System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

Trajectory tracking and traction coordinating controller design for lunar rover based on dynamics and kinematics analysis

Trajectory tracking control is a necessary part for autonomous navigation of planetary rover and traction coordinating control can reduce the forces consumption during navigation. As a result, a trajectory tracking and traction coordinating controller for wheeled lunar rover with Rocker Bogie is proposed in the paper. Firstly, the longitudinal dynamics model and the kinematics model of six-wheeled rover are established. Secondly, the traction coordinating control algorithm is studied based on sliding mode theory with improved exponential approach law. Thirdly, based on kinematics analysis and traction system identification, the trajectory tracking controller is designed using optimal theory. Then, co-simulations between ADAMS and MATLAB/Simulink are carried out to validate the proposed algorithm, and the simulation results have confirmed the effectiveness of path tracking and traction mobility improving

3D Path planning using a fuzzy logic navigational map for Planetary Surface Rovers

This work proposes an innovative app navigation path-planning problem exploration rovers by including terrain characteristics. The objective is to enhance the typical 2D arithmetical cost function by adding 3D information computed from the laser-scanned terrain such as terrain height, slopes, shadows, orientation and terrain roughness. This paper describes the algorithm developed by UPM and GMV and the tests made at the GMV outdoor test facilities using the Moon-Hound rover. This rover is a 50 Kg rover including a Sick laser mounted on a pan&tilt unit for generation of 3D Digital Elevation Models (DEM’s). Experimental results have shown the effectiveness of the proposed approach

Fault-Tolerant Control Strategy for Steering Failures in Wheeled Planetary Rovers

Fault-tolerant control design of wheeled planetary rovers is described. This paper covers all steps of the design process, from modeling/simulation to experimentation. A simplified contact model is used with a multibody simulation model and tuned to fit the experimental data. The nominal mode controller is designed to be stable and has its parameters optimized to improve tracking performance and cope with physical boundaries and actuator saturations. This controller was implemented in the real rover and validated experimentally. An impact analysis defines the repertory of faults to be handled. Failures in steering joints are chosen as fault modes; they combined six fault modes and a total of 63 possible configurations of these faults. The fault-tolerant controller is designed as a two-step procedure to provide alternative steering and reuse the nominal controller in a way that resembles a crab-like driving mode. Three fault modes are injected (one, two, and three failed steering joints) in the real rover to evaluate the response of the nonreconfigured and reconfigured control systems in face of these faults. The experimental results justify our proposed fault-tolerant controller very satisfactorily. Additional concluding comments and an outlook summarize the lessons learned during the whole design process and foresee the next steps of the research

Telerobotic Excavator Designed to Compete in NASA's Lunabotics Mining Competition

The second annual NASA Lunabotics Mining competition is to be held in May 23-28, 2011. The goal of the competition is for teams of university level students to design, build, test and compete with a fully integrated lunar excavator on a simulated lunar surface. Our team, named Lunar Solutions I, will be representing Temple University's College of Engineering in the competition. The team's main goal was to build a robot which is able to compete with other teams, and ultimately win the competition. The main challenge of the competition was to build a wireless robot that can excavate and collect a minimum of 10 kilograms of the regolith material within 15 minutes. The robot must also be designed to operate in conditions similar to those found on the lunar surface. The design of the lunar excavator is constrained by a set of requirements determined by NASA and detailed in the competition's rulebook. The excavator must have the ability to communicate with the "main base" wirelessly, and over a Wi-Fi network. Human operators are located at a remote site approximately 60 meters away from the simulated lunar surface upon which the robot must excavate the lunar regolith surface. During the competition, the robot will operate in a separate area from the control room in an area referred to as the "Lunarena." From the control room, the operators will have to control the robot using visual feedback from cameras placed both within the arena and on the robot. Using this visual feedback the human operators control the robots movement using both keyboard and joystick commands. In order to place in the competition, a minimum of 10 kg of regolith material has to be excavated, collected, and dumped into a specific location. For that reason, the robot must be provided with an effective and powerful excavation system. Our excavator uses tracks for the drive system. After performing extensive research and trade studies, we concluded that tracks would be the most effective method for transporting the excavator. When designing the excavation system, we analyzed several design options from the previous year's competition. We decided to use a front loader to collect the material, rather than a conveyer belt system or auger. Many of the designs from last year's competition used a conveyer belt mechanism to mine regolith and dump it into a temporary storage bin place on the robot. Using the front end loader approach allowed us to combine the scooping system and storage unit, which meant that the excavation system required less space

Cooperative Path-Planning for Multi-Vehicle Systems

In this paper, we propose a collision avoidance algorithm for multi-vehicle systems, which is a common problem in many areas, including navigation and robotics. In dynamic environments, vehicles may become involved in potential collisions with each other, particularly when the vehicle density is high and the direction of travel is unrestricted. Cooperatively planning vehicle movement can effectively reduce and fairly distribute the detour inconvenience before subsequently returning vehicles to their intended paths. We present a novel method of cooperative path planning for multi-vehicle systems based on reinforcement learning to address this problem as a decision process. A dynamic system is described as a multi-dimensional space formed by vectors as states to represent all participating vehicles’ position and orientation, whilst considering the kinematic constraints of the vehicles. Actions are defined for the system to transit from one state to another. In order to select appropriate actions whilst satisfying the constraints of path smoothness, constant speed and complying with a minimum distance between vehicles, an approximate value function is iteratively developed to indicate the desirability of every state-action pair from the continuous state space and action space. The proposed scheme comprises two phases. The convergence of the value function takes place in the former learning phase, and it is then used as a path planning guideline in the subsequent action phase. This paper summarizes the concept and methodologies used to implement this online cooperative collision avoidance algorithm and presents results and analysis regarding how this cooperative scheme improves upon two baseline schemes where vehicles make movement decisions independently