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.

To meet the demand for high additive manufacturing efficiency, we are working on an FDM (Fused Deposition Modeling) printer called DEXTER which has dual Selective Compliance Articulated Robot Arms (SCARA). We develop a path planner to produce time-optimal motion paths for each of DEXTER's arms while guaranteeing that the arms do not collide with one another. We present a collision-free path planner for DEXTER's arms using an improved Sampling-Based Model Predictive Optimization (SBMPO) based on A* type optimization by adding efficient collision determination and a new type of cost function. The simulation results show that the improved SBMPO can be used to efficiently generate smooth collision-free paths and trajectories with bounded velocity and acceleration.

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.

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.

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.

Skid-steered vehicles are often used as outdoor mobile robots due to their robust mechanical structure and high maneuverability. Sliding, along with rolling, is inherent to general curvilinear motion, which makes both kinematic and dynamic modeling difficult. For the purpose of motion planning, this paper develops and experimentally verifies dynamic models of a skid-steered wheeled vehicle for general planar (2-D) motion and for linear 3-D motion. These models are characterized by the coefficient of rolling resistance, the coefficient of friction, and the shear deformation modulus, which have terrain-dependent values. The dynamic models also include motor saturation and motor power limitations, which enable correct prediction of vehicle velocities when traversing on hills. It is shown that the closed-loop system that results from inclusion of the dynamics of the [proportional--integral--derivative (PID)] speed controllers for each set of wheels does a much better job than the open-loop model of predicting the vehicle linear and angular velocities. For a vehicle turning with small linear and angular accelerations, the model provides accurate predictions of velocities and reasonable predictions of torques. Hence, the closed-loop model is recommended for motion planning.

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.

Skid-steered vehicles are often used as outdoor mobile robots due to their robust mechanical structure and high maneuverability. Sliding along with rolling is inherent to general curvilinear motion, which makes both kinematic and dynamic modeling difficult. For the purpose of motion planning this paper develops and experimentally verifies dynamic models of a skid-steered wheeled vehicle for general planar (2D) motion and for linear 3D motion. These models are characterized by the coefficient of rolling resistance, the coefficient of friction, and the shear deformation modulus, which have terrain-dependent values. The dynamic models also include motor saturation and motor power limitations, which enable correct prediction of vehicle velocities when traversing hills. It is shown that the closed-loop system that results from inclusion of the dynamics of the (PID) speed controllers for each set of wheels does a much better job than the open loop model of predicting the vehicle linear and angular velocities. Hence, the closed-loop model is recommended for motion planning.

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.

This paper proposes a control approach for an active robotic walking support system based on environment feedback. The support system is controlled using imposed apparent dynamics and its parameters are varied by the environment information. The environment information will not cause any motion but will only change the characteristics or maneuverability of the support system. This approach leads to a passive behavior for an active walking support system. In addition, the stability of the support system based on the apparent dynamics is also discussed. This is important as a guideline on what parameters to vary that will not cause instability to the system. Experimental results are presented to show the validity of the control algorithm with environment feedback.

This paper proposes a motion control algorithm for an active type of robotic mobility aid based on passive behavior concept. Passive behavior is an important characteristic of a system that provides mobility to elderly or person with walking disability. It allows the user to control the system based on intention. Passive behavior is implemented using imposed desired dynamics, which represents the actual behavior of passive system. This approach uses user intention represented by applied force/torque to derive the mobility aid desired motion. In addition, a guideline in parameter selection of the desired dynamics is also presented. This guideline is used to ensure the stability of the active robotic mobility aid with passive behavior. Experimental results are presented to show the validity of the proposed control method.

This paper proposes a new control approach for an active (motorized) robotic walking support system based on passive behavior concept. The control approach aims to enhance the interaction between the support system and the user. The passive behavior of a support system allows the user to safely interact with the system since it removes the system's capability to move when there is no user's intention. This passive behavior is realized using imposed apparent dynamics, which uses the user's intention represented by the applied force/torque to derive the system's desired motion. The control approach is extended into a user-oriented motion control algorithm to adapt user's controlling characteristics. This is implemented by varying the point of application of the apparent dynamics. The control approach is further extended to use environment information and realize environment feedback concept. This is implemented by varying the parameters of the apparent dynamics based on environment data. Experimental results are presented to show the validity of the proposed control approach.

This paper presents an approach in assisting a sit-to-stand movement using a robotic walking support system. In general, robotic walking support system is used to provide walking stability. We will extend its function to assist a user in executing a sit-to-stand movement. The first approach would be called passive support. In this approach, the support system would be stationary as the user execute the sit-to-stand movement. The knee torque would be determined and this would be compared to a sit-to-stand movement without support. The second approach would be called active support and the knee torque would be supported. A motion control algorithm would be proposed such that the support system will move based on the supporting torque. The actual experimentation on both approaches in assisting a sit-to-stand movement will be presented. The experimental result on active support shows the validity of the proposed motion control algorithm

This paper proposes a new control approach for a robotic walking support system to adapt a user's controlling characteristic. The control approach will be implemented by changing the kinematic structure of the robotic walking support system based on a variable center of rotation. This new control approach aims to help users who have difficulties in controlling their walking support system. In this study, we have a training stage to evaluate and adapt user's controlling characteristics. This will be implemented by allowing the user to follow some training paths. In the event a large path error occurs, a learning algorithm will vary the center of rotation of the support system until the user can successfully follow the training path. The relationship between the user intent in the form of applied force/torque and the new center of rotation will be taken by considering several training paths. This relationship will be used in actual control of the robotic walking support system. Experimentation and evaluation are presented to show the validity of the proposed control algorithm

This paper propose a new control algorithm for a robotic walker based on augmented variable center of rotation (COR) to adapt to user's controlling characteristics. The new CORs are taken from environment-based training. This is done by allowing the user to use the robotic walker from a start point to a goal point in a training environment and path following algorithm is employed. The user's intention represented by applied force/torque is logged during training. The logged data is segmented to correspond to the straight and curve segments in the training environment. This data is used to regenerate the path of the robotic walker. The error between the regenerated and the desired path is taken and in case a large path error exists, an optimization program is used to change the COR until the sum of the square of the path error is minimized. This approach refers to an offline COR determination. A variable COR controller as a function of applied torque is designed based on the new CORs. This is augmented and used in the actual control of the robotic walker. The proposed control algorithm was experimentally implemented and results show its validity.

This paper propose a new control algorithm for a robotic walking support system, which varies the kinematic structure of the system to adapt users' walking or controlling disability. Elderly who suffers from deficiencies in motor skills cannot control or steer their walkers properly. Since the applied torque is highly correlated to the heading angle of the walker, torque transformation is needed to properly steer the walker. The variation of kinematic structure would be implemented by varying the center of rotation (COR) and as a result the torque of the system is changed. In order to model the users' walking or controlling disability, the user would be asked to follow a desired path. Using a learning algorithm, errors between the desired and actual path would be used to determine the new COR. The proposed control algorithm was experimentally implemented and the result shows its validity.