A hybrid multi-objective evolutionary approach for optimal path planning of a hexapod robot

Hexapod robots are six-legged robotic systems, which have been widely investigated in the literature for various applications including exploration, rescue, and surveillance. Designing hexapod robots requires to carefully considering a number of different aspects. One of the aspects that require careful design attention is the planning of leg trajectories. In particular, given the high demand for fast motion and high-energy autonomy it is important to identify proper leg operation paths that can minimize energy consumption while maximizing the velocity of the movements. In this frame, this paper presents a preliminary study on the application of a hybrid multi-objective optimization approach for the computer-aided optimal design of a legged robot. To assess the methodology, a kinematic and dynamic model of a leg of a hexapod robot is proposed as referring to the main design parameters of a leg. Optimal criteria have been identified for minimizing the energy consumption and efficiency as well as maximizing the walking speed and the size of obstacles that a leg can overtake. We evaluate the performance of the hybrid multi-objective evolutionary approach to explore the design space and provide a designer with an optimal setting of the parameters. Our simulations demonstrate the effectiveness of the hybrid approach by obtaining improved Pareto sets of trade-off solutions as compared with a standard evolutionary algorithm. Computational costs show an acceptable increase for an off-line path planner. © Springer International Publishing Switzerland 2016

Dynamic Control of Robot Perception Using Stochastic Spatial Models

Robot perception has traditionally been addressed as a passive and incidental activity, rather than an active and task-directed activity. Consequently, although sensor systems are essential to provide the information required by the decision-making and actuation components of a robot system, no explicit planning and control of the sensory activities of the robot is performed. This has lead to the development of sensor modules that are either excessively specialized, or inefficient and unfocused in their informational output. In this paper, we develop strategies for the dynamic control of robot perception, using stochastic sensor and spatial models to explicitly plan and control the sensory activities of an autonomous mobile robot, and to dynamically servo the robot and its sensors to acquire the information necessary for successful execution of robot tasks. We discuss the explicit characterization of robot task-specific information requirements, the use of information-theoretic measures to model the extent, accuracy and complexity of the robot’s world model, and the representation of inferences about the robot’s environment using the Inference Grid, a multiproperty tesselated random field model. We describe the use of stochastic sensor models to determine the utility of sensory actions, and to compute the loci of observation of relevant information. These models allow the development of various perception control strategies, including attention control and focussing, perceptual responsiveness to varying spatial complexity, and control of multi-goal perceptual activities. We illustrate these methodologies using an autonomous multi-sensor mobile robot, and show the application of dynamic perception strategies to active exploration and multi-objective motion planning.