Our lab has developed new capabilities for snake robots that allow them to successfully navigate networks of pipes. Recent developments in the control and state estimation of snake robots have enabled these capabilities. The development of a gait-based compliant controller enables us to develop more complex motions while at the same time simplifying the controls for the operator. Additionally, new state estimation techniques that exploit the robot's redundant sensing allow accurate estimation of the robot's orientation and kinematic configuration, even when significant amounts of sensor feedback is missing or corrupted.
Biological snakes exhibit a natural ability to decouple locomotion from the perceptual task of aligning their heads in a particular direction. This same multi-tasking problem is nontrivial for snake robots. A snake robot can mimic the locomotion of their biological counterparts through use of analytic gait expressions, but to orient its head the robot must solve an inverse kinematics problem at every time step. In this work we use modal decomposition to modify a snake robot’s sidewinding gait to orient the head while locomoting. This avoids the problem of determining online inverse kinematic solutions which can be computationally costly. We use knowledge of the robot’s ground contact points to vary the number of joints used for head reorientation to minimally impact locomotion stability. We further show that the resulting expression can be used as a controller to improve real-time target tracking.
In this paper, we generate gaits for mixed systems, that is, dynamic systems that are subject to a set of nonholonomic
constraints. What is unique about mixed systems is that when we express their dynamics in body
coordinates, the motion of these systems can be attributed to two decoupled terms: the geometric and dynamic
phase shifts. In our prior work, we analyzed systems whose dynamic phase shift was null by definition. Purely
mechanical and principally kinematic systems are two classes of mechanical systems that have this property. We
generated gaits for these two classes of systems by intuitively evaluating their geometric phase shift and relating
it to a volume integral under well-defined height functions.
One of the contributions of this paper is to present a similar intuitive approach for computing the dynamic
phase shift. We achieve this, by introducing a new scaled momentum variable that not only simplifies the
momentum evolution equation but also allows us to introduce a new set of well-defined gamma functions which
enable us to intuitively evaluate the dynamic phase shift. More specifically, by analyzing these novel gamma
functions in a similar way to how we analyzed height functions, and by analyzing the sign-definiteness of the
scaled momentum variable, we are able to ensure that the dynamic phase shift is non-zero solely along the desired
fiber direction.
Finally, we also introduce a novel mechanical system, the variableinertia snakeboard, which is a generalization
of the original snakeboard that was previously studied in the literature. Not only does this general system help
us identify regions of the base space where we can not define a certain type of gaits, but also it helps us verify
the generality and applicability of our gait generation approach.
We introduce a new algorithm to cover an unknown space with a homogenous team of circular mobile robots. Our approach uses a previous single robot coverage algorithm that divides the target space into cells, each of which can be covered with simple back and forth motions. The advantage of our method is that we plan in a two dimensional configuration space for a team of n robots, bypassing the 2n dimensional configuration space. The approach used is semi-decentralized - robot teams cover the space independent of each other, but, robots within a team communicate state and share information. An implementation of the algorithm, in simulation, is also detailed.
One way to improve the efficiency of a mine search, compared with a complete coverage algorithm, is to direct the search based on the spatial distribution of the minefield. The key for the success of this probabilistic approach is to efficiently extract the spatial distribution of the minefield during the process of the search. In our research, we assume that a minefield follows a regular pattern, which belongs to a family of known patterns. Likelihood and Bayesian approaches to the pattern extraction algorithm are developed to extract the underlying pattern of the minefield. Both algorithms perform well in their ability to catch the "actual" pattern. And both algorithms are efficient, therefore, online implement of the algorithm on a mobile robot is possible. Compared to the likelihood approach, the advantage of using a Bayesian approach is that this approach provides information about the uncertainty of the extracted "actual" pattern.
A coverage path planning algorithm produces a path that directs a robot (or its detector range, occupied volume, etc.) to sweep out a target volume. The goal of this work is to direct an agent to cover (fill) an unknown space using different coverage patterns. We achieve coverage by dividing the target region into sub-regions, termed cells, such that coverage in each cell can be accomplished by basic maneuvers, such as simple back-and-forth motions. This approach to coverage employs a representation of the free space called an exact cellular decomposition, which already has been widely used for path planning between two points. In this paper, we define exact cellular decompositions where critical points of Morse functions indicate the locations of cell boundaries. Morse functions are those whose critical points are non-degenerate. Different Morse functions induce different coverage patterns. Between critical points, the structure of a Morse function is effectively the same, so simple control laws to achieve tasks, such as coverage, are feasible within each cell. This paper addresses the issue of how to cover a space with varying patterns, but it also suggests a common framework for many conventional path planners. A companion paper describes the sensor based implementation of this approach.
Serpentine robots offer advantages over traditional mobile robots and robot arms because they have enhanced flexibility and reachability; especially in convoluted environments. These mechanisms are especially well suited for search and rescue operations where making contact with surviving victims trapped in a collapsed building is essential. The same flexibility that makes serpentine robots incredibly useful also makes them difficult to design and control. This paper will describe the current status of serpentine robot design and path planning underway in our research group and point towards future directions of research.
Serpentine robots offer advances over traditional mobile robots and robot arms because they have enhanced flexibility and reachability, especially in convoluted environments. These mechanisms are especially well suited for search and rescue operations where making contact with surviving victims trapped in a collapsed building is essential. The same flexibility that makes serpentine robots incredibly useful also makes them difficult to design and control. This paper will describe the current status of serpentine robot design and path planning underway in our research group and point towards future directions of research.
Sensor based exploration is a task which enables a robot to explore and map an unknown environment, using sensor information. The map used in this paper is the generalized Voronoi graph (GVG). The robot explores an unknown environment using an already developed incremental construction procedure to generate the GVG using sensor information. This paper presents some initial results which uses the GVG for robot localization, while mitigating the need to update encoder values. Experimental result verify the described work.
This work prescribes the procedures that are required to implement, on a conventional mobile robot, a sensor based motion planning algorithm based on the generalized Voronoi graph (GVG). The GVG is a roadmap of a static environment; recall that a roadmap is a 1D representation of an environment which the robot can use to plan a path between any two points in that environment. Once the robot has constructed the roadmap, it has in essence explored the environment. This work describes some issues in incrementally constructing the GVG with a mobile robot wit a ring of sonar sensors. Specifically, we consider some issues in specularity and dead-reckoning error reduction.
A novel materials handling system is being developed at Carnegie Mellon University's Mechanical Engineering Department. This system contains an array of cells, each of which has two actuators. The two actuators are orthogonally oriented motorized roller wheels which, in combination, can generate a vector of motion in any planar direction. This work develops control laws for transporting and manipulating objects which rest on the array. Towards this goal, we consider the dynamics of parcel transport and manipulation. The parcel dynamics are based on an exact discrete representation of the system, unlike other methods where a continuity assumption is made. Two types of contact models are considered. This work extends the previous 1D discrete model into 2D.
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