The seafloor is a complex environment and it is challenging to conduct detailed mapping, soil composition sampling, and habitat characterization missions in this benthic region. As a step toward overcoming these challenges, we present a quadruped robot capable of walking on the seafloor and maneuvering via midfluid swimming. SELQIE, the Seafloor Environment Legged Quadruped Intelligent Explorer, is capable of walking underwater at speeds up to$0.2 ~\mathrm{m} / \mathrm{s}$, swimming at over$0.16 ~\mathrm{m} / \mathrm{s}$, and transitioning between modes. We also introduce a path planning algorithm that can account for both swimming and walking gaits to efficiently navigate around or over obstacles, and demonstrate the robot executing such a multi-modal trajectory.

Heterogeneous robotic systems in the field often encounter bodies of water with unknown traversability properties. One approach to measuring depth, current, soil composition, etc. is via an in situ underwater sensor being dragged by cable attached to a maneuvering airborne multicopter - which entails a novel motion planning and control problem with mixed resistive media. In this work we propose a framework to plan trajectories for future characterization sensors and payloads moving through mixed (air-water) media while considering uncertainty in the depth of the underwater ground surface. The methodology is applied to example underactuated systems with suspended payloads of increasing levels of complexity, including a cable robot and 4- and 8-DOF multicopter systems. Simulation studies employing trajectory optimization indicate that under certain payload configurations and task constraints, there are maneuvers in which it is more efficient to drag the payloads through water than through air. The paper also includes preliminary experiments with a testbed cable robot platform.

In this paper we introduce a novel optimal trajectory planning method for robots in static and dynamic environments while accounting for the robot’s kinematic and dynamic constraints. The proposed method is called Danger Field Sampling Based Model Predictive optimization (DF-SBMPO). This new method includes the advantages of SBMPO such as dealing with non-linear constraints and non-invertible models and planning in the input space while improving its computational efficiency in local minima situations. By introducing a new cost function and combining the Danger Field method in the collision checking process, our method can efficiently find a near time-optimal and smooth trajectory to the goal. We verify the method in simulation using a car-like robot model in an environment containing both static and moving obstacles. By comparing it systematically to other sampling-based path planning algorithms and the Time-Scaling time-optimal trajectory planning method, the results show that our method generally produces collision-free, smoother, near time-optimal trajectories that satisfy constraints with less computation time and fewer samples.

Wheeled robots operating in the field are faced with mobility challenges such as drop-offs, slippery slopes, and cluttered environments. In these situations, it is desirable to have agile and highly mobile platforms. This paper explores the utilization of multiple rotors to enhance the mobility of ground vehicles. A strategy based on downward thrust generation is developed to increase traction and surmount otherwise non-traversable slippery inclines. Both simulation and experimental results are presented to show the efficacy of the proposed solution.

This paper presents a community of practice with the aim of potentiate students' skills for developing applications for mobile devises. The stages of this proposal are approaching, diagnostic, preparation, development, and monitoring. The results obtained in the monitoring stage demonstrate that the community of practice impacts positively for potentiating the knowledge, skills and abilities of its members. This allows members to increase the possibility to be hired in a labour market, which is in constant growth.

Most wheelchairs have low directional stability due to their wheel-drive system configurations. Hence, when a wheelchair experiences excessive slip, it will create an uncontrollable moment that changes its heading direction, endangering the safety of the wheelchair user. This paper presents an approach to longitudinal slip reduction (or traction control) for an Electric Powered Wheelchair that uses a variable reference model, which is a mass-damper system. The position of a joystick is mapped to force and torque values, which are the input to this model. The output is the desired trajectory, which is fed to a trajectory tracking controller. The key idea is that if slip occurs, the applied torque to the wheels is reduced by decreasing the desired acceleration, which is achieved by changing the parameters of the reference model. In this study, the mass of the reference model is changed. The proposed slip reduction approach is validated via experiments.

In real world motion planning tasks, autonomous vehicles can easily deviate away from their planned trajectories due to external disturbances, uncertain wheel/leg-terrain interaction, and other errors in the model used for planning. A possible solution to this problem consists in the continuous usage of replanning strategies. However, replanning is in general computationally intensive and its use should be minimized when possible. In this paper, a new methodology for robust trajectory tracking is proposed. The method generates, via sampling, correcting control inputs to drive the vehicle back to the desired trajectory. Due to the use of sampling, the methodology easily incorporates nonlinear planning models and integrates seamlessly with sampling-based motion planners. The paper presents simulation and preliminary experimental results showing the efficacy of the proposed approach and thus its potential application to motion planning tasks with real-time constraints.

Autonomous ground vehicles operating in the field are likely to be faced with several mobility challenges such as piles of rubble, water crossings, steep hills, mud, and stiff vegetation patches. These scenarios are particularly critical for smaller robots with torque and power limited actuators, which as experimentally shown in this work can easily fail to accomplish their tasks in these environments. This paper motivates and provides a methodology that integrates the robot, actuator and terrain models with an efficient motion planner to exploit the vehicle momentum as a way to successfully traverse these difficult terrains. In particular, experimental results showing the efficacy of the proposed methodology are presented for a vegetation patch and a steep hill. Finally, a discussion of the necessary perception work to fully automate the process is included.

Analysis of the power consumption of a robotic ground vehicle (RGV) is important for planning since it enables motion plans that do not violate the power limitations of the motors, energy efficient path planning, prediction of the ability to complete a task based upon the vehicle's current energy supply, and estimation of when the vehicle will need to refuel or recharge. Power modeling is particularly difficult for skid steered vehicles because of the complexities of properly taking into account the skidding that is used for vehicle turning. This paper begins with a 2-dimensional, second order differential equation of a skid steered, wheeled RGV and shows that the power model is terrain dependent and is a function of both the turning radius and linear velocity of the vehicle. This model was verified experimentally, and a comprehensive set of experiments was performed to describe the power consumption of a skid steered RGV on asphalt.

To increase autonomous ground vehicle (AGV) safety and efficiency on outdoor terrains the vehicle's control system should have settings for individual terrain surfaces. A first step in such a terrain-dependent control system is classification of the surface upon which the AGV is traversing. This paper considers vision-based terrain surface classification for the path directly in front of the vehicle (¿ 1 m). Most visionbased terrain classification has focused on terrain traversability and not on terrain surface classification. The few approaches to classifying traversable terrain surfaces, with the exception of the use of infrared cameras to classify mud, have relied on stand-alone cameras that are designed for daytime use and are not expected to perform well in the dark. In contrast, this research uses a laser stripe-based structured light sensor, which uses a laser in conjunction with a camera, and hence can work at night. Also, unlike most previous results, the classification here does not rely on color since color changes with illumination and weather, and certain terrains have multiple colors (e.g., sand may be red or white). Instead, it relies only on spatial relationships, specifically spatial frequency response and texture, which captures spatial relationships between different gray levels. Terrain surface classification using each of these features separately is conducted by using a probabilistic neural network. Experimental results based on classifying four outdoor terrains demonstrate the effectiveness of the proposed methods.

Ruts formed as a result of vehicle traversal on soft ground are used by expert off road drivers because they can improve vehicle safety on turns and slopes thanks to the extra lateral force they provide to the vehicle. In this paper we propose a rut detection and tracking algorithm for autonomous ground vehicles (AGVs) equipped with a laser range finder. The proposed algorithm utilizes an extended Kalman filter (EKF) to recursively estimate the parameters of the rut and the relative position and orientation of the vehicle with respect to the ruts. Simulation results show that the approach is promising for future implementation.