Riderless bicycles, which belong to the class of narrow autonomous vehicles, offer numerous potentials to improve living conditions in the smart cities of the future. Various obstacles exist in achieving full autonomy for this class of autonomous vehicles. One of these significant challenges lie within the synthesis of automatic control algorithms that provide self-balancing and maneuvering capabilities for this class of autonomous vehicles. Indeed, the nonlinear, underactuated, and non-minimum phase dynamics of riderless bicycles offer rich challenges for automatic control of these autonomous vehicles. In this paper, we report on implementing linear parameter varying (LPV)-based controllers for balancing our constructed autonomous bicycle, which is equipped with linear electric actuators for automatic steering, in the upright position. Experimental results demonstrate the effectiveness of the proposed control strategy.
Mobility and terrain are two sides of the same coin. We cannot speak to our mobility unless we describe the terrain’s ability to thwart our maneuver. Game theory describes the interactions of rational players who behave strategically. In previous work1 we described the interactions between a mobility player, who is trying to maximize the chances that he makes it from point A to point B with one chance to refuel, and a terrain player who is trying to minimize that probability by placing an obstacle somewhere along the path from A to B. This relates to the literature of games of incomplete information, and can be thought of as a more realistic model of this interaction. In this paper, we generalize the game of timing studied in the previous paper to include the possibility that both players have imperfect ability to detect his adversary.
Autonomous bicycles offer numerous potentials for smart city applications thanks in part to their light weight, safe autonomy, being optionally manned, and last-mile delivery. This paper describes the design of a self-stabilizing autonomous bicycle with electric linear actuators. The high-speed linear actuator is mounted between the seat and the handlebar of the autonomous bicycle, which provides the bicycle with high peak power and energy efficiency. Physical tests are carried out to verify automatic steering and speed regulation capabilities of the autonomous bicycle.
In previous work, a multi-layered neural network trust model, dubbed NeuroTrust, was introduced. This trust model was also implemented in an autonomous vehicles convoy simulation, in which speed and gap distance depended on trust. It has been shown that, in time, through on-line reinforcement learning, this trust model produces better results for significant performance metrics in the respective autonomous vehicle convoy when compared to a baseline trust algorithm. In this paper, the NeuroTrust model is expanded to leverage the experience of multiple decision-making agents. A trust aggregation method is proposed for NeuroTrust and is simulated for multiple autonomous vehicle convoy scenarios. It is shown that the NeuroTrust model tends to optimize faster by leveraging each agent’s experience.
Mobility and terrain are two sides of the same coin. I cannot speak to my mobility unless I describe the terrain's ability to thwart my maneuver. Game theory describes the interactions of rational players who behave strategically. In previous work we described the interactions between a mobility player, who is trying to maximize the chances that he makes it from point A to point B with one chance to refuel, and a terrain player who is trying to minimize that probability by placing an obstacle somewhere along the path from A to B. In this paper, we add the twist that the mobility player cannot use their resource until they detect the terrain player. This relates to the literature of games of incomplete information, and can be thought of as a more realistic model of this interaction. In this paper we generalize the game of timing studied in the previous paper to include the possibility that one of the players has imperfect ability to detect his adversary.
To lighten the load of dismounted infantry, research is being conducted into the use of “robotic mules” that can help carry equipment and supplies for small units as they conduct their operations. While autonomy and perception features of robotic systems are steadily improving, the way in which people interact with them is still fairly rudimentary. The operator control units (OCUs) for these robotic mules are typically hand-held gaming controllers for tele-operation, or worn ruggedized portable computers or tablets with point-and-click interfaces. These control devices add more weight to the operator, and often require them to hold the controllers in their hands and to spend considerable time looking down at the OCU screen to enter commands or to understand what the robot is doing. Furthermore, these interfaces often require specialized training to understand how to operate the OCUs. This paper describes research aimed at reducing the physical, cognitive, and training burdens that robotic systems place on operators above and beyond the their regular jobs as warfighters. We first present an analysis of relevant infantry communication to identify interaction requirements, and an analysis of technologies that might be used to support these interactions. We then describe a prototype heads-up, hands-free system for controlling robotic mules using a lightweight, worn interaction device that facilitates natural twoway interaction (including speech and gesture input) between the robotic mule and the user. We describe the challenges in building this system and some formative evaluations of the technology
In this paper we propose a trust algorithm, dubbed NeuroTrust, based on a multi-layered neural network. Previous work introduced trust as a performance estimation algorithm between team members in multi-agent systems, to allow for behavior optimization of the team. The trust model was developed based on an Acceptance Observation History (AOH) and confirmation and tolerance parameters to control trust growth and decay. Further work proposed certain improvements, in an autonomous vehicles convoy scenario, by considering agent diversity and a non-linear relationship between trust and vehicle control. In this work we show a further optimization using a deep recurrent neural network. This multi-layered neural network delivers trust as a probability function estimation with AOH as a sliding window batch input. The neural network is pre-trained using supervised learning, to emulate the previous trust model, as baseline. This pre-trained model is then exposed to future optimization using on-line reinforcement learning. The proposed trust model could be adaptable to a variety of systems, external conditions, and agent diversity. One application example where such a biologically-inspired trust model is suitable would be for soldier-machine teaming. Furthermore, particularly in the autonomous convoy scenario, we can account for the trust-control relationship nonlinearity in the trust domain, thus simplifying the control algorithm.
Mobility and terrain are two sides of the same coin. You cannot describe mobility unless you describe the terrain. For example, if my world is trench warfare, the tank may be the ideal vehicle. If my world is urban warfare, clearing buildings and such, the tank may not be an ideal vehicle, perhaps an anthropomorphic robot would be better. We seek a general framework for mobility that captures the relative value of different mobility strategies. Game theory is positively the right way to analyze the interactions of rational players who behave strategically. In this paper, we will describe the interactions between a mobility player, who is trying to make it from point A to point B with one chance to refuel, and a terrain player who is trying to minimize that probability by placing an obstacle somewhere along the path from A to B. In previous work , we used Monte Carlo methods to analyze this mobility game, and found optimal strategies for a discretized version of the game. Here we show the relationship of this game to a classic game of timing , and use solution methods from that literature to solve for optimal strategies in a continuous version of this mobility game.
We model the scenario between a robotic system and its operating environment as a
strategic game between two players. The problem will be formulated as a game of timing. We will
treat disturbances in a worst case scenario, i.e., as if they were placed by an opponent acting
optimally. Game theory is a formal way to analyze the interactions among a group of rational
players who behave strategically. We believe that behavior in the presence of disturbances using
games of timing will reduce to optimal control when the disturbance is suppressed. In this paper
we create a model of phase space similar to the dolichobrachistochrone problem. We discretize
phase space to a simple grid where Player P is trying to reach a goal as fast as possible, i.e., with
minimum cost. Player E is trying to maximize this cost. To do this, E has a limited number of
"chips" to distribute on the grid. How should E distribute his resources and how should P
navigate the grid? Rather than treating disturbances as a random occurrence, we seek to treat
them as an optimal strategy
In this project, we further developed and tested a "ZipperMast" for small robots and legacy manned vehicles. The
ZipperMast knits three coiled bands of spring steel together to form a rigid mast. As the mast is extended, it draws up a
cable connecting the host platform to the payload, typically antennas and sensors. Elevating the payload improves line of
sight, and thus improves radio communication and surveillance situation awareness. When the mast is retracted, the
interior cable slides into a horizontal tray. The ZipperMast is a scaleable design. We have made systems that elevate to
8 and 20 feet. The 8 foot ZipperMast collapses to less that 8 inches high and 8 inches wide. The 20 foot ZipperMast
collapses to less that 12 inches high and 18 inches wide. In this paper we report on tests of the mechanical properties of
the mast, specifically the strength and stiffness under quasi-static and impulsive loading. These properties are important
for specifying constraints on height as a function of speed and payload and on speed as a function of height and payload
in order to ensure that the mast will not fail in the event of sudden stop, as in the event of a collision.
Legged Robots have tremendous mobility, but they can also be very inefficient. These inefficiencies can
be due to suboptimal control schemes, among other things. If your goal is to get from point A to point B
in the least amount of time, your control scheme will be different from if your goal is to get there using
the least amount of energy. In this paper, we seek a balance between these extremes by looking at both
efficiency and speed. We model a walking robot as a rimless wheel, and, using Pontryagin's Maximum
Principle (PMP), we find an "on-off" control for the model, and describe the switching curve between
these control extremes.
Picture someone walking from left to right. During one step (intra-step) we treat them as
a simple pendulum. This model is called the rimless wheel in the literature. We analyze
this model intra-step using dynamic programming to find the optimum energy profile
based on time and energy cost. We then analyze the problem inter-step for the ideal
stepsize based on time cost alone, i.e., without foot collision (energy) losses.
Over the years, scientists and artists alike have imagined walking mechanisms that mimic the natural gait of humans and animals. Only recently have engineers begun to unravel the mystery of animal locomotion. Several walking robots have been built in the past few years . An ongoing research problem with these robots is their inefficiency. Whereas animal locomotion is quite efficient, our efforts to mimic it have not been, with a few notable exceptions . In this paper, we present a design for efficient legged locomotion, and we show the initial concept demonstration.
Unmanned ground vehicle (UGV) technology can be used in a number of ways to assist in counter-terrorism activities. In addition to the conventional uses of tele-operated robots for unexploded ordinance handling and disposal, water cannons and other crowd control devices, robots can also be employed for a host of terrorism deterrence and detection applications. In previous research USU developed a completely autonomous prototype robot for performing under- vehicle inspections in parking areas (ODIS). Testing of this prototype and discussions with the user community indicated that neither the technology nor the users are ready for complete autonomy. In this paper we present a robotic system based on ODIS that balances the users' desire/need for tele- operation with a limited level of autonomy that enhances the performance of the robot. The system can be used by both civilian law enforcement and military police to replace the traditional mirror on a stick system of looking under cars for bombs and contraband.
The U.S. Army Tank-Automotive Research, Development, and Engineering Center (TARDEC) recently opened a 5000 square foot robotics laboratory known as the TARDEC Robotics Laboratory. The focus of the lab is on robotics research, both basic and applied, in the area of robot mobility. Mobility is the key problem for light weight robotic systems, and the TARDEC Robotics Lab will develop innovative ways to deal with the mobility issues. The lab will also test and evaluate robotic systems in all aspects of mobility and control. The lab has the highest concentration of senior researchers at TARDEC, and is committed to maintaining in- house research talent so that new combat concepts using robots can be evaluated effectively by the Army. This paper serves as an introduction to the lab, its missions, goals, capabilities and programs.
Teleoperation is important to the Army because of its interest in incorporating robotics in the battlefield. The objective of this research is to demonstrate the capability to drive multiple vehicles using only a single driver. Teleoperation in an important near term goal, and we hope that his research will further this goal.
US Army Tank-Automotive Command researchers are in the early stages of developing an autostereoscopic, 3D holographic visual display system. The present system uses holographic optics, low and high-resolution optics, low and high- resolution projectors, and computer workstation graphics to achieve real-time, 3D user-interactivity. This system is being used to conduct 3D visual perception studies for the purpose of understanding the effects of 3D in military target visual detection and as an alternative technique to CAD model visualization. The authors describe the present system configuration, operation, some of the technical limitations encountered during the system development, and the result of a human perception test that compared subject response times, hit rates and miss rates of visual detection when subjects used conventional 2D methods versus the 3D holographic image produced by the holographic display system. The results of this study revealed that 3D HOE system increased the perception of accuracy of moving vehicles. This research has provided some insights into which technology will be the best for presenting 3D simulated objects to subjects or designers in the laboratory.