Path planning for reconfigurable rovers in planetary exploration

This paper introduces a path planning algorithm that takes into consideration different locomotion modes in a wheeled reconfigurable rover. Such algorithm, based on Fast Marching, calculates the optimal path in terms of power consumption between two positions, providing the most appropriate locomotion mode to be used at each position. Finally, the path planning algorithm is validated on a virtual Martian scene created within the V-REP simulation platform, where a virtual model of a planetary rover prototype is controlled by the same software that is used on the real one. Results of this contribution also demonstrate how the use of two locomotion modes, wheel-walking and normal-driving, can reduce the power consumption for a particular area.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Multi-scale path planning for a planetary exploration vehicle with multiple locomotion modes

Planetary exploration vehicles (rovers) can encounter with a great variety of situations. Most of them are related to the terrain, which can cause the end of the mission if these vehicles are not able to traverse it. It was the case of Spirit rover, which got stuck in loose sand, making it impossible to continue advancing. A solution to this is to make rovers capable of modifying their locomotion to traverse terrains with particular terramechanic parameters.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Path Planning for Reconfigurable Rovers in Planetary Exploration

This paper introduces a path planning algorithm that takes into consideration different locomotion modes in a wheeled reconfigurable rover. Power consumption and traction are estimated by means of simplified dynamics models for each locomotion mode. In particular, wheel-walking and normaldriving are modeled for a planetary rover prototype. These models are then used to define the cost function of a path planning algorithm based on fast marching. It calculates the optimal path, in terms of power consumption, between two positions, providing the most appropriate locomotion mode to be used at each position. Finally, the path planning algorithm was implemented in V-REP simulation software and a Martian area was used to validate it. Results of this contribution also demonstrate how the use of these locomotion modes would reduce the power consumption for a particular area.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Efficient Autonomous Navigation for Planetary Rovers with Limited Resources

Rovers operating on Mars are in need of more and more autonomous features to ful ll their challenging mission requirements. However, the inherent constraints of space systems make the implementation of complex algorithms an expensive and difficult task. In this paper we propose a control architecture for autonomous navigation. Efficient implementations of autonomous features are built on top of the current ExoMars navigation method, enhancing the safety and traversing capabilities of the rover. These features allow the rover to detect and avoid hazards and perform long traverses by following a roughly safe path planned by operators on ground. The control architecture implementing the proposed navigation mode has been tested during a field test campaign on a planetary analogue terrain. The experiments evaluated the proposed approach, autonomously completing two long traverses while avoiding hazards. The approach only relies on the optical Localization Cameras stereobench, a sensor that is found in all rovers launched so far, and potentially allows for computationally inexpensive long-range autonomous navigation in terrains of medium difficulty

Coupled path and motion planning for a rover-manipulator system

This paper introduces a motion planning strategy aimed at the coordination of a rover and manipulator. The main purpose is to fetch samples of scientific interest that could be placed on difficult locations, requiring to maximize the workspace of the combined system. In order to validate this strategy, a simulation environment has been built, based on the VORTEX Studio platform. A virtual model of the ExoTer rover prototype, owned by the European Space Agency, has been used together with the same robot control software. Finally, we show in this paper the benefits of validating the proposed strategy on simulation, prior to its future use on the real experimental rover.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Dynamic path planning for reconfigurable rovers using a multi-layered grid

Autonomy on rovers is desirable in order to extend the traversed distance, and therefore, maximize the number of places visited during the mission. It depends heavily on the information that is available for the traversed surface on other planet. This information may come from the vehicle’s sensors as well as from orbital images. Besides, future exploration missions may consider the use of reconfigurable rovers, which are able to execute multiple locomotion modes to better adapt to different terrains. With these considerations, a path planning algorithm based on the Fast Marching Method is proposed. Environment information coming from different sources is used on a grid formed by two layers. First, the Global Layer with a low resolution, but high extension is used to compute the overall path connecting the rover and the desired goal, using a cost function that takes advantage of the rover locomotion modes. Second, the Local Layer with higher resolution but limited distance is used where the path is dynamically repaired because of obstacle presence. Finally, we show simulation and field test results based on several reconfigurable and non-reconfigurable rover prototypes and a experimental terrain

Multi-stage warm started optimal motion planning for over-actuated mobile platforms

This work presents a computationally lightweight motion planner for over-actuated platforms. For this purpose, a general state-space model for mobile platforms with several kinematic chains is defined, which considers dynamics, nonlinearities and constraints. The proposed motion planner is based on a sequential multi-stage approach that takes advantage of the warm start on each step. Firstly, a globally optimal and smooth 2D/3D trajectory is generated using the Fast Marching Method. This trajectory is fed as a warm start to a sequential linear quadratic regulator that is able to generate an optimal motion plan without constraints for all the platform actuators. Finally, a feasible motion plan is generated considering the constraints defined in the model. In this respect, the sequential linear quadratic regulator is employed again, taking the previously generated unconstrained motion plan as a warm start. The motion planner has been deployed into the Exomars Testing Rover of the European Space Agency. This rover is an Ackermann-capable planetary exploration testbed that is equipped with a robotic arm. Several experiments were carried out demonstrating that the proposed approach speeds up the computation time and increases the success ratio for a martian sample retrieval mission, which can be considered as a representative use case of goal-constrained trajectory generation for an over-actuated mobile platform.This work has been partially funded by the EU-H2020 project entitled “Cooperative Robots for Extreme Environments” (CoRob-X) under grant agreement: 101004130. Funding for open access charge: Universidad de Málaga / CBUA”

Samples detection and retrieval for a sample fetch rover

Future planetary exploration missions are demanding more and more autonomy since these missions are getting more complex. A clear example is the Mars Sample Return mission, where the Sample Fetch Rover needs to collect sample tubes on a remote location, and bring them back to the base station to be launched to Earth. This mission requires to extend the autonomous capabilities onboard. First, the Navigation component needs to be able to detect and locate the sample tubes, and second, the Guidance and Control ones require to place the rover close the sample tubes and move the manipulator to pick them up. These are the main contributions of this paper. The first issue has been solved by the use of Deep Neural Networks, which allow to identify the previously trained sample tubes on images, and the second one has been solved by extending the path planning algorithm within the Guidance component. To demonstrate and validate the proposed methods, two experiments were carried out. A first field test in the Search and Rescue experimental terrain at the University of Malaga, and a second lab test in the Planetary Robotics Lab at the European Space Agency. Both experiments were carried out using the ExoMars Testing Rover owned by the last institution.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Concept, Development and Testing of Mars Rover Prototypes for ESA Planetary Exploration

This paper presents the system architecture and design of two planetary rover laboratory prototypes developed at the European Space Agency (ESA). These research platforms have been developed to provide early prototypes for validation of designs and serve ESA’s Automation & Robotics Lab infrastructure as testbeds for continuous research and testing. Both rovers have been built considering the constraints of Space Systems with the sufficient level of representativeness to allow rapid prototyping. They avoid strictly space-qualified components and designs that present a major cost burden and frequently lack the flexibility or modularity that the lab environment requires for its investigations. This design approach is followed for all the mechanical, electrical, and software aspects of the system. In this paper, two ExoMars mission-representative rovers, the ExoMars Testing Rover (ExoTeR) and the Martian Rover Testbed for Autonomy (MaRTA), are thoroughly described. The lessons learnt and experience gained while running several research activities and test campaigns are also presented. Finally, the paper aims to provide some insight on how to reduce the gap between lab R&D and flight implementation by anticipating system constraints when building and testing these platforms