scalable Multi-Robot Planning in Tunnel Environments

Mike Peasgood et al. — Wed, 12 Aug 2009 17:05:58 PDT

This paper addresses the challenging problem of finding collision-free trajectories for many robots moving to individual goals within a common environment. Most popular algorithms for multi-robot planning manage the complexity of the problem by planning trajectories for robots sequentially; such decoupled methods may fail to find a solution even if one exists. In contrast, this paper describes a multi-phase approach to the planning problem that guarantees a solution by creating and maintaining obstacle-free paths through the environment as required for each robot to reach its goal. Using a topological graph and spanning tree representation of a tunnel or corridor environment, the multi-phase planner is capable of planning trajectories for up to r = L-1 robots, where the spanning tree includes L leaves. Monte Carlo simulations in a large environment with varying number of robots demonstrate that the algorithm can solve planning problems requiring complex coordination of many robots that cannot be solved with a decoupled approach, and is scalable with complexity linear in the number of robots.

Motion Planning for Formations of Mobile Robots

T. D. Barfoot et al. — Wed, 12 Aug 2009 17:05:57 PDT

This paper is concerned with planning the motion of mobile robots in formation, which means certain geometrical constraints are imposed on the relative positions and orientations of the robots throughout their travel. Specifically, a method of planning motion for formations of mobile robots with non-holonomic constraints is presented. The kinematic equations developed allow a certain class of formations to be maintained while the group as a whole exhibits motion. The work was validated using the Stanford Micro-Autonomous RoverS Testbed.

Development of a Microscopic Traffic Simulator for Inter-Vehicle Communication Application Research

Keith Yu Kit Leung et al. — Mon, 10 Aug 2009 16:00:24 PDT

This paper describes the development of a microscopic traffic simulator purposely designed for ITS researchers studying inter-vehicle communication (IVC) concepts and applications in large traffic networks. The simulator can represent real life vehicles within the simulation by using data from vehicle Global Positioning System (GPS) receivers, enabling validation of theories with real vehicle data. The software is developed on top of the existing microscopic traffic simulator VISSIM with the added flexibility of modelling and efficiently handling communication between large numbers of vehicles. This along with the software architecture was discussed in detail.

A Genetic Algorithm Approach to Solve for Multiple Solutions of Inverse Kinematics Using Adaptive Niching and Clustering

Saleh Tabandeh et al. — Mon, 10 Aug 2009 16:00:24 PDT

Inverse kinematics is a nonlinear problem that may have multiple solutions. A Genetic Algorithm(GA) for solving the inverse kinematics of a serial robotic manipulator is presented. The algorithm is capable of finding multiple solutions of the inverse kinematics through niching methods. Despite the fact that the number and position of solutions in the search space depends on the the position and orientation of the end-effector as well as the configuration of the robot, the number of GA parameters that must be set by a user are limited to a minimum through the use of an adaptive niching method. The only requirement of the algorithm is the forward kinematics equations which can be easily obtained from the link parameters and joint variables of the robot. For identifying and processing the outputs of this GA, a modified filtering and clustering phase is also added to the algorithm. The algorithm was tested to solve the inverse kinematic problem of a 3 degree-of-freedom(DOF) robotic manipulator.

Optimized Lane Assignment Using Inter-Vehicle Communication

Thanh-Son Dao et al. — Mon, 10 Aug 2009 16:00:23 PDT

This paper presents an approach to lane assignment for highway vehicles that increases traffic throughput while ensuring they exit successfully at their destinations. Most of current traffic management systems do not consider lane organization of vehicles and only regulate traffic flows by controlling traffic signals or ramp meters. However, traffic throughput and efficient use of highways can be increased by coordinating driver behaviors intelligently. The goal of this research is to form a distributed control strategy for cars themselves to select lanes using inter-vehicle communication. Initial results are promising and demonstrate that intelligent lane selection can decrease vehicle traffic time.