This paper presents analytical models to investigate the steering maneuvers of planetary exploration rovers on loose soil. The models are based on wheel-soil interaction mechanics, or terramechanics, with which the traction and disturbance forces of a wheel are evaluated for various slip conditions. These traction forces are decomposed into the longitudinal and lateral directions of the wheel. The latter component, termed the side force has a major influence in characterizing the steering maneuvers of the rover. In this paper, the wheel-soil mechanics models are developed with particular attention to the side force and the validity of the model is confirmed by using a single-wheel test bed. The motion profile of the entire rover is numerically evaluated by incorporating the wheel-soil models into an articulated multibody model that describes the motion dynamics of the vehicle's body and chassis. Steering maneuvers are investigated under different steering angles by using a four-wheel rover test bed on simulated lunar soil ͑regolith simulant͒. The experimental results are compared with the simulation results using the corresponding model parameters. The proposed wheel-and-vehicle model demonstrates better accuracy in predicting steering maneuvers as compared to the conventional kinematics-based model.

In this paper, two control approaches are presented for exploration rovers traversing sandy-sloped terrain. One of the proposed controls is a model-based feed-forward control using a characteristic diagram, called a Thrust-cornering characteristic diagram. It consists of various characteristic curves of wheel forces for varied wheel slip conditions. An appropriate steering maneuver for slope traversal can be found using the diagram with slope traversal criteria. The other control is a sensor-based feedback control. A key approach to this feedback control is to compensate for three types of slip, namely, the vehicle sideslip and longitudinal/lateral slips of a wheel. The feedback control calculates both steering and driving maneuvers that can compensate for these slips and also allow the rover to successfully traverse a sandy slope. The performances of these two control approaches are confirmed in slope traversal experiments using a four-wheeled rover test bed. The proposed controls are verified by quantitative evaluations of distance and orientation errors. Through the experiment, it was found that the two controls have advantages and disadvantages, and the possibility of merging the model-based control and the sensor-based control is discussed.

Abstract-In this paper, a path planning and its evaluation method is described with taking into account wheel slip dynamics of lunar/planetary exploration rovers. The surface of the planetary body is largely covered with powdery soil. On such loose soil, the wheel slippage which will make the rover get stuck must be concerned. Since the slippage dynamically depends on the posture/velocity of vehicle, soil characteristics, and wheel-soil interactions, it becomes difficult issues to incorporate the wheel slip dynamics as a criterion into path-planning algorithms. To tackle the slippage problem, the authors develop the path-planning algorithm and the path-evaluation method based on the following approach. First, a path on a rough terrain is generated with the terrain-based criteria function. Subsequently, the dynamics simulation of a rover is carried out in which the rover is controlled to follow the candidate path. Finally, the path is properly evaluated based on the slip motion profiles calculated by the simulation. Demonstrations for the proposed technique are addressed along with a discussion on characteristics of the candidate path and the slip motion profile of the rover.

Abstract-For a mobile robot it is critical to detect and compensate for slippage, especially when driving in rough terrain environments. Due to its highly unpredictable nature, drift largely affects the accuracy of localization and control systems, even leading, in extreme cases, to the danger of vehicle entrapment with consequent mission failure. This paper presents a novel method for lateral slip estimation based on visually observing the trace produced by the wheels of the robot, during traverse of soft, deformable terrain, as that expected for lunar and planetary rovers. The proposed algorithm uses a robust Hough transform enhanced by fuzzy reasoning to estimate the angle of inclination of the wheel trace with respect to the vehicle reference frame. Any deviation of the wheel trace from the planned path of the robot suggests occurrence of sideslip that can be detected, and more interestingly, measured. This allows one to estimate the actual heading angle of the robot, usually referred to as the slip angle. The details of the various steps of the visual algorithm are presented and the results of experimental tests performed in the field with an allterrain rover are shown, proving the method to be effective and robust.

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.

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.

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This paper introduces a novel method for slip angle estimation based on visually observing the traces produced by the wheels of a robot on soft, deformable terrain. The proposed algorithm uses a robust Hough transform enhanced by fuzzy reasoning to estimate the angle of inclination of the wheel trace with respect to the vehicle reference frame. Any deviation of the wheel track from the planned path of the robot suggests occurrence of sideslip that can be detected and, more interestingly, measured. In turn, the knowledge of the slip angle allows encoder readings affected by wheel slip to be adjusted and the accuracy of the position estimation system to be improved, based on an integrated longitudinal and lateral wheel-terrain slip model. The description of the visual algorithm and the odometry correction method is presented, and a comprehensive set of experimental results is included to validate this approach.