This paper discusses the possibilities of extending and adapting the CHOMP motion planner [1] to work with a non-holonomic vehicle such as an autonomous truck with a single trailer. A detailed study has been done to find out the different ways of implementing these constraints on the motion planner. CHOMP, which is a successful motion planner for articulated robots produces very fast and collision-free trajectories. This nature is important for a local path adaptor in a multi-vehicle path planning for resolving path-conflicts in a very fast manner and hence, CHOMP was adapted. Secondly, this paper also details the experimental integration of the modified CHOMP with the sensor fusion and control system of an autonomous Volvo FH-16 truck and a set of experiments conducted on a real-time environment. Finally, additional simulations were also conducted to compare the performance of the different approaches developed to study the feasibility of employing CHOMP to autonomous vehicles.

This study explores the effects of minor changes in automation level on drivers' engagement in secondary activities. Three levels of automation were tested: manual, semi-autonomous, and fully-autonomous. Potential distractor items were present and participants were instructed they could use them if they felt it was safe. Hand positions and engagement in secondary activities were manually coded. Participants were significantly less likely to engage in a secondary activity in semi-autonomous than fully-autonomous mode. Likewise, they were significantly less likely to use two hands to interact with a secondary activity in semi-autonomous mode than fully-autonomous mode. Gaze classification for each of the driver roles revealed that increasing levels of automation resulted in an increasing percentage of off-road glance durations. These observations suggest that in the event of automation failures, a driver in semi-autonomous driving may be in a somewhat better position to retake control and avoid collisions than during fully autonomous driving.

This paper presents an optimal global planner for autonomous tracked vehicles navigating in off-road terrain with uncertain slip, which affects the vehicle as a process noise. This paper incorporates two fields of study: slip estimation and motion planning. For slip estimation, an experimental result from [9] is used to model the effect of the slip on the vehicle in various soil types. For motion planning, a robust incremental sampling based motion planning algorithm (CC-RRT*) is combined with the LQG-MP algorithm. CC-RRT* yields the optimal and probabilistically feasible trajectory by using a chance constrained approach under the RRT* framework. LQG-MP provides the capability of considering the role of compensator in the motion planning phase and bounds the degree of uncertainty to appropriate size. In simulation, the planner successfully finds the optimal and robust solution. In addition, the planner is compared with an RRT* algorithm with dilated obstacles to show that it avoids being overly conservative.

This paper presents a robust motion planning methodology for autonomous tracked vehicles navigating in a rough and unknown environment. Two fields of study are dealt with in this paper: motion planning and slip estimation. For the motion planner, the CC-RRT* algorithm is combined with LQG-MP. The motion planner uses a chance-constrained approach and considers the role of compensator in the planning step to provide a robust yet non-conservative planner. For the slip estimator, a stable yet practical online approach known as IPEM is used. IPEM compares the integrated prediction with the measurement to calculate appropriate parameters. The methodology performs online slip estimation and re-planning iteratively. This guarantees the safe travel of the vehicle even when there is an unexpected terrain change that can be fatal. The simulation result shows that the iterative estimation and re-planning plays a significant role in ensuring the safety of the vehicle.

This paper proposes a method for safe navigation based on representative sample inputs. The representative inputs are chosen in safe input sets based on their distance from forbidden input sets. The inputs are not only the safest decisions with respect to various unmodeled sources of uncertainties, but are also representatives of groups of nearby input sets resulting in similar maneuvers. This approach provides an obstacle avoidance strategy for the maximum control margins. For computational efficiency, a sampling-based approach is adopted, and its performance in terms of solution quality and computation time is analyzed. The algorithm has been successfully demonstrated with an example of a car-like robot in fields with obstacles. For the multiple-step horizon problem, a best-first search algorithm is proposed with guarantee of optimality, and its computational efficiency is demonstrated.

This paper proposes an optimal trajectory generation framework in which the global obstacle-avoidance problem is decomposed into simpler subproblems, corresponding to distinct path homotopies. In classical approaches to homotopic trajectory planning, trajectory planning and homotopy identification are performed simultaneously, leading to a substantial computational burden. The main benefit of the proposed approach is the development of a method to enumerate and explicitly represent distinct homotopy classes before trajectory planning or optimization, which allow the problem to be decomposed into simpler independent subproblems. The main contribution of the paper is twofold. The first contribution is the description of a method for utilizing existing cell-decomposition methods to enumerate and represent local trajectory generation problems that can be solved efficiently and independently. In addition, a relationship between the proposed cell-sequence representation and homotopy classes is analyzed. The second contribution is a computationally efficient novel formulation of the trajectory optimization problem within a cell sequence via mixed-integer quadratic programming (MIQP). Computational efficiency and increased solution richness of the proposed approach are demonstrated through simulation studies. The proposed MIQP formulation fits into a linear model-predictive control framework with nonconvex collision-free constraints.

The paper describes the system developed by researchers from MIT for the Defense Advanced Research Projects Agency's (DARPA) Virtual Robotics Challenge (VRC), held in June 2013. The VRC was the first competition in the DARPA Robotics Challenge (DRC), a program that aims to “develop ground robotic capabilities to execute complex tasks in dangerous, degraded, human-engineered environments”. The VRC required teams to guide a model of Boston Dynamics' humanoid robot, Atlas, through driving, walking, and manipulation tasks in simulation. Team MIT's user interface, the Viewer, provided the operator with a unified representation of all available information. A 3D rendering of the robot depicted its most recently estimated body state with respect to the surrounding environment, represented by point clouds and texture-mapped meshes as sensed by on-board LIDAR and fused over time.

In this paper we present a new approach to the threat assessment problem for semi-autonomous and fully autonomous vehicles, based on the estimation of the control freedom afforded to a vehicle. Given sensor information available about the surrounding environment, an algorithm is described for identifying fields of safe travel through which the vehicle can safely navigate. Within each candidate field, we then characterize the level of threat, to influence autonomous navigation or driver support inputs. To characterize threat, the fields of safe travel are associated with sets of feasible trajectories generated from a lattice sampled in the vehicle's input space. A planner then computes a metric associated with available control freedom from these sampled trajectories. This method potentially allows a semi-autonomous control system to honor safe driver inputs while ensuring safe and robust navigation properties. It could also serve as an input to an autonomous decision-making layer.

This paper describes and experimentally demonstrates a new approach to shared-adaptive control of human-machine systems. Motivated by observed human proclivity toward fields of safe travel rather than specific trajectories, our approach is rooted in the planning and enforcement of constraints rather than the more traditional reference paths. This approach identifies path homotopies, bounds a desired homotopy with constraints, and allocates control as necessary to ensure that these constraints remain satisfied without unduly restricting the human operator. We present a summary of this framework's technical background and analyze its effect both with and without driver feedback on the performance and confidence of 20 different drivers teleoperating an unmanned (teleoperated) vehicle through an outdoor obstacle course. In 1200 trials, constraint-based semiautonomy was shown to increase the operator speed by 26% while reducing the occurrence of collisions by 78%, and improving overall user confidence and sense of control by 44% and 12%, respectively-all the while assuming less than 43% control of the vehicle.

In robotic single-port surgery, it is desirable for a manipulator to exhibit the property of variable stiffness. Small-port incisions may require both high flexibility of the manipulator for safety purposes, as well as high structural stiffness for operational precision and high payload capability. This paper presents a new hyperredundant tubular manipulator with a variable neutral-line mechanisms and adjustable stiffness. A unique asymmetric arrangement of the tendons and the links realizes both articulation of the manipulator and continuous stiffness modulation. This asymmetric motion of the manipulator is compensated by a novel actuation mechanism without affecting its structural stiffness. The paper describes the basic mechanics of the variable neutral-line manipulator, and its stiffness characteristics. Simulation and experimental results verify the performance of the proposed mechanism.

This paper addresses the problem of ensuring mobile robot motion safety when reacting to soft and hard hazards in a static environment. The work is aimed at off-road navigation for mobile ground robots where soft hazards are posed by varying terrain conditions (e.g. deformable soil, slopes, vegetation). Soft hazards pose operating constraints (i.e. speed limits) to the mobile robot that need to be satisfied to ensure motion safety. This paper presents a new morphological erosion operator that generalizes binary obstacle growing to mobility space (the space of speed limits) to deal with both hard and soft hazards seamlessly. This ensures that topological constraints due to vehicle size as well as momentum are taken into account, and leads to a straight-forward approach to generalize the concept of `regions of inevitable collision' for soft hazards.

We present a vision based method for the autonomous geolocation of ground vehicles and unmanned mobile robots in forested environments. The method provides an estimate of the global horizontal position of a vehicle strictly based on finding a geometric match between a map of observed tree stems, scanned in 3D by sensors onboard the vehicle, to another stem map generated from the structure of tree crowns observed in overhead imagery of the forest canopy. This method can be used in real-time as a complement to the Global Positioning System (GPS) in areas where signal coverage is inadequate due to attenuation by the forest canopy, or due to intentional denied access. The method presented in this paper has two key properties that are significant: i) It does not require a priori knowledge of the area surrounding the robot. ii) It uses the geometry of detected tree stems as the only input to determine horizontal geoposition.

In order to grasp and hold uneven or dusty objects, a robust adhesion mechanism is required. This paper evaluates controllable adhesion using magneto-rheological fluid (MRF) as a technique to stick to non-magnetic materials and rough/dusty surfaces. This technique is both simple to use and robust to uncertain surface conditions, as it involves applying MRF on a surface and activating it with a magnetic field. In this paper, we experimentally evaluate yield stresses in both normal and shear directions with respect to MRF layer thickness, magnetic flux density and surface type. Based on these results, a four-legged climbing robot is designed to demonstrate scaling vertical walls and shows effectiveness of the controllable adhesion using MRF for rough surface.

This paper presents a novel “layer jamming” mechanism that can achieve variable stiffness. The layer jamming mechanism exploits the friction present between layers of thin material, which can be controlled by a confining pressure. Due to the mechanism's hollow geometry, compact size, and light weight, it is well suited for various minimally invasive surgery applications, where stiffness change is required. This paper describes the concept, the mathematical model, and a tubular snake-like manipulator prototype. Various characteristics of layer jamming, such as stiffness and yield strength, are studied both theoretically and experimentally.

Hyper-redundant manipulators can be fragile, expensive, and limited in their flexibility due to the distributed and bulky actuators that are typically used to achieve the precision and degrees of freedom (DOFs) required. Here, a manipulator is proposed that is robust, high-force, low-cost, and highly articulated without employing traditional actuators mounted at the manipulator joints. Rather, local tunable stiffness is coupled with off-board spooler motors and tension cables to achieve complex manipulator configurations. Tunable stiffness is achieved by reversible jamming of granular media, which—by applying a vacuum to enclosed grains—causes the grains to transition between solid-like states and liquid-like ones. Experimental studies were conducted to identify grains with high strength-to-weight performance. A prototype of the manipulator is presented with performance analysis, with emphasis on speed, strength, and articulation. This novel design for a manipulator—and use of jamming for robotic applications in general—could greatly benefit applications such as human-safe robotics and systems in which robots need to exhibit high flexibility to conform to their environments.

In this paper, a new systematical method for extracting tree trunk landmarks from 3D point clouds of cluttered forested environments is proposed. This purely geometric method is established on scene understanding and automatic analysis of trees. The pipeline of our method includes three steps. First, the raw point clouds are segmented by utilizing the circular shape of trees, and segments are grouped into tree sections based on the principle of spatial proximity. Second, circles and axes are extracted from tree sections which are subject to loss of shape information. Third, by clustering and integrating the tree sections resulted from various space inconsistencies, straight tree trunk landmarks are finally formed for future localization. The experimental results from real forested environments are presented.

In this paper we present a generalization of reactive obstacle avoidance algorithms for mobile robots operating among soft hazards such as off-road slopes and deformable terrain. A new hazard avoidance scheme generalizes constraint based reactive algorithms [1], [2] from hard to soft hazards. Reactive controllers operate by directly parameterizing the closedloop dynamics of the system with respect to the environment the robot is operating in. Traditionally, reactive controllers are parameterized by weighting virtual attraction and repulsion forces from goals and obstacles [3], [4]. One pitfall of such parameterizations is sensitivity of the tuning parameters to the operating environment. A reactive controller tuned in one set of conditions is not applicable in another (e.g. a different density of obstacles). The algorithm presented in this paper has two key properties which are significant i) Parameterization is environment independent. ii) It can deal with non-binary environments that contain soft hazards.

This paper introduces a new mechanism for achieving tunable stiffness, named layer jamming, and presents a hollow snake-like manipulator having tunable stiffness capability. The layer jamming mechanism is composed of multiple layers of thin Mylar film, and makes use of amplified friction between the films by applying vacuum pressure. In contrast to other tunable stiffness technology, such as particle jamming or field-activated materials (MR or ER fluids), layer jamming occupies a small volume and thus can be applied to small, light weight manipulators. The snake-like manipulator presented here is composed only of Mylar film and wires, and has highly flexible and under-actuated properties without application of a vacuum; however, it becomes highly stiff when a vacuum is applied. In this paper the characteristics of layer jamming are explored: its stiffness and breaking strength are obtained both theoretically and experimentally, and a prototype manipulator is developed and experimentally characterized.

This paper presents a new feature based scan matching method for solving 6D localization problem in forested environments. The proposed registration process includes two steps. First, the largest group of approximately parallel tree trunk features is utilized to align successive scans along the five dimensions except z direction. Tree correspondences are established by matching point patterns which are abstracted from the position relationships of trees. The optimal 5D transformation is thus determined based on the axes of two key tree pairs which are selected by evaluating their ability of tree alignment. Second, we assign the ground points of two scans into a grid of cells, and minimize z-direction difference of points in shared cells. The experimental results on data collected in real forested environments have demonstrated the effectiveness of this method.

This paper presents a new approach to semi-autonomous vehicle hazard avoidance and stability control, based on the design and selective enforcement of constraints. This differs from traditional approaches that rely on the planning and tracking of paths. This emphasis on constraints facilitates “minimally-invasive” control for human-machine systems; instead of forcing a human operator to follow an automation-determined path, the constraint-based approach identifies safe homotopies, and allows the operator to navigate freely within them, introducing control action only as necessary to ensure that the vehicle does not violate safety constraints. The method evaluates candidate homotopies based on “restrictiveness”, rather than traditional measures of path goodness, and designs and enforces requisite constraints on the human's control commands to ensure that the vehicle never leaves the controllable subset of a desired homotopy. Identification of these homotopic classes in off-road environments is performed using geometric constructs. The goodness of competing homotopies and their associated constraints is then characterized using geometric heuristics. Finally, input limits satisfying homotopy and vehicle dynamic constraints are enforced using threat-based feedback mechanisms to ensure that the vehicle avoids collisions and instability while preserving the human operator's situational awareness and mental models. The methods developed in this work are shown in simulation and experimentally demonstrated in safe, high-speed teleoperation of an unmanned ground vehicle.

This paper describes a nonholonomic robotic wheeled vehicle ripple tentacle motion planning method, aiming to improve the vehicle's trajectory smoothness and avoid frequent weight parameters adjustment in different environments. In the regular tentacle motion planning algorithm, the planning result is selected among the drivable tentacles using a weighted sum cost function. Though the method is simple and easy to understand, it is difficult to adjust the weighted coefficients in different environments. To solve this problem, a geometrical ripple tentacles technique is used to choose a tentacle as a sub-optimal path. Compared with the regular tentacles algorithm, the proposed ripple tentacle algorithm can get a better performance in vehicle's trajectory smoothness with an acceptable runtime expense. And another two traits can also distinguish this method: (a) it can avoid weight parameter adjustment in different environments and varied vehicle's states, and (b) it can be used in both unknown environment and partly known environment with goal point and global reference path. In the totally unknown environment, it acts as a pure obstacle avoidance algorithm, and when there is a global path, it can follow the reference path and avoid hazards simultaneously.

In this paper an omnidirectional mobile robot that possesses high mobility in rough terrain is presented. The omnidirectional robot has four active split offset caster (ASOC) modules, enabling the robot to move in any planar direction. It also possesses passive suspension articulation, allowing the robot to conform to uneven terrain. The agility of the robot is experimentally evaluated in various configurations. In addition, an odometry method that mitigates position estimation error due to wheel slippage is proposed. A key aspect of the proposed method is to utilize sensory data of wheel velocity, and turning rate around each ASOC pivot shaft, along with kinematic constraints of the robot configuration. Experimental odometry tests with different maneuvers in rough terrain are presented that confirm the utility of the proposed method.

A trajectory tracking controller based on differential flatness is presented for a nonlinear bicycle model. This controller maps the bicycle dynamics into a point mass located at a center of oscillation with an additional degree of freedom of yaw dynamics. A state transformation is performed that reveals structure in the yaw dynamics resembling a Lie¿nard system. A candidate Lyapunov function inspired by this structure is used to assess the stability of the yaw dynamics while tracking straight-line trajectories and steady turns. The basin of attraction of the controller is limited by actuator constraints and the presence of unstable equilibrium points during turns with high lateral acceleration. The controller properties and the stability of yaw dynamics are demonstrated in simulation.

Soft robotic systems have applications in industrial, medical, and security applications. Many applications require these robots to be small and lightweight. One challenge in developing a soft robotic system is to drive multiple degrees-of-freedom (DOF) with few actuators, thereby reducing system size and weight. This paper presents the analysis and design of an inchworm-like mobile robot that consists of multiple, independent thermally activated joints but is driven by a single actuator. To realize control of this under-actuated system, a solder-based locking mechanism has been developed to selectively activate individual joints without requiring additional actuators. The design and performance analysis of a prototype mobile robot that is capable of inchworm-like translational and steering motion is described. The design of novel “feet” with anisotropic friction properties is also described.

Planetary surface exploration rovers must accurately and efficiently predict their mobility on natural, rough terrain. Most approaches to mobility prediction assume precise a priori knowledge of terrain physical parameters, however in practical scenarios knowledge of terrain parameters contains significant uncertainty. In this paper, a statistical method for mobility prediction that incorporates terrain uncertainty is presented. The proposed method consists of two techniques: a wheeled vehicle model for calculating vehicle dynamic motion and wheel-terrain interaction forces, and a stochastic response surface method (SRSM) for modeling of uncertainty. The proposed method generates a predicted motion path of the rover with confidence ellipses indicating the probable rover position due to uncertainty in terrain physical parameters. Rover orientations and wheel slippage are also predicted. The computational efficiency of SRSM as compared to conventional Monte Carlo methods is shown via numerical simulations. Experimental results of rover travel over sloped terrain in two different uncertain terrains are presented that confirms the utility of the proposed mobility prediction method.

To operate autonomously in forested environments, unmanned ground vehicles (UGVs) must be able to identify the load-bearing surface of the terrain (i.e. the ground). This paper presents a novel two-stage approach for identifying ground points from 3-D point clouds sensed using LIDAR. The first stage, a local height-based filter, discards most of the non-ground points. The second stage, based on a support vector machine (SVM) classifier, operates on a set of geometrically defined features to identify which of the remaining points belong to the ground. Experimental results from two forested environments demonstrate the effectiveness of this approach.

An analogy between the dynamics of a cart-pole system and vehicle rollover dynamics is used to derive a controller for tipping up and stabilizing a planar model of a passenger vehicle near rollover by controlling lateral tire friction forces. The controller is based on a previously published controller for stabilizing a cart-pole using partial feedback linearization and energy shaping. A necessary condition for tip-up is given based on the surface friction coefficient and the location of the vehicle center of gravity (c.g.). A multi-body vehicle model with suspension is presented in the form of the robotic manipulator equations. Simulation results are presented demonstrating the effect of friction and suspension properties on the tip-up problem.

Road detection is a crucial problem in the application of autonomous vehicle and on-road mobile robot. Most of the recent methods only achieve reliable results in some particular well-arranged environments. In this paper, we describe a road detection algorithm for front-view monocular camera using road probabilistic distribution model (RPDM) and online learning method. The primary contribution of this paper is that the combination of dynamical RPDM and Fuzzy Support Vector Machines (FSVMs) makes the algorithm being capable of self-supervised learning and optimized learning from the inheritance of previous result. The secondary contribution of this paper is that the proposed algorithm uses road geometrical assumption to extract assumption based misclassified points and retrains itself online which makes it easier to find potential misclassified points. Those points take an important role in online retraining the classifier which makes the algorithm adaptive to environment changing.

Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle.

In this video, a novel tracked mechanism for sideways motion is presented. The tracked mechanism is of circular cross-section and has active rolling axes at the center of the circles. Conventional tracked mechanisms can support massive loads, but cannot produce sideways motion. Additionally, previous crawler edges sink undesirably on soft ground, particularly when the vehicle body is subject to a sideways tilt. The proposed design solves these drawbacks by adopting a circular cross-section crawler. A prototype has been developed to illustrate the concept. Motion experiments confirm the novel properties of this mechanism: sideways motion and robustness against edge-sink. Motion experiments, with a test vehicle are also presented.

The ability of mobile robots to quickly and accurately analyze their dynamics is critical to their safety and efficient operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this must be considered in an analysis of robot motion. Here a Multi-Element generalized Polynomial Chaos (MEgPC) approach is presented that explicitly considers vehicle parameter uncertainty for long term estimation of robot dynamics. It is shown to be an improvement over the generalized Askey polynomial chaos framework as well as the standard Monte Carlo scheme, and can be used for efficient, accurate prediction of robot dynamics.

The ability of mobile robots to generate feasible trajectories online is an important requirement for their autonomous operation in unstructured environments. Many path generation techniques focus on generation of time- or distance-optimal paths while obeying dynamic constraints, and often assume precise knowledge of robot and/or environmental (i.e. terrain) properties. In uneven terrain, it is essential that the robot mobility over the terrain be explicitly considered in the planning process. Further, since significant uncertainty is often associated with robot and/or terrain parameter knowledge, this should also be accounted for in a path generation algorithm. Here, extensions to the rapidly exploring random tree (RRT) algorithm are presented that explicitly consider robot mobility and robot parameter uncertainty based on the stochastic response surface method (SRSM). Simulation results suggest that the proposed approach can be used for generating safe paths on uncertain, uneven terrain.

In this paper, a tetrahedral mobile robot with central axis for transformation to the flat vehicle is presented. The throwable robot with the function of going into narrow spaces when its in the flat-vehicle mode is explained in detail. A prototype has been developed to illustrate the concept. Motion experiments confirm the novel properties of this mechanism: mode changing function and omnidirectional motion. Basic Motion experiments, with a test vehicle are also presented.

In this paper, the basic running performance of the cylindrical tracked vehicle with sideways mobility is presented. The crawler mechanism is of circular cross-section and has active rolling axes at the center of the circles. Conventional crawler mechanisms can support massive loads, but cannot produce sideways motion. Additionally, previous crawler edges sink undesirably on soft ground, particularly when the vehicle body is subject to a sideways tilt. The proposed design solves these drawbacks by adopting a circular cross-section crawler. A prototype. Basic motion experiments with confirm the novel properties of this mechanism: sideways motion and robustness against edge-sink.

This paper describes the design of unified active safety framework that combines trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles into a single constrained-optimal-control-based system. This framework allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. The vehicle navigation problem is formulated as a constrained optimal control problem with constraints bounding a navigable region of the road surface. A model predictive controller iteratively plans the best-case vehicle trajectory through this constrained corridor. The framework then uses this trajectory to assess the threat posed to the vehicle and intervenes in proportion to this threat. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. Simulated results are presented here to demonstrate the framework's ability to incorporate multiple threat thresholds and configurable intervention laws while sharing control with a human driver.

An omnidirectional mobile robot is able, kinematically, to move in any planar direction regardless of current pose. To date, nearly all designs and analyses of omnidirectional robots have considered the case of motion on flat, smooth terrain. This paper presents design constraints and guidelines for designing an omnidirectional mobile robot propelled by active split offset casters to be driven in rough terrain. Geometric constraints on wheel and linkage sizes are presented. The effects of terrain roughness and loss of wheel contact are analyzed. The vehicle design is optimized for kinematic isotropy, ability to maintain ground contact, traversable obstacle height, and maximum traversable distance over four diverse terrain types. The results are contrasted with a baseline design.

This paper proposes a method for near-optimal navigation of high speed mobile robots on uneven terrain. The method relies on a layered control strategy. A high-level planning layer generates an optimal desired trajectory through uneven terrain. A low-level navigation layer guides a robot along the desired trajectory via a potential field-based control algorithm. The high-level planner is guaranteed to yield optimal trajectories but is computationally intensive. The low-level navigation layer is sub-optimal but computationally efficient. To guard against failures at the navigation layer, a model-based lookahead approach is employed that utilizes a reduced form of the optimal trajectory generation algorithm. Simulation results show that the proposed method can successfully navigate a mobile robot over uneven terrain while avoiding hazards. A comparison of the method’s performance to a similar algorithm is also presented.

The ability of autonomous or semi-autonomous mobile robots to rapidly and accurately predict their mobility characteristics is an important requirement for their use in unstructured environments. Most methods for mobility prediction, however, assume precise knowledge of environmental (i.e. terrain) properties. In practical conditions, significant uncertainty is associated with terrain parameter estimation from robotic sensors, and this uncertainty must be considered in a mobility prediction algorithm. Here a method for efficient mobility prediction based on the stochastic response surface approach is presented that explicitly considers terrain parameter uncertainty. The method is compared to a Monte Carlo-based method and simulations show that the stochastic response surface approach can be used for efficient, accurate prediction of mobile robot mobility.

Path tracking control on non-flat terrain is an important capability of mobile robots operating in outdoor environments. A path tracking controller based on the model predictive control (MPC) framework is presented that explicitly considers terrain geometry and actuator limitations. The controller performance is studied with three vehicle dynamic models in a high-fidelity ADAMS simulation. The effect of model order on path tracking performance on flat terrain and sloped terrain is evaluated. It is shown that improved performance can be obtained by explicitly considering terrain effects.

In this paper, a novel crawler mechanism for sideways motion is presented. The crawler mechanism is of circular cross-section and has active rolling axes at the center of the circles. Conventional crawler mechanisms can support massive loads, but cannot produce sideways motion. Additionally, previous crawler edges sink undesirably on soft ground, particularly when the vehicle body is subject to a sideways tilt. The proposed design solves these drawbacks by adopting a circular cross-section crawler. A prototype has been developed to illustrate the concept. Motion experiments confirm the novel properties of this mechanism: sideways motion and robustness against edge-sink. Motion experiments, with a test vehicle are also presented.

This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, inertial measurement unit, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.

This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, IMU, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.

This paper introduces a signal-recognition based approach for detecting autonomous mobile robot immobilization on outdoor terrain. The technique utilizes a support vector machine classifier to form class boundaries in a feature space composed of statistics related to inertial and (optional) wheel speed measurements. The proposed algorithm is validated using experimental data collected with an autonomous robot operating in an outdoor environment. Additionally, two detector fusion techniques are proposed to combine the outputs of multiple immobilization detectors. One technique is proposed to minimize false immobilization detections. A second technique is proposed to increase overall detection accuracy while maintaining rapid detector response. The two fusion techniques are demonstrated experimentally using the detection algorithm proposed in this work and a dynamic model-based algorithm. It is shown that the proposed techniques can be used to rapidly and robustly detect mobile robot immobilization in outdoor environments, even in the absence of absolute position information.

An omnidirectional mobile robot is able, kinematically, to move in any direction regardless of current pose. To date, nearly all designs and analyses of omnidirectional robots have considered the case of motion on flat, smooth terrain. This paper presents a kinematic analysis and control method for an omnidirectional mobile robot driven by active split offset casters operating on rough, uneven terrain. Robot average isotropy is analyzed as a function of wheel module geometry on both flat and rough terrain. A simple kinematic control scheme that considers the effects of terrain unevenness is presented. The performance of the algorithm in rough terrain is studied in simulation. It is shown that with knowledge of terrain conditions, near- omnidirectional mobility is achievable in rough terrain.

Mobile robots and passenger vehicles are frequently required to operate at high speeds, on terrain that is sloped or uneven. These systems can be susceptible to rollover, particularly during severe maneuvers. This paper presents an analysis of rollover stability measurement for mobile robots operating at high speeds. The analysis examines the accuracy of a commonly accepted rollover stability metric during operation on sloped and rough terrain. The effects of sensor placement, center-of-gravity position estimation error, and wheel dynamics are examined. It is shown that these effects can have a significant impact on stability measurement during high speed operation

A mobile in vivo camera robot was developed to provide the ability for a single port biopsy procedure. Such a robot can be inserted into the abdominal cavity through a standard trocar. The surgeon controls the robot using visual feedback from the on-board camera. Measurements were made to identify the forces required to successfully biopsy in vivo tissue, including clamping and tearing forces. The robot design was developed around these parameters and the need to traverse the abdominal environment using specially designed wheels. This mobility allows the biopsy robot to move to the area of interest to sample specific tissues. The lead-screw linkage system that actuated the graspers allows for large force production through careful mechanical design. In vivo testing of this system in a porcine (pig) model has been successful. The robot is capable of traversing the entire in vivo abdominal environment and has successfully been used to biopsy hepatic tissue. In addition, experimental analysis of the biopsy mechanism shows good results towards more elaborate tissue manipulation in the future

Laparoscopy is abdominal surgery performed with long tools inserted through small incisions. The use of small incisions reduces patient trauma, but also eliminates the surgeon's ability to view and touch the surgical environment directly. These limitations generally restrict the application of laparoscopy to procedures less complex than those performed during open surgery. This paper presents a theoretical and experimental analysis of miniature, wheeled, in vivo robots to support laparoscopy. The objective is to develop a wireless mobile imaging robot that can be placed inside the abdominal cavity during surgery. Such robots will allow the surgeon to view the surgical environment from multiple angles. The motion of these in vivo robots will not be constrained by the insertion incisions. Simulation and experimental analyses have led to a wheel design that can attain good mobility performance in in vivo conditions

Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover’s safety on slopes and uneven surfaces. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. Vibrations are measured using an accelerometer on the rover structure. The classifier is trained using labeled vibration data during an off-line learning phase. Linear discriminant analysis is used for on-line identification of terrain classes such as sand, gravel, or clay. This approach is experimentally validated on a laboratory testbed.

This paper proposes a potential field-based method for high speed navigation of unmanned ground vehicles (UGVs) on uneven terrain. A potential field is generated in the two-dimensional “trajectory space” of the UGV path curvature and longitudinal velocity. Dynamic constraints, terrain conditions, and navigation conditions can be expressed in this space. A maneuver is chosen within a set of performance bounds, based on the potential field gradient. In contrast to traditional potential field methods, the proposed method is subject to local maximum problems, rather than local minimum. It is shown that a simple randomization technique can be employed to address this problem. Simulation and experimental results show that the proposed method can successfully navigate a UGV between pre-defined waypoints at high speed, while avoiding unknown hazards. Further, vehicle velocity and curvature are controlled to avoid rollover and excessive side slip. The method is computationally efficient, and thus suitable for on-board real-time implementation.

Safe, autonomous mobility in rough terrain is an important requirement for planetary exploration rovers. Knowledge of local terrain properties is critical to ensure a rover's safety on slopes and uneven surfaces. Visual features are often used to classify terrain; however, vision can be sensitive to lighting variations and other effects. This paper presents a method to classify terrain based on vibrations induced in the rover structure by wheel-terrain interaction during driving. This sensing mode is robust to lighting variations. Vibrations are measured using an accelerometer mounted on the rover structure. The classifier is trained using labeled vibration data during an offline learning phase. Linear discriminant analysis is used for online identification of terrain classes, such as sand, gravel, or clay. This approach has been experimentally validated on a laboratory testbed and on a four-wheeled rover in outdoor conditions.

Mobile robots have important applications in high speed, rough-terrain scenarios. It would be desirable to construct accurate models of these systems. However, due to the system complexity, accurate modeling is difficult. In This work a high-speed rough-terrain robot model is presented. Experiments show that this model can accurately predict robot performance in simple, well-known terrain. However in unstructured, rough terrain, performance prediction is less accurate. A stochastic method for analyzing system performance in spite of model parameter uncertainty is presented. A method for studying model sensitivity to parameter uncertainty is also presented. It is shown that stochastic analysis can be used effectively for model-based analysis of real-world rough-terrain robotic systems.

Future planetary exploration missions will require wheeled mobile robots ("rovers") to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper, an online estimation method that identifies key terrain parameters using on-board robot sensors is presented. These parameters can be used for traversability prediction or in a traction control algorithm to improve robot mobility and to plan safe action plans for autonomous systems. Terrain parameters are also valuable indicators of planetary surface soil composition. The algorithm relies on a simplified form of classical terramechanics equations and uses a linear-least squares method to compute terrain parameters in real time. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for various soil types.

Future planetary exploration missions will require rovers to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper an on-line estimation method that identifies key terrain parameters using on-board rover sensors is presented. These parameters can be used for accurate traversability prediction or in a traction control algorithm. These parameters are also valuable indicators of planetary surface soil composition. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for loose sand.

In future planetary exploration missions, rovers will be required to autonomously traverse challenging environments. Much of the previous work in robot motion planning cannot be successfully applied to the rough-terrain planning problem. A model-based planning method is presented in this paper that is computationally efficient and takes into account uncertainty in the robot model, terrain model, range sensor data, and rover path following errors. It is based on rapid path planning through the visible terrain map with a simple graph-search algorithm, followed by a physics-based evaluation of the path with a rover model. Simulation results are presented which demonstrate the effectiveness of the method presented.

A practical method is proposed for estimating the inertial parameters of robot manipulators with substantial unmodeled joint friction and actuator dynamics. The manipulator is mounted on a six-axis force/torque sensor. Sensor measurements and joint velocities recorded during manipulator motion are used to identify the inertial parameters. The unmodeled joint friction and actuator dynamics do not degrade the estimation results, as in conventional methods. The estimation algorithm does not require difficult-to-measure acceleration measurements. Experimental results presented show that an accurate estimation of inertia parameters is attainable. Since the sensor is external to the manipulator, the same sensor can be used for parameter estimation for a number of different systems.