Kinodynamic Motion Planning: A Novel Type Of Nonlinear, Passive Damping Forces And Advantages

T his article extends the capabilities of the harmonic potential field (HPF) approach to planning to cover both the kinematic and dynamic aspects of a robot's motion. The suggested approach converts the gradient guidance field from a harmonic potential to a control signal by augmenting it with a novel type of damping forces called nonlinear, anisotropic, damping forces (NADFs). The combination of the two provides a signal that can both guide a robot and effectively manage its dynamics. The kinodynamic planning signal inherits the guidance capabilities of the harmonic gradient field. It can also be easily configured to efficiently suppress the inertia-induced transients in the robot's trajectory without compromising the speed of operation. The approach works with dissipative systems as well as systems acted on by external forces without needing the full knowledge of the system's dynamics. Theoretical developments and simulation results are provided in this article. The HPF approach to planning is emerging as a powerful paradigm for the guidance of autonomous agents. Since it was suggested in the mid-late 1980s [1], [2], the approach is continuously being developed to meet the stringent requirements operation in a real-life environment imposes on an agent. Until now, the approach has amassed many attractive properties crucial for enhancing goal reachability. The approach is provably correct, driving the agent to a successful conclusion if the task is manageable and providing an indication if the task is intractable. It can be used to guide the motion of an arbitrarily shaped agent in an unknown environment regardless of its geometry or topology, relying only on the sensory data acquired online by the agent's finite-range sensors. The method can also impose a variety of constraints on the agent's trajectory such as regional avoidance and directional constraints [3]–[8]. Harmonic functions are also Morse functions and a general form of the navigation functions suggested in [13] (see ''Navigation Functions''). A planner may be defined as an intelligent, purposive, context sensitive controller that can instruct an agent on how to deploy its motion actuators (i.e., generate a control signal) so that a target state may be reached in a constrained manner. Traditionally, a planning task is distributed at two stages: a high-level control (HLC) and a low-level control (LLC). The HLC stage receives data about the environment, the target of the agent, and constraints on its behavior. It then simultaneously processes these data to generate a reference plan or …