Perception-and-Energy-aware Motion Planning for UAV using Learning-based Model under Heteroscedastic Uncertainty

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.Comment: 7 pages, 7 figures, 2 tables. Submitted article for presentation at the 2024 IEEE International Conference on Robotics and Automation (ICRA

Slope Traversal Experiments with Slip Compensation Control for Lunar/Planetary Exploration Rover

2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, May 19-23, 200

Vision-based Estimation of Slip Angle for Mobile Robots and Planetary Rovers

2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, May 19-23, 200

Slope traversal experiments with slip compensation control for lunar/planetary exploration rover

Abstract-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 fourwheeled 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

Vision-based estimation of slip angle for mobile robots and planetary rovers

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

Modeling of slip rate-dependent traversability for path planning of wheeled mobile robot in sandy terrain

A planetary exploration rover has been employed for scientific endeavors or as a precursor for upcoming manned missions. Predicting rover traversability from its wheel slip ensures safe and efficient autonomous operations of rovers on deformable planetary surfaces; path planning algorithms that reduce slips by considering wheel-soil interaction or terrain data can minimize the risk of the rover becoming immobilized. Understanding wheel-soil interaction in transient states is vital for developing a more precise slip ratio prediction model, while path planning in the past assumes that slips generated at the path is a series of slip ratio in steady state. In this paper, we focus on the transient slip, or slip rate the time derivative of slip ratio, to explicitly address it into the cost function of path planning algorithm. We elaborated a regression model that takes slip rate and traction force as inputs and outputs slip ratio, which is employed in the cost function to minimize the rover slip in path planning phase. Experiments using a single wheel testbed revealed that even with the same wheel traction force, the slip ratio varies with different slip rates; we confirmed that the smaller the absolute value of the slip rate, the larger the slip ratio for the same traction force. The statistical analysis of the regression model confirms that the model can estimate the slip ratio within an accuracy of 85% in average. The path planning simulation with the regression model confirmed a reduction of 58% slip experienced by the rover when driving through rough terrain environments. The dynamics simulation results insisted that the proposed method can reduce the slip rate in rough terrain environments

Video2_Modeling of slip rate-dependent traversability for path planning of wheeled mobile robot in sandy terrain.MP4

A planetary exploration rover has been employed for scientific endeavors or as a precursor for upcoming manned missions. Predicting rover traversability from its wheel slip ensures safe and efficient autonomous operations of rovers on deformable planetary surfaces; path planning algorithms that reduce slips by considering wheel-soil interaction or terrain data can minimize the risk of the rover becoming immobilized. Understanding wheel-soil interaction in transient states is vital for developing a more precise slip ratio prediction model, while path planning in the past assumes that slips generated at the path is a series of slip ratio in steady state. In this paper, we focus on the transient slip, or slip rate the time derivative of slip ratio, to explicitly address it into the cost function of path planning algorithm. We elaborated a regression model that takes slip rate and traction force as inputs and outputs slip ratio, which is employed in the cost function to minimize the rover slip in path planning phase. Experiments using a single wheel testbed revealed that even with the same wheel traction force, the slip ratio varies with different slip rates; we confirmed that the smaller the absolute value of the slip rate, the larger the slip ratio for the same traction force. The statistical analysis of the regression model confirms that the model can estimate the slip ratio within an accuracy of 85% in average. The path planning simulation with the regression model confirmed a reduction of 58% slip experienced by the rover when driving through rough terrain environments. The dynamics simulation results insisted that the proposed method can reduce the slip rate in rough terrain environments.</p

Predictable Mobility: A Statistical Approach for Planetary Surface Exploration Rovers in Uncertain Terrain

In this article, a statistical mobility prediction for planetary surface exploration rovers has been described. This method explicitly considers uncertainty of the terrain physical parameters via SRSM and employs models of both vehicle dynamics and wheel-terrain interaction mechanics. The simulation results of mobility prediction using three different techniques, SMC, LHSMC, and SRSM, confirms that SRSM significantly improves the computational efficiency compared with those conventional methods. The usefulness and validity of the proposed method has been confirmed through experimental studies of the slope traversal scenario in two different terrains. The results show that the predicted motion path with confidence ellipses can be used as a probabilistic reachability metric of the rover position. Also, for the slope-traversal case, terrain parameter uncertainty has a larger influence on the lateral motion of the rover than on longitudinal motion. Future directions of this study will apply the proposed technique to the path-planning problem. Here, confidence ellipses will be used to define collision-free areas, which will provide useful criteria for generating safe trajectories