Global navigation satellite systems (GNSS) denied environments/conditions require unmanned aerial vehicles (UAVs) to energy-efficiently and reliably fly. To this end, this study presents perception-and-energy-aware motion planning for UAVs in GNSS-denied environments. The proposed planner solves the trajectory planning problem by optimizing a cost function consisting of two indices: the total energy consumption of a UAV and the perception quality of light detection and ranging (LiDAR) sensor mounted on the UAV. Before online navigation, a high-fidelity simulator acquires a flight dataset to learn energy consumption for the UAV and heteroscedastic uncertainty associated with LiDAR measurements, both as functions of the horizontal velocity of the UAV. The learned models enable the online planner to estimate energy consumption and perception quality, reducing UAV battery usage and localization errors. Simulation experiments in a photorealistic environment confirm that the proposed planner can address the trade-off between energy efficiency and perception quality under heteroscedastic uncertainty. The open-source code is released at https://gitlab.com/ReI08/perception-energy-planner.

Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especially for heterogeneous terrains where the geological features vary from place to place, erroneous traversability prediction can become more apparent, increasing the risk of unrecoverable rover's wheel slip and immobilization. In this work, we propose a new path planning algorithm that explicitly accounts for such erroneous prediction. The key idea is the probabilistic fusion of distinctive ML models for terrain type classification and slip prediction into a single distribution. This gives us a multimodal slip distribution accounting for heterogeneous terrains and further allows statistical risk assessment to be applied to derive risk-aware traversing costs for path planning. Extensive simulation experiments have demonstrated that the proposed method is able to generate more feasible paths on heterogeneous terrains compared to existing methods.

This paper presents a framework for trajectory planning that explicitly considers robotic traversability based on a quasi-dynamic vehicle model of a mobile robot in loose soil. The quasi-dynamic model estimates the slip effect due to wheel-terrain interaction forces regardless of solving complicated multibody dynamics. Therefore, our proposed model is computationally efficient for quantifying how the robot safely traverses each trajectory segment generated by a planning algorithm. The trajectory planning in our framework exploits a sampling-based incremental search algorithm, i.e., Closed-Loop Rapidly-Exploring Random Trees (CL-RRT). In the tree extension process of the CL-RRT, the traversability assessment based on the quasi-dynamic vehicle model excludes the trajectory segment associated with a hazardous wheel slip ratio. As a result, a trajectory generated from the proposed framework is safely traversable for the robot even in high slip terrain. Simulation results show that the proposed vehicle model can run 57K times faster than the dynamic model and predict the robot motion 3 times more accurately than the kinematic model. Multiple trials of the trajectory planning simulation show that our proposed framework incorporated with the quasi-dynamic model reduces a wheel slip ratio by about 40 % as compared with the kinematic model.

There is a possibility of remaining indication of microorganisms on Martian subsurface and therefore, a robotic arm mounted on an exploration robot is required to dig to a certain depth and collect appropriate sample to be analyzed. However, the environment on the surface of Mars is harsh and most of all, limited power is available from the solar panel. In this paper, FEMI (Force-and-Energy Manipulability Index) is proposed to evaluate a feasible arm configuration for low energy consumption in its soil sampling operation. FEMI is calculated by the combination of an energy manipulability of an arm and an external force generated at an end effector of the arm. The FEMI derives the most feasible configuration on the joint angles of the arm for each sampling point. The usefulness of the FEMI is confirmed through a numerical simulation of a robotic arm. The simulation also presents that the FEMI can be used to obtain mechanical parameters of the arm such as link length and motor power, that are optimally-designed for a certain mission.

Difficult conditions on outdoor terrains make outdoor autonomy for rovers, a challenging task. The conventional wheel odometry method uses orientation measurements to assume a momentary plane to apply wheel encoder readings. On uneven terrains, this method often gives poor results for position tracking, and therefore rarely used. To improve the conventional odometry motion model, immediate terrain data can be used. This paper proposes a novel state variable extension (SVE) method to establish a connection between state space variables of a terrain rover by combining terrain point clouds with rover kinematics. The simulation results show that when the 2D state variables (x, y, yaw) are known, the 2D state can be extended to its 3D state (x, y, z, roll, pitch, yaw) with minimal error. The proposed SVE method is employed in a particle filter to determine the 2D state variables, which in turn results in achieving the full 3D position tracking of the rover.

This paper proposes a novel control strategy for autonomous underwater vehicles (AUVs), named as path tracking, which combines the conventional path following and trajectory tracking control in order to achieve smooth spatial convergence and tight temporal performance as well. This idea is inspired by the previous work of Hindman [1] and Encarnacao [2], however, the path tracking design herein goes from path following to trajectory tracking, which indeed is an inverse way from the previous solutions so that the complex projection algorithm resulting in a local stability is avoided. A kinematics controller is first derived by using Lyapunov direct method where a virtual path parameter is introduced to bring an extra control degree of freedom, and then it is extended to the dynamics of AUVs based on backstepping technique. The resulting nonlinear control design is formally shown and it yields global asymptotic convergence of the AUV to the path. Finally, simulation results illustrate the efficiency of the path tracking control design for AUVs.

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.

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.

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.

This paper presents slope traversal experiments with slip compensation control for lunar/planetary exploration rovers. On loose soil, wheels of the rover easily slip even when the rover travels with relatively low velocity. Because of the slip, following an arbitrary path on loose soil becomes a difficult task for the rover, and also, the slip will increase when the rover traverses a slope. To cope with the slip issue, the authors previously proposed path following control strategy taking wheel slippages into account. Through numerical simulations in the previous work, it has been confirmed that the proposed control effectively compensates and reduces the slip motions of the rover, and then, the rover can follow a given path. In order to confirm the usefulness of the proposed control for practical application, slope traversal experiments using a four-wheeled rover test bed are addressed in this paper. The control performance of the slip compensation is compared to that of no slip control based on motion traces of the rover in side slope traversal case. Further, the effectiveness of the proposed control is verified by quantitative evaluations of distance and orientation errors.

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 all-terrain rover are shown, proving the method to be effective and robust.

In this paper, a trafficability analysis for the exploration rover is described. A rover traveling on loose terrain often experiences its slippages (wheel slips or vehicle sideslip), and in particular, these slips become larger when the rover traverses on sandy slopes. The authors have investigated traction mechanics of a rigid wheel of the rover on loose terrain with paying attention to slipping behaviors of the wheel. In this paper, based on our previous works regarding the wheel-terrain mechanics, we propose a Thrust-Cornering Characteristic Diagram for trafficability analyses of the rover. The thrust-cornering characteristic diagram consists of various characteristics curves of wheel forces, namely thrust and cornering forces, with various wheel slippage conditions. This diagram provides quantitative criteria for slope traversing capability of the rover on arbitrary angles of slope. The usefulness of the proposed diagram for the trafficability analysis is confirmed through slope traversal experiments using a four-wheel test bed. Further, a steering maneuver control for slope traversing situation is discussed based on the diagram.

In this paper, we propose an action planning algorithm and its evaluation method based on dynamic simulation for a novel type of hybrid leg-wheel rover for planetary exploration. Hybrid leg-wheel robots are recently receiving a growing interest from the space community to explore planets, since they offer an appropriate solution to gain improved speed and mobility on unstructured terrain. However, in order to fully reach the hybrid mechanismpsilas potential, it is necessary to establish an optimal way to define when to use one over the other locomotion mode, depending on the soil conditions and topology. Even though this step is crucial, little attention has been devoted to this topic by the robotic community. The switching of motion mode, that is either wheel or leg are the actions to be planned, that we are considering in this paper. We aim at generating the safest and the least energy demanding path to reach a point of scientific interest. In order to define the optimal path with the set of switching actions required for the robot to follow it, the authors developed an action planning algorithm and a path evaluation method based on a four steps approach. First, an optimal candidate path on a rough terrain is generated based on topology and specificationspsila criteria functions. Then switching actions are defined along this path depending on the hybrid robotpsilas performances in each motion mode. The next step is a dynamic simulation of the robot controlled to follow the path. Finally, the path is evaluated based on the energy profile spent by the actuators and calculated by the simulation. Demonstrations for the proposed technique are addressed along with a discussion on characteristics of the candidate path and the energy profile of the robot.

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

In this paper, a path following control strategy for lunar/planetary exploration rovers is described, taking into account slip motion of the rover. It is determined that the slip motion of each wheel of the rover must be increased and cannot be neglected when the rover travels on loose soil. Because of slip, following an arbitrary path on loose soil is a difficult task. In order to improve this situation, the authors have developed a path following algorithm with slip compensation. In this algorithm, both steering and driving maneuvers of the rover are derived not only to follow an arbitrary path, but also simultaneously compensate for the slip. The performance of the path following strategy is confirmed through numerical simulation using the wheel-and-vehicle model elaborated in our previous research. The slip motion of the wheel is also addressed, based on a terramechanics approach. The proposed path following algorithm shows better performance than traditional control without slip compensation in the simulation

In this paper, steering characteristics of an exploration rover on loose soil is studied. Analysis of the steering characteristics is a key to plan and control the motion trajectory of a rover. Traditionally, such analysis has been made based on a model called "bicycle model." In that model, a four-wheel car-like vehicle is approximated by a two-wheel bicycle-like vehicle with the fore-wheels and the rear-wheels paired. However, the bicycle model does not show a good performance when a vehicle travels off-road. In order to analyze the steering characteristics of a vehicle on loose soil, the authors develop a model that respects the dynamics of each wheel's slip and skid behavior. The developed model is called all-wheel dynamics model. In the all-wheel dynamics model, the behavior of each wheel on loose soil is modeled based on terramechanics. The motion trajectory of the vehicle is obtained by numerical simulation using the wheel-and-vehicle dynamics model. The validity of the proposed model is examined by the experiments of a wheel and a vehicle using simulated lunar-surface soil. The experimental results show that the proposed model provides a better approximation than the traditional bicycle model.

In this paper, steering characteristics of a rigid wheel (tire) on loose soil is investigated. Based on terra-mechanic analysis, the lateral force characteristics of a driving wheel is modeled as a function of slip ratio and slip angle. The model suggests that the lateral force decreases according to the increment of the slip ratio and increases according to the increment of the slip angle. Such characteristics are confirmed and evaluated by experiments using simulated lunar-surface soil, called lunar regolith simulant. The proposed model is validated with the experimental results in reasonable precision. A model that properly predicts the lateral force can be useful for future practical issues, such as controlling the steering motion of a vehicle for following a desired trajectory in the operational phase, and also to compare the feasibility and/or stability of candidate steering maneuvers in the motion planning phase.